fix issues

.gitignore

@@ -46,7 +46,7 @@ models-mnt
 /infill
 /libllama.so
 /llama-bench
-/llava-cli
+/llava
 /main
 /metal
 /perplexity

Makefile

@@ -1,7 +1,7 @@
 # Define the default target now so that it is always the first target
 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  \
+	simple batched batched-bench save-load-state server gguf llama-bench llava baby-llama beam-search  \
 	speculative infill benchmark-matmult parallel finetune export-lora tests/test-c.o
 
 # Binaries only useful for tests
@@ -617,10 +617,7 @@ convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggm
 llama-bench: examples/llama-bench/llama-bench.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-libllava.a: examples/llava/llava.cpp examples/llava/llava.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h common/base64.hpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual
-
-llava-cli: examples/llava/llava-cli.cpp examples/llava/clip.h examples/llava/clip.cpp examples/llava/llava.h examples/llava/llava.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llava: examples/llava/llava.cpp examples/llava/llava-utils.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
 
 baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)

common/CMakeLists.txt

@@ -41,7 +41,6 @@ endif()
 set(TARGET common)
 
 add_library(${TARGET} STATIC
-    base64.hpp
     common.h
     common.cpp
     sampling.h

common/base64.hpp

@@ -1,392 +0,0 @@
-/*
-This is free and unencumbered software released into the public domain.
-
-Anyone is free to copy, modify, publish, use, compile, sell, or
-distribute this software, either in source code form or as a compiled
-binary, for any purpose, commercial or non-commercial, and by any
-means.
-
-In jurisdictions that recognize copyright laws, the author or authors
-of this software dedicate any and all copyright interest in the
-software to the public domain. We make this dedication for the benefit
-of the public at large and to the detriment of our heirs and
-successors. We intend this dedication to be an overt act of
-relinquishment in perpetuity of all present and future rights to this
-software under copyright law.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
-IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
-OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
-ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
-OTHER DEALINGS IN THE SOFTWARE.
-
-For more information, please refer to <http://unlicense.org>
-*/
-
-#ifndef PUBLIC_DOMAIN_BASE64_HPP_
-#define PUBLIC_DOMAIN_BASE64_HPP_
-
-#include <cstdint>
-#include <iterator>
-#include <stdexcept>
-#include <string>
-
-class base64_error : public std::runtime_error
-{
-public:
-    using std::runtime_error::runtime_error;
-};
-
-class base64
-{
-public:
-    enum class alphabet
-    {
-        /** the alphabet is detected automatically */
-        auto_,
-        /** the standard base64 alphabet is used */
-        standard,
-        /** like `standard` except that the characters `+` and `/` are replaced by `-` and `_` respectively*/
-        url_filename_safe
-    };
-
-    enum class decoding_behavior
-    {
-        /** if the input is not padded, the remaining bits are ignored */
-        moderate,
-        /** if a padding character is encounter decoding is finished */
-        loose
-    };
-
-    /**
-     Encodes all the elements from `in_begin` to `in_end` to `out`.
-
-     @warning The source and destination cannot overlap. The destination must be able to hold at least
-     `required_encode_size(std::distance(in_begin, in_end))`, otherwise the behavior depends on the output iterator.
-
-     @tparam Input_iterator the source; the returned elements are cast to `std::uint8_t` and should not be greater than
-     8 bits
-     @tparam Output_iterator the destination; the elements written to it are from the type `char`
-     @param in_begin the beginning of the source
-     @param in_end the ending of the source
-     @param out the destination iterator
-     @param alphabet which alphabet should be used
-     @returns the iterator to the next element past the last element copied
-     @throws see `Input_iterator` and `Output_iterator`
-    */
-    template<typename Input_iterator, typename Output_iterator>
-    static Output_iterator encode(Input_iterator in_begin, Input_iterator in_end, Output_iterator out,
-                                  alphabet alphabet = alphabet::standard)
-    {
-        constexpr auto pad = '=';
-        const char* alpha  = alphabet == alphabet::url_filename_safe
-                                ? "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_"
-                                : "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";
-
-        while (in_begin != in_end) {
-            std::uint8_t i0 = 0, i1 = 0, i2 = 0;
-
-            // first character
-            i0 = static_cast<std::uint8_t>(*in_begin);
-            ++in_begin;
-
-            *out = alpha[i0 >> 2 & 0x3f];
-            ++out;
-
-            // part of first character and second
-            if (in_begin != in_end) {
-                i1 = static_cast<std::uint8_t>(*in_begin);
-                ++in_begin;
-
-                *out = alpha[((i0 & 0x3) << 4) | (i1 >> 4 & 0x0f)];
-                ++out;
-            } else {
-                *out = alpha[(i0 & 0x3) << 4];
-                ++out;
-
-                // last padding
-                *out = pad;
-                ++out;
-
-                // last padding
-                *out = pad;
-                ++out;
-
-                break;
-            }
-
-            // part of second character and third
-            if (in_begin != in_end) {
-                i2 = static_cast<std::uint8_t>(*in_begin);
-                ++in_begin;
-
-                *out = alpha[((i1 & 0xf) << 2) | (i2 >> 6 & 0x03)];
-                ++out;
-            } else {
-                *out = alpha[(i1 & 0xf) << 2];
-                ++out;
-
-                // last padding
-                *out = pad;
-                ++out;
-
-                break;
-            }
-
-            // rest of third
-            *out = alpha[i2 & 0x3f];
-            ++out;
-        }
-
-        return out;
-    }
-    /**
-     Encodes a string.
-
-     @param str the string that should be encoded
-     @param alphabet which alphabet should be used
-     @returns the encoded base64 string
-     @throws see base64::encode()
-    */
-    static std::string encode(const std::string& str, alphabet alphabet = alphabet::standard)
-    {
-        std::string result;
-
-        result.reserve(required_encode_size(str.length()) + 1);
-
-        encode(str.begin(), str.end(), std::back_inserter(result), alphabet);
-
-        return result;
-    }
-    /**
-     Encodes a char array.
-
-     @param buffer the char array
-     @param size the size of the array
-     @param alphabet which alphabet should be used
-     @returns the encoded string
-    */
-    static std::string encode(const char* buffer, std::size_t size, alphabet alphabet = alphabet::standard)
-    {
-        std::string result;
-
-        result.reserve(required_encode_size(size) + 1);
-
-        encode(buffer, buffer + size, std::back_inserter(result), alphabet);
-
-        return result;
-    }
-    /**
-     Decodes all the elements from `in_begin` to `in_end` to `out`. `in_begin` may point to the same location as `out`,
-     in other words: inplace decoding is possible.
-
-     @warning The destination must be able to hold at least `required_decode_size(std::distance(in_begin, in_end))`,
-     otherwise the behavior depends on the output iterator.
-
-     @tparam Input_iterator the source; the returned elements are cast to `char`
-     @tparam Output_iterator the destination; the elements written to it are from the type `std::uint8_t`
-     @param in_begin the beginning of the source
-     @param in_end the ending of the source
-     @param out the destination iterator
-     @param alphabet which alphabet should be used
-     @param behavior the behavior when an error was detected
-     @returns the iterator to the next element past the last element copied
-     @throws base64_error depending on the set behavior
-     @throws see `Input_iterator` and `Output_iterator`
-    */
-    template<typename Input_iterator, typename Output_iterator>
-    static Output_iterator decode(Input_iterator in_begin, Input_iterator in_end, Output_iterator out,
-                                  alphabet alphabet          = alphabet::auto_,
-                                  decoding_behavior behavior = decoding_behavior::moderate)
-    {
-        //constexpr auto pad = '=';
-        std::uint8_t last  = 0;
-        auto bits          = 0;
-
-        while (in_begin != in_end) {
-            auto c = *in_begin;
-            ++in_begin;
-
-            if (c == '=') {
-                break;
-            }
-
-            auto part = _base64_value(alphabet, c);
-
-            // enough bits for one byte
-            if (bits + 6 >= 8) {
-                *out = (last << (8 - bits)) | (part >> (bits - 2));
-                ++out;
-
-                bits -= 2;
-            } else {
-                bits += 6;
-            }
-
-            last = part;
-        }
-
-        // check padding
-        if (behavior != decoding_behavior::loose) {
-            while (in_begin != in_end) {
-                auto c = *in_begin;
-                ++in_begin;
-
-                if (c != '=') {
-                    throw base64_error("invalid base64 character.");
-                }
-            }
-        }
-
-        return out;
-    }
-    /**
-     Decodes a string.
-
-     @param str the base64 encoded string
-     @param alphabet which alphabet should be used
-     @param behavior the behavior when an error was detected
-     @returns the decoded string
-     @throws see base64::decode()
-    */
-    static std::string decode(const std::string& str, alphabet alphabet = alphabet::auto_,
-                              decoding_behavior behavior = decoding_behavior::moderate)
-    {
-        std::string result;
-
-        result.reserve(max_decode_size(str.length()));
-
-        decode(str.begin(), str.end(), std::back_inserter(result), alphabet, behavior);
-
-        return result;
-    }
-    /**
-     Decodes a string.
-
-     @param buffer the base64 encoded buffer
-     @param size the size of the buffer
-     @param alphabet which alphabet should be used
-     @param behavior the behavior when an error was detected
-     @returns the decoded string
-     @throws see base64::decode()
-    */
-    static std::string decode(const char* buffer, std::size_t size, alphabet alphabet = alphabet::auto_,
-                              decoding_behavior behavior = decoding_behavior::moderate)
-    {
-        std::string result;
-
-        result.reserve(max_decode_size(size));
-
-        decode(buffer, buffer + size, std::back_inserter(result), alphabet, behavior);
-
-        return result;
-    }
-    /**
-     Decodes a string inplace.
-
-     @param[in,out] str the base64 encoded string
-     @param alphabet which alphabet should be used
-     @param behavior the behavior when an error was detected
-     @throws base64::decode_inplace()
-    */
-    static void decode_inplace(std::string& str, alphabet alphabet = alphabet::auto_,
-                               decoding_behavior behavior = decoding_behavior::moderate)
-    {
-        str.resize(decode(str.begin(), str.end(), str.begin(), alphabet, behavior) - str.begin());
-    }
-    /**
-     Decodes a char array inplace.
-
-     @param[in,out] str the string array
-     @param size the length of the array
-     @param alphabet which alphabet should be used
-     @param behavior the behavior when an error was detected
-     @returns the pointer to the next element past the last element decoded
-     @throws base64::decode_inplace()
-    */
-    static char* decode_inplace(char* str, std::size_t size, alphabet alphabet = alphabet::auto_,
-                                decoding_behavior behavior = decoding_behavior::moderate)
-    {
-        return decode(str, str + size, str, alphabet, behavior);
-    }
-    /**
-     Returns the required decoding size for a given size. The value is calculated with the following formula:
-
-     $$
-     \lceil \frac{size}{4} \rceil \cdot 3
-     $$
-
-     @param size the size of the encoded input
-     @returns the size of the resulting decoded buffer; this the absolute maximum
-    */
-    static std::size_t max_decode_size(std::size_t size) noexcept
-    {
-        return (size / 4 + (size % 4 ? 1 : 0)) * 3;
-    }
-    /**
-     Returns the required encoding size for a given size. The value is calculated with the following formula:
-
-     $$
-     \lceil \frac{size}{3} \rceil \cdot 4
-     $$
-
-     @param size the size of the decoded input
-     @returns the size of the resulting encoded buffer
-    */
-    static std::size_t required_encode_size(std::size_t size) noexcept
-    {
-        return (size / 3 + (size % 3 ? 1 : 0)) * 4;
-    }
-
-private:
-    static std::uint8_t _base64_value(alphabet& alphabet, char c)
-    {
-        if (c >= 'A' && c <= 'Z') {
-            return c - 'A';
-        } else if (c >= 'a' && c <= 'z') {
-            return c - 'a' + 26;
-        } else if (c >= '0' && c <= '9') {
-            return c - '0' + 52;
-        }
-
-        // comes down to alphabet
-        if (alphabet == alphabet::standard) {
-            if (c == '+') {
-                return 62;
-            } else if (c == '/') {
-                return 63;
-            }
-        } else if (alphabet == alphabet::url_filename_safe) {
-            if (c == '-') {
-                return 62;
-            } else if (c == '_') {
-                return 63;
-            }
-        } // auto detect
-        else {
-            if (c == '+') {
-                alphabet = alphabet::standard;
-
-                return 62;
-            } else if (c == '/') {
-                alphabet = alphabet::standard;
-
-                return 63;
-            } else if (c == '-') {
-                alphabet = alphabet::url_filename_safe;
-
-                return 62;
-            } else if (c == '_') {
-                alphabet = alphabet::url_filename_safe;
-
-                return 63;
-            }
-        }
-
-        throw base64_error("invalid base64 character.");
-    }
-};
-
-#endif // !PUBLIC_DOMAIN_BASE64_HPP_

common/common.cpp

@@ -90,19 +90,6 @@ void process_escapes(std::string& input) {
                 case '\'': input[output_idx++] = '\''; break;
                 case '\"': input[output_idx++] = '\"'; break;
                 case '\\': input[output_idx++] = '\\'; break;
-                case 'x':
-                    // Handle \x12, etc
-                    if (input_idx + 2 < input_len) {
-                        const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 };
-                        char *err_p = nullptr;
-                        const long val = std::strtol(x, &err_p, 16);
-                        if (err_p == x + 2) {
-                            input_idx += 2;
-                            input[output_idx++] = char(val);
-                            break;
-                        }
-                    }
-                    // fall through
                 default:   input[output_idx++] = '\\';
                            input[output_idx++] = input[input_idx]; break;
             }

convert-baichuan-hf-to-gguf.py

@@ -16,7 +16,7 @@ import torch
 from sentencepiece import SentencePieceProcessor  # type: ignore[import]
 
 if 'NO_LOCAL_GGUF' not in os.environ:
-    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
 import gguf
 
 

convert-bloom-hf-to-gguf.py

@@ -0,0 +1,247 @@
+#!/usr/bin/env python3
+# HF bloom --> gguf conversion
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import re
+import struct
+import sys
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+from transformers import AutoTokenizer  # type: ignore[import]
+
+if 'NO_LOCAL_GGUF' not in os.environ:
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
+import gguf
+
+
+def count_model_parts(dir_model: Path) -> int:
+    num_parts = 0
+    for filename in os.listdir(dir_model):
+        if filename.startswith("pytorch_model-"):
+            num_parts += 1
+
+    if num_parts > 0:
+        print("gguf: found " + str(num_parts) + " model parts")
+    return num_parts
+
+
+# Supported Models:
+#   https://huggingface.co/bigscience/bloom-1b7
+#   https://huggingface.co/bigscience/bloom-3b
+#   https://huggingface.co/bigscience/bloom-7b1
+#   https://huggingface.co/Langboat/bloom-1b4-zh
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Convert a Bloom model to a GGML compatible file")
+    parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
+    parser.add_argument("--outfile",    type=Path,           help="path to write to; default: based on input")
+    parser.add_argument("model",        type=Path,           help="directory containing model file, or model file itself (*.bin)")
+    parser.add_argument("ftype",        type=int,            help="output format - use 0 for float32, 1 for float16", choices=[0, 1], default = 1)
+    return parser.parse_args()
+
+args = parse_args()
+
+dir_model = args.model
+ftype = args.ftype
+if not dir_model.is_dir():
+    print(f'Error: {args.model} is not a directory', file = sys.stderr)
+    sys.exit(1)
+
+# possible tensor data types
+#   ftype == 0 -> float32
+#   ftype == 1 -> float16
+
+# map from ftype to string
+ftype_str = ["f32", "f16"]
+
+if args.outfile is not None:
+    fname_out = args.outfile
+else:
+    # output in the same directory as the model by default
+    fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
+
+print("gguf: loading model "+dir_model.name)
+
+with open(dir_model / "config.json", "r", encoding="utf-8") as f:
+    hparams = json.load(f)
+
+if hparams["architectures"][0] != "BloomForCausalLM":
+    print("Model architecture not supported: " + hparams["architectures"][0])
+    sys.exit(1)
+
+# get number of model parts
+num_parts = count_model_parts(dir_model)
+
+ARCH=gguf.MODEL_ARCH.BLOOM
+gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
+
+print("gguf: get model metadata")
+
+block_count = hparams["n_layer"]
+
+gguf_writer.add_name("Bloom")
+n_embed = hparams.get("hidden_size", hparams.get("n_embed"))
+n_head = hparams.get("n_head", hparams.get("num_attention_heads"))
+gguf_writer.add_context_length(hparams.get("seq_length", n_embed))
+gguf_writer.add_embedding_length(n_embed)
+gguf_writer.add_feed_forward_length(4 * n_embed)
+gguf_writer.add_block_count(block_count)
+gguf_writer.add_head_count(n_head)
+gguf_writer.add_head_count_kv(n_head)
+gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"])
+gguf_writer.add_file_type(ftype)
+
+# TOKENIZATION
+
+print("gguf: get tokenizer metadata")
+
+tokens: list[bytearray] = []
+scores: list[float] = []
+toktypes: list[int] = []
+
+# gpt2 tokenizer
+gguf_writer.add_tokenizer_model("gpt2")
+
+print("gguf: get gpt2 tokenizer vocab")
+
+# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
+tokenizer = AutoTokenizer.from_pretrained(dir_model)
+
+# The number of tokens in tokenizer.json can differ from the expected vocab size.
+# This causes downstream issues with mismatched tensor sizes when running the inference
+vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
+assert max(tokenizer.vocab.values()) < vocab_size
+
+added_vocab = tokenizer.get_added_vocab()
+reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
+
+for i in range(vocab_size):
+    if i not in reverse_vocab:
+        tokens.append(f"[PAD{i}]")
+        toktypes.append(gguf.TokenType.USER_DEFINED)
+    elif reverse_vocab[i] in added_vocab:
+        tokens.append(reverse_vocab[i])
+        if tokenizer.added_tokens_decoder[i].special:
+            toktypes.append(gguf.TokenType.CONTROL)
+        else:
+            toktypes.append(gguf.TokenType.USER_DEFINED)
+    else:
+        tokens.append(reverse_vocab[i])
+        toktypes.append(gguf.TokenType.NORMAL)
+
+gguf_writer.add_token_list(tokens)
+gguf_writer.add_token_types(toktypes)
+
+special_vocab = gguf.SpecialVocab(dir_model, load_merges=True, n_vocab = len(tokens))
+special_vocab.add_to_gguf(gguf_writer)
+
+# TENSORS
+
+tensor_map = gguf.get_tensor_name_map(ARCH, block_count)
+
+# params for qkv transform
+n_head_kv = hparams.get("n_head_kv", n_head)
+head_dim = n_embed // n_head
+
+# tensor info
+print("gguf: get tensor metadata")
+
+if num_parts == 0:
+    part_names = iter(("pytorch_model.bin",))
+else:
+    part_names = (
+        f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
+    )
+
+for part_name in part_names:
+    if args.vocab_only:
+        break
+    print("gguf: loading model part '" + part_name + "'")
+    model_part = torch.load(dir_model / part_name, map_location="cpu")
+
+    has_lm_head = True
+    if "lm_head.weight" not in model_part.keys() and "output.weight" not in model_part.keys():
+        has_lm_head = False
+
+    for original_name in model_part.keys():
+        data = model_part[original_name]
+        name = re.sub(r'transformer\.', '', original_name)
+
+        old_dtype = data.dtype
+
+        # convert any unsupported data types to float32
+        if data.dtype != torch.float16 and data.dtype != torch.float32:
+            data = data.to(torch.float32)
+
+        data = data.squeeze().numpy()
+
+        if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name):
+            # Map bloom-style qkv_linear to gpt-style qkv_linear
+            # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252  # noqa
+            # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312  # noqa
+            qkv_weights = data.reshape((n_head, 3, n_embed // n_head, n_embed))
+            data = np.concatenate(
+                (qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
+                 qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
+                 qkv_weights[:, 2, :, :].reshape((-1, n_embed))),
+                axis=0
+            )
+            print("re-format attention.linear_qkv.weight")
+        elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name):
+            qkv_bias = data.reshape((n_head, 3, n_embed // n_head))
+            data = np.concatenate(
+                (qkv_bias[:, 0, :].reshape((n_embed,)),
+                 qkv_bias[:, 1, :].reshape((n_embed,)),
+                 qkv_bias[:, 2, :].reshape((n_embed,))),
+                axis=0
+            )
+            print("re-format attention.linear_qkv.bias")
+
+        # map tensor names
+        new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
+        if new_name is None:
+            print("Can not map tensor '" + name + "'")
+            sys.exit()
+
+        n_dims = len(data.shape)
+        data_dtype = data.dtype
+
+        # if f32 desired, convert any float16 to float32
+        if 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 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 ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+            data = data.astype(np.float16)
+
+        print(name, "=>", new_name + ", shape = " + str(data.shape) + ", " + str(old_dtype) + " --> " + str(data.dtype))
+
+        gguf_writer.add_tensor(new_name, data)
+
+        if not has_lm_head and name == "word_embeddings.weight":
+            gguf_writer.add_tensor("output.weight", data)
+            print(name, "=>", "output.weight" + ", shape = " + str(data.shape) + ", " + str(old_dtype) + " --> " + str(data.dtype))  # noqa
+
+
+print("gguf: write header")
+gguf_writer.write_header_to_file()
+print("gguf: write metadata")
+gguf_writer.write_kv_data_to_file()
+if not args.vocab_only:
+    print("gguf: write tensors")
+    gguf_writer.write_tensors_to_file()
+
+gguf_writer.close()
+
+print(f"gguf: model successfully exported to '{fname_out}'")
+print("")

convert-falcon-hf-to-gguf.py

@@ -0,0 +1,253 @@
+#!/usr/bin/env python3
+# HF falcon--> gguf conversion
+
+from __future__ import annotations
+
+import argparse
+import contextlib
+import json
+import os
+import struct
+import sys
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+from transformers import AutoTokenizer  # type: ignore[import]
+
+if 'NO_LOCAL_GGUF' not in os.environ:
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
+import gguf
+
+
+def count_model_parts(dir_model: Path, prefix: str) -> int:
+    num_parts = 0
+    for filename in os.listdir(dir_model):
+        if filename.startswith(prefix):
+            num_parts += 1
+
+    if num_parts > 0:
+        print("gguf: found " + str(num_parts) + " model parts")
+    return num_parts
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Convert a Falcon model to a GGML compatible file")
+    parser.add_argument(
+        "--vocab-only", action="store_true",
+        help="extract only the vocab",
+    )
+    parser.add_argument(
+        "--outfile", type=Path,
+        help="path to write to; default: based on input",
+    )
+    parser.add_argument(
+        "model", type=Path,
+        help="directory containing model file, or model file itself (*.bin)",
+    )
+    parser.add_argument(
+        "ftype", type=int, choices=[0, 1], default=1, nargs='?',
+        help="output format - use 0 for float32, 1 for float16",
+    )
+    return parser.parse_args()
+
+args = parse_args()
+
+dir_model = args.model
+ftype = args.ftype
+if not dir_model.is_dir():
+    print(f'Error: {args.model} is not a directory', file = sys.stderr)
+    sys.exit(1)
+
+# possible tensor data types
+#   ftype == 0 -> float32
+#   ftype == 1 -> float16
+
+# map from ftype to string
+ftype_str = ["f32", "f16"]
+
+if args.outfile is not None:
+    fname_out = args.outfile
+else:
+    # output in the same directory as the model by default
+    fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
+
+print("gguf: loading model "+dir_model.name)
+
+with open(dir_model / "config.json", "r", encoding="utf-8") as f:
+    hparams = json.load(f)
+
+if hparams["architectures"][0] not in ("RWForCausalLM", "FalconForCausalLM"):
+    print("Model architecture not supported: " + hparams["architectures"][0])
+
+    sys.exit(1)
+
+# get number of model parts
+num_parts = count_model_parts(dir_model, "model-00")
+if num_parts:
+    is_safetensors = True
+    from safetensors import safe_open
+else:
+    is_safetensors = False
+    num_parts = count_model_parts(dir_model, "pytorch_model-")
+
+ARCH=gguf.MODEL_ARCH.FALCON
+gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
+
+print("gguf: get model metadata")
+
+block_count = hparams.get("num_hidden_layers")
+if block_count is None:
+    block_count = hparams["n_layer"]  # old name
+
+n_head = hparams.get("num_attention_heads")
+if n_head is None:
+    n_head = hparams["n_head"]  # old name
+
+n_head_kv = hparams.get("num_kv_heads")
+if n_head_kv is None:
+    n_head_kv = hparams.get("n_head_kv", 1)  # old name
+
+gguf_writer.add_name("Falcon")
+gguf_writer.add_context_length(2048) # not in config.json
+gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform
+gguf_writer.add_embedding_length(hparams["hidden_size"])
+gguf_writer.add_feed_forward_length(4 * hparams["hidden_size"])
+gguf_writer.add_block_count(block_count)
+gguf_writer.add_head_count(n_head)
+gguf_writer.add_head_count_kv(n_head_kv)
+gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"])
+gguf_writer.add_file_type(ftype)
+
+# TOKENIZATION
+
+print("gguf: get tokenizer metadata")
+
+tokens: list[bytearray] = []
+scores: list[float] = []
+toktypes: list[int] = []
+
+# gpt2 tokenizer
+gguf_writer.add_tokenizer_model("gpt2")
+
+print("gguf: get gpt2 tokenizer vocab")
+
+# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
+tokenizer = AutoTokenizer.from_pretrained(dir_model)
+
+# The number of tokens in tokenizer.json can differ from the expected vocab size.
+# This causes downstream issues with mismatched tensor sizes when running the inference
+vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
+assert max(tokenizer.vocab.values()) < vocab_size
+
+reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
+
+for i in range(vocab_size):
+    tokens.append(reverse_vocab[i])
+    scores.append(0.0) # dummy
+    toktypes.append(gguf.TokenType.NORMAL)
+
+gguf_writer.add_token_list(tokens)
+gguf_writer.add_token_scores(scores)
+gguf_writer.add_token_types(toktypes)
+
+special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens))
+special_vocab.add_to_gguf(gguf_writer)
+
+# TENSORS
+
+tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
+
+head_dim = hparams["hidden_size"] // n_head
+
+# tensor info
+print("gguf: get tensor metadata")
+
+if num_parts == 0:
+    part_names = iter(("pytorch_model.bin",))
+elif is_safetensors:
+    part_names = (
+        f"model-{n:05}-of-{num_parts:05}.safetensors" for n in range(1, num_parts + 1)
+    )
+else:
+    part_names = (
+        f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
+    )
+
+for part_name in part_names:
+    if args.vocab_only:
+        break
+    print("gguf: loading model part '" + part_name + "'")
+    if is_safetensors:
+        ctx = safe_open(dir_model / part_name, framework="pt", device="cpu")
+    else:
+        ctx = contextlib.nullcontext(torch.load(dir_model / part_name, map_location="cpu"))
+
+    with ctx as model_part:
+        for name in model_part.keys():
+            data = model_part.get_tensor(name) if is_safetensors else model_part[name]
+
+            old_dtype = data.dtype
+
+            # convert any unsupported data types to float32
+            if data.dtype != torch.float16 and data.dtype != torch.float32:
+                data = data.to(torch.float32)
+
+            # QKV tensor transform
+            # The original query_key_value tensor contains n_head_kv "kv groups",
+            # each consisting of n_head/n_head_kv query weights followed by one key
+            # and one value weight (shared by all query heads in the kv group).
+            # This layout makes it a big pain to work with in GGML.
+            # So we rearrange them here,, so that we have n_head query weights
+            # followed by n_head_kv key weights followed by n_head_kv value weights,
+            # in contiguous fashion.
+            # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py
+
+            if "query_key_value" in name:
+                qkv = data.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head)
+                q = qkv[:, :-2 ].reshape(n_head * head_dim, head_dim * n_head)
+                k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head)
+                v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head)
+                data = torch.cat((q,k,v)).reshape_as(data)
+
+            data = data.squeeze().numpy()
+
+            # map tensor names
+            new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
+            if new_name is None:
+                print("Can not map tensor '" + name + "'")
+                sys.exit()
+
+            n_dims = len(data.shape)
+            data_dtype = data.dtype
+
+            # if f32 desired, convert any float16 to float32
+            if 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 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 ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+                data = data.astype(np.float16)
+
+            print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
+
+            gguf_writer.add_tensor(new_name, data)
+
+
+print("gguf: write header")
+gguf_writer.write_header_to_file()
+print("gguf: write metadata")
+gguf_writer.write_kv_data_to_file()
+if not args.vocab_only:
+    print("gguf: write tensors")
+    gguf_writer.write_tensors_to_file()
+
+gguf_writer.close()
+
+print(f"gguf: model successfully exported to '{fname_out}'")
+print("")

convert-gptneox-hf-to-gguf.py

@@ -0,0 +1,221 @@
+#!/usr/bin/env python3
+# HF gptneox--> gguf conversion
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import struct
+import sys
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+from transformers import AutoTokenizer  # type: ignore[import]
+
+if 'NO_LOCAL_GGUF' not in os.environ:
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
+import gguf
+
+
+def count_model_parts(dir_model: Path) -> int:
+    num_parts = 0
+    for filename in os.listdir(dir_model):
+        if filename.startswith("pytorch_model-"):
+            num_parts += 1
+
+    if num_parts > 0:
+        print("gguf: found " + str(num_parts) + " model parts")
+    return num_parts
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Convert a GPT-NeoX model to a GGML compatible file")
+    parser.add_argument(
+        "--vocab-only", action="store_true",
+        help="extract only the vocab",
+    )
+    parser.add_argument(
+        "--outfile", type=Path,
+        help="path to write to; default: based on input",
+    )
+    parser.add_argument(
+        "model", type=Path,
+        help="directory containing model file, or model file itself (*.bin)",
+    )
+    parser.add_argument(
+        "ftype", type=int, choices=[0, 1], default=1, nargs='?',
+        help="output format - use 0 for float32, 1 for float16",
+    )
+    return parser.parse_args()
+
+args = parse_args()
+
+dir_model = args.model
+ftype = args.ftype
+if not dir_model.is_dir():
+    print(f'Error: {args.model} is not a directory', file = sys.stderr)
+    sys.exit(1)
+
+# possible tensor data types
+#   ftype == 0 -> float32
+#   ftype == 1 -> float16
+
+# map from ftype to string
+ftype_str = ["f32", "f16"]
+
+if args.outfile is not None:
+    fname_out = args.outfile
+else:
+    # output in the same directory as the model by default
+    fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
+
+print("gguf: loading model "+dir_model.name)
+
+with open(dir_model / "config.json", "r", encoding="utf-8") as f:
+    hparams = json.load(f)
+
+if hparams["architectures"][0] != "GPTNeoXForCausalLM":
+    print("Model architecture not supported: " + hparams["architectures"][0])
+
+    sys.exit()
+
+# get number of model parts
+num_parts = count_model_parts(dir_model)
+
+ARCH=gguf.MODEL_ARCH.GPTNEOX
+gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
+
+print("gguf: get model metadata")
+
+block_count = hparams["num_hidden_layers"]
+
+gguf_writer.add_name(dir_model.name)
+gguf_writer.add_context_length(hparams["max_position_embeddings"])
+gguf_writer.add_embedding_length(hparams["hidden_size"])
+gguf_writer.add_block_count(block_count)
+gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
+gguf_writer.add_rope_dimension_count(int(hparams["rotary_pct"]*(hparams["hidden_size"]//hparams["num_attention_heads"])))
+gguf_writer.add_head_count(hparams["num_attention_heads"])
+gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
+gguf_writer.add_layer_norm_eps(hparams["layer_norm_eps"])
+
+# TOKENIZATION
+
+print("gguf: get tokenizer metadata")
+
+tokens: list[bytearray] = []
+scores: list[float] = []
+toktypes: list[int] = []
+
+# gpt2 tokenizer
+gguf_writer.add_tokenizer_model("gpt2")
+
+print("gguf: get gpt2 tokenizer vocab")
+
+# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
+tokenizer = AutoTokenizer.from_pretrained(dir_model)
+
+# The number of tokens in tokenizer.json can differ from the expected vocab size.
+# This causes downstream issues with mismatched tensor sizes when running the inference
+vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
+assert max(tokenizer.vocab.values()) < vocab_size
+
+added_vocab = tokenizer.get_added_vocab()
+reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
+
+for i in range(vocab_size):
+    if i not in reverse_vocab:
+        tokens.append(f"[PAD{i}]")
+        toktypes.append(gguf.TokenType.USER_DEFINED)
+    elif reverse_vocab[i] in added_vocab:
+        tokens.append(reverse_vocab[i])
+        if tokenizer.added_tokens_decoder[i].special:
+            toktypes.append(gguf.TokenType.CONTROL)
+        else:
+            toktypes.append(gguf.TokenType.USER_DEFINED)
+    else:
+        tokens.append(reverse_vocab[i])
+        toktypes.append(gguf.TokenType.NORMAL)
+
+gguf_writer.add_token_list(tokens)
+gguf_writer.add_token_types(toktypes)
+
+special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens))
+special_vocab.add_to_gguf(gguf_writer)
+
+# TENSORS
+
+tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
+
+# tensor info
+print("gguf: get tensor metadata")
+
+if num_parts == 0:
+    part_names = iter(("pytorch_model.bin",))
+else:
+    part_names = (
+        f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
+    )
+
+for part_name in part_names:
+    if args.vocab_only:
+        break
+    print("gguf: loading model part '" + part_name + "'")
+    model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
+
+    for name in model_part.keys():
+        data = model_part[name]
+
+        # we don't need these
+        if name.endswith(".attention.masked_bias") or name.endswith(".attention.bias") or name.endswith(".attention.rotary_emb.inv_freq"):
+            continue
+
+        old_dtype = data.dtype
+
+        # convert any unsupported data types to float32
+        if data.dtype != torch.float16 and data.dtype != torch.float32:
+            data = data.to(torch.float32)
+
+        data = data.squeeze().numpy()
+
+        # map tensor names
+        new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
+        if new_name is None:
+            print("Can not map tensor '" + name + "'")
+            sys.exit()
+
+        n_dims = len(data.shape)
+        data_dtype = data.dtype
+
+        # if f32 desired, convert any float16 to float32
+        if 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 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 ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+            data = data.astype(np.float16)
+
+        print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
+
+        gguf_writer.add_tensor(new_name, data)
+
+
+print("gguf: write header")
+gguf_writer.write_header_to_file()
+print("gguf: write metadata")
+gguf_writer.write_kv_data_to_file()
+if not args.vocab_only:
+    print("gguf: write tensors")
+    gguf_writer.write_tensors_to_file()
+
+gguf_writer.close()
+
+print(f"gguf: model successfully exported to '{fname_out}'")
+print("")

convert-hf-to-gguf.py

@@ -1,890 +0,0 @@
-#!/usr/bin/env python3
-
-from __future__ import annotations
-
-import argparse
-import contextlib
-import json
-import os
-import re
-import sys
-from enum import IntEnum
-from pathlib import Path
-from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast
-
-import numpy as np
-import torch
-
-if TYPE_CHECKING:
-    from torch import Tensor
-
-if 'NO_LOCAL_GGUF' not in os.environ:
-    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
-import gguf
-
-
-###### MODEL DEFINITIONS ######
-
-class SentencePieceTokenTypes(IntEnum):
-    NORMAL = 1
-    UNKNOWN = 2
-    CONTROL = 3
-    USER_DEFINED = 4
-    UNUSED = 5
-    BYTE = 6
-
-
-class Model:
-    def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool):
-        self.dir_model = dir_model
-        self.ftype = ftype
-        self.fname_out = fname_out
-        self.is_big_endian = is_big_endian
-        self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
-        self.is_safetensors = self._is_model_safetensors()
-        self.num_parts = Model.count_model_parts(self.dir_model, ".safetensors" if self.is_safetensors else ".bin")
-        self.part_names = self._get_part_names()
-        self.hparams = Model.load_hparams(self.dir_model)
-        self.model_arch = self._get_model_architecture()
-        self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess)
-
-    def set_vocab(self):
-        self._set_vocab_gpt2()
-
-    def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
-        for part_name in self.part_names:
-            print(f"gguf: loading model part '{part_name}'")
-            ctx: ContextManager[Any]
-            if self.is_safetensors:
-                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(self.dir_model / part_name, map_location="cpu"))
-
-            with ctx as model_part:
-                for name in model_part.keys():
-                    data = model_part.get_tensor(name) if self.is_safetensors else model_part[name]
-                    yield name, data
-
-    def set_gguf_parameters(self):
-        self.gguf_writer.add_name(self.dir_model.name)
-        self.gguf_writer.add_block_count(self.hparams.get(
-            "n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")),
-        ))
-        if (n_ctx := self.hparams.get("max_position_embeddings")) is not None:
-            self.gguf_writer.add_context_length(n_ctx)
-        if (n_embd := self.hparams.get("hidden_size")) is not None:
-            self.gguf_writer.add_embedding_length(n_embd)
-        if (n_ff := self.hparams.get("intermediate_size")) is not None:
-            self.gguf_writer.add_feed_forward_length(n_ff)
-        if (n_head := self.hparams.get("num_attention_head")) is not None:
-            self.gguf_writer.add_head_count(n_head)
-        self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
-
-    def write_tensors(self):
-        block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
-        for name, data_torch in self.get_tensors():
-            # we don't need these
-            if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.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)
-
-    def write(self):
-        self.write_tensors()
-        self.gguf_writer.write_header_to_file()
-        self.gguf_writer.write_kv_data_to_file()
-        self.gguf_writer.write_tensors_to_file()
-        self.gguf_writer.close()
-
-    def write_vocab(self):
-        self.gguf_writer.write_header_to_file()
-        self.gguf_writer.write_kv_data_to_file()
-        self.gguf_writer.close()
-
-    @staticmethod
-    def count_model_parts(dir_model: Path, prefix: str) -> int:
-        num_parts = 0
-        for filename in os.listdir(dir_model):
-            if filename.endswith(prefix):
-                num_parts += 1
-
-        return num_parts
-
-    @staticmethod
-    def load_hparams(dir_model):
-        with open(dir_model / "config.json", "r", encoding="utf-8") as f:
-            return json.load(f)
-
-    @staticmethod
-    def from_model_architecture(model_architecture):
-        if model_architecture == "StableLMEpochForCausalLM":
-            return StableLMModel
-        if model_architecture == "GPTNeoXForCausalLM":
-            return GPTNeoXModel
-        if model_architecture == "BloomForCausalLM":
-            return BloomModel
-        if model_architecture == "MPTForCausalLM":
-            return MPTModel
-        if model_architecture in ("BaichuanForCausalLM", "BaiChuanForCausalLM"):
-            return BaichuanModel
-        if model_architecture in ("FalconForCausalLM", "RWForCausalLM"):
-            return FalconModel
-        if model_architecture == "GPTBigCodeForCausalLM":
-            return StarCoderModel
-        if model_architecture == "GPTRefactForCausalLM":
-            return RefactModel
-        if model_architecture == "PersimmonForCausalLM":
-            return PersimmonModel
-        return Model
-
-    def _is_model_safetensors(self) -> bool:
-        return Model.count_model_parts(self.dir_model, ".safetensors") > 0
-
-    def _get_part_names(self):
-        if self.is_safetensors:
-            if self.num_parts == 1:  # there's only one .safetensors file
-                return ("model.safetensors",)
-            return (f"model-{n:05}-of-{self.num_parts:05}.safetensors" for n in range(1, self.num_parts + 1))
-
-        if self.num_parts == 1:  # there's only one .bin file
-            return ("pytorch_model.bin",)
-        return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1))
-
-    def _get_model_architecture(self) -> gguf.MODEL_ARCH:
-        arch = self.hparams["architectures"][0]
-        if arch == "GPTNeoXForCausalLM":
-            return gguf.MODEL_ARCH.GPTNEOX
-        if arch == "BloomForCausalLM":
-            return gguf.MODEL_ARCH.BLOOM
-        if arch == "MPTForCausalLM":
-            return gguf.MODEL_ARCH.MPT
-        if arch in ("BaichuanForCausalLM", "BaiChuanForCausalLM"):
-            return gguf.MODEL_ARCH.BAICHUAN
-        if arch == "FalconForCausalLM":
-            return gguf.MODEL_ARCH.FALCON
-        if arch == "GPTBigCodeForCausalLM":
-            return gguf.MODEL_ARCH.STARCODER
-        if arch == "GPTRefactForCausalLM":
-            return gguf.MODEL_ARCH.REFACT
-        if arch == "PersimmonForCausalLM":
-            return gguf.MODEL_ARCH.PERSIMMON
-
-        raise NotImplementedError(f'Architecture "{arch}" not supported!')
-
-    def _set_vocab_gpt2(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)
-        vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
-        assert max(tokenizer.vocab.values()) < vocab_size
-
-        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
-        added_vocab = tokenizer.get_added_vocab()
-
-        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])
-                if tokenizer.added_tokens_decoder[i].special:
-                    toktypes.append(gguf.TokenType.CONTROL)
-                else:
-                    toktypes.append(gguf.TokenType.USER_DEFINED)
-            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=True)
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def _set_vocab_sentencepiece(self):
-        from sentencepiece import SentencePieceProcessor
-
-        tokenizer_path = self.dir_model / 'tokenizer.model'
-
-        tokens: list[bytes] = []
-        scores: list[float] = []
-        toktypes: list[int] = []
-
-        if not tokenizer_path.is_file():
-            print(f'Error: Missing {tokenizer_path}', file=sys.stderr)
-            sys.exit(1)
-
-        tokenizer = SentencePieceProcessor(str(tokenizer_path))
-        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
-
-        for token_id in range(vocab_size):
-            piece = tokenizer.id_to_piece(token_id)
-            text = piece.encode("utf-8")
-            score = tokenizer.get_score(token_id)
-
-            toktype = SentencePieceTokenTypes.NORMAL
-            if tokenizer.is_unknown(token_id):
-                toktype = SentencePieceTokenTypes.UNKNOWN
-            elif tokenizer.is_control(token_id):
-                toktype = SentencePieceTokenTypes.CONTROL
-            elif tokenizer.is_unused(token_id):
-                toktype = SentencePieceTokenTypes.UNUSED
-            elif tokenizer.is_byte(token_id):
-                toktype = SentencePieceTokenTypes.BYTE
-
-            tokens.append(text)
-            scores.append(score)
-            toktypes.append(toktype)
-
-        added_tokens_file = self.dir_model / 'added_tokens.json'
-        if added_tokens_file.is_file():
-            with open(added_tokens_file, "r", encoding="utf-8") as f:
-                added_tokens_json = json.load(f)
-
-                for key in added_tokens_json:
-                    tokens.append(key.encode("utf-8"))
-                    scores.append(-1000.0)
-                    toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
-
-        self.gguf_writer.add_tokenizer_model("llama")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_scores(scores)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-
-class StableLMModel(Model):
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_rope_dimension_count(
-            int(self.hparams["rope_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])),
-        )
-        self.gguf_writer.add_layer_norm_eps(1e-5)
-
-
-class GPTNeoXModel(Model):
-    def set_gguf_parameters(self):
-        block_count = self.hparams["num_hidden_layers"]
-
-        self.gguf_writer.add_name(self.dir_model.name)
-        self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
-        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
-        self.gguf_writer.add_block_count(block_count)
-        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
-        self.gguf_writer.add_rope_dimension_count(
-            int(self.hparams["rotary_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])),
-        )
-        self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
-        self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
-        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
-
-
-class BloomModel(Model):
-    def set_gguf_parameters(self):
-        self.gguf_writer.add_name("Bloom")
-        n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
-        n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
-        self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
-        self.gguf_writer.add_embedding_length(n_embed)
-        self.gguf_writer.add_feed_forward_length(4 * n_embed)
-        self.gguf_writer.add_block_count(self.hparams["n_layer"])
-        self.gguf_writer.add_head_count(n_head)
-        self.gguf_writer.add_head_count_kv(n_head)
-        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
-        self.gguf_writer.add_file_type(self.ftype)
-
-    def write_tensors(self):
-        block_count = self.hparams["n_layer"]
-        tensors = dict(self.get_tensors())
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
-        has_lm_head = True
-        n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
-        n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
-
-        for name, data_torch in tensors.items():
-            if "lm_head.weight" not in tensors.keys() and "output.weight" not in tensors.keys():
-                has_lm_head = False
-
-            name = re.sub(r'transformer\.', '', name)
-
-            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()
-
-            if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name):
-                # Map bloom-style qkv_linear to gpt-style qkv_linear
-                # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252  # noqa
-                # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312  # noqa
-                qkv_weights = data.reshape((n_head, 3, n_embed // n_head, n_embed))
-                data = np.concatenate(
-                    (
-                        qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
-                        qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
-                        qkv_weights[:, 2, :, :].reshape((-1, n_embed)),
-                    ),
-                    axis=0,
-                )
-                print("re-format attention.linear_qkv.weight")
-            elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name):
-                qkv_bias = data.reshape((n_head, 3, n_embed // n_head))
-                data = np.concatenate(
-                    (
-                        qkv_bias[:, 0, :].reshape((n_embed,)),
-                        qkv_bias[:, 1, :].reshape((n_embed,)),
-                        qkv_bias[:, 2, :].reshape((n_embed,)),
-                    ),
-                    axis=0,
-                )
-                print("re-format attention.linear_qkv.bias")
-
-            # 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}, shape = {data.shape}, {old_dtype} --> {data.dtype}")
-
-            self.gguf_writer.add_tensor(new_name, data)
-
-            if not has_lm_head and name == "word_embeddings.weight":
-                self.gguf_writer.add_tensor("output.weight", data)
-                print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}")
-
-
-class MPTModel(Model):
-    def set_gguf_parameters(self):
-        block_count = self.hparams["n_layers"]
-        self.gguf_writer.add_name(self.dir_model.name)
-        self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
-        self.gguf_writer.add_embedding_length(self.hparams["d_model"])
-        self.gguf_writer.add_block_count(block_count)
-        self.gguf_writer.add_feed_forward_length(4 * self.hparams["d_model"])
-        self.gguf_writer.add_head_count(self.hparams["n_heads"])
-        if kv_n_heads := self.hparams["attn_config"].get("kv_n_heads"):
-            self.gguf_writer.add_head_count_kv(kv_n_heads)
-        self.gguf_writer.add_layer_norm_eps(1e-5)
-        if self.hparams["attn_config"]["clip_qkv"] is not None:
-            self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"])
-        self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"])
-
-    def write_tensors(self):
-        block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers"))
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
-        for name, data_torch in self.get_tensors():
-            # we don't need these
-            if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.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)
-
-            # note: MPT output is tied to (same as) wte in original model;
-            # for easier implementation in llama.cpp it's duplicated in GGUF, though :/
-            if new_name == "token_embd.weight":
-                self.gguf_writer.add_tensor("output.weight", data)
-
-
-class BaichuanModel(Model):
-    def set_vocab(self):
-        self._set_vocab_sentencepiece()
-
-    def set_gguf_parameters(self):
-        block_count = self.hparams["num_hidden_layers"]
-        head_count = self.hparams["num_attention_heads"]
-        head_count_kv = self.hparams.get("num_key_value_heads", head_count)
-        hf_repo = self.hparams.get("_name_or_path", "")
-
-        ctx_length = 0
-        if "max_sequence_length" in self.hparams:
-            ctx_length = self.hparams["max_sequence_length"]
-        elif "max_position_embeddings" in self.hparams:
-            ctx_length = self.hparams["max_position_embeddings"]
-        elif "model_max_length" in self.hparams:
-            ctx_length = self.hparams["model_max_length"]
-        else:
-            print("gguf: can not find ctx length parameter.")
-            sys.exit()
-
-        self.gguf_writer.add_name(self.dir_model.name)
-        self.gguf_writer.add_source_hf_repo(hf_repo)
-        self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
-        self.gguf_writer.add_context_length(ctx_length)
-        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
-        self.gguf_writer.add_block_count(block_count)
-        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
-        self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
-        self.gguf_writer.add_head_count(head_count)
-        self.gguf_writer.add_head_count_kv(head_count_kv)
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
-
-        if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
-            if self.hparams["rope_scaling"].get("type") == "linear":
-                self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
-                self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
-
-    def write_tensors(self):
-        # Collect tensors from generator object
-        model_kv = dict(self.get_tensors())
-        block_count = self.hparams["num_hidden_layers"]
-        head_count = self.hparams["num_attention_heads"]
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
-        head_count_kv = self.hparams.get("num_key_value_heads", head_count)
-
-        for i in range(block_count):
-            if (w := model_kv.get(f"model.layers.{i}.self_attn.W_pack.weight")) is not None:
-                print(f"Unpacking and permuting layer {i}")
-                model_kv[f"model.layers.{i}.self_attn.q_proj.weight"] = \
-                    self._reverse_hf_permute_part(w, 0, head_count, head_count)
-                model_kv[f"model.layers.{i}.self_attn.k_proj.weight"] = \
-                    self._reverse_hf_permute_part(w, 1, head_count, head_count_kv)
-                model_kv[f"model.layers.{i}.self_attn.v_proj.weight"] = \
-                    self._reverse_hf_part(w, 2)
-                del model_kv[f"model.layers.{i}.self_attn.W_pack.weight"]
-
-        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"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
-            self.gguf_writer.add_tensor(new_name, data)
-
-    def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
-        if n_kv_head is not None and n_head != n_kv_head:
-            n_head //= n_kv_head
-
-        return (
-            weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
-            .swapaxes(1, 2)
-            .reshape(weights.shape)
-        )
-
-    def _reverse_hf_permute_part(
-        self, weights: Tensor, n_part: int, n_head: int, n_head_kv: int | None = None,
-    ) -> Tensor:
-        r = weights.shape[0] // 3
-        return self._reverse_hf_permute(weights[r * n_part:r * n_part + r, ...], n_head, n_head_kv)
-
-    def _reverse_hf_part(self, weights: Tensor, n_part: int) -> Tensor:
-        r = weights.shape[0] // 3
-        return weights[r * n_part:r * n_part + r, ...]
-
-
-class FalconModel(Model):
-    def set_gguf_parameters(self):
-        block_count = self.hparams.get("num_hidden_layers")
-        if block_count is None:
-            block_count = self.hparams["n_layer"]  # old name
-
-        n_head = self.hparams.get("num_attention_heads")
-        if n_head is None:
-            n_head = self.hparams["n_head"]  # old name
-
-        n_head_kv = self.hparams.get("num_kv_heads")
-        if n_head_kv is None:
-            n_head_kv = self.hparams.get("n_head_kv", 1)  # old name
-
-        self.gguf_writer.add_name("Falcon")
-        self.gguf_writer.add_context_length(2048)  # not in config.json
-        self.gguf_writer.add_tensor_data_layout("jploski")  # qkv tensor transform
-        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
-        self.gguf_writer.add_feed_forward_length(4 * self.hparams["hidden_size"])
-        self.gguf_writer.add_block_count(block_count)
-        self.gguf_writer.add_head_count(n_head)
-        self.gguf_writer.add_head_count_kv(n_head_kv)
-        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
-        self.gguf_writer.add_file_type(self.ftype)
-
-    def write_tensors(self):
-        block_count = self.hparams.get("num_hidden_layers")
-        if block_count is None:
-            block_count = self.hparams["n_layer"]  # old name
-
-        n_head = self.hparams.get("num_attention_heads")
-        if n_head is None:
-            n_head = self.hparams["n_head"]  # old name
-
-        n_head_kv = self.hparams.get("num_kv_heads")
-        if n_head_kv is None:
-            n_head_kv = self.hparams.get("n_head_kv", 1)  # old name
-
-        head_dim = self.hparams["hidden_size"] // n_head
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
-
-        for name, data_torch in self.get_tensors():
-            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)
-
-            # QKV tensor transform
-            # The original query_key_value tensor contains n_head_kv "kv groups",
-            # each consisting of n_head/n_head_kv query weights followed by one key
-            # and one value weight (shared by all query heads in the kv group).
-            # This layout makes it a big pain to work with in GGML.
-            # So we rearrange them here,, so that we have n_head query weights
-            # followed by n_head_kv key weights followed by n_head_kv value weights,
-            # in contiguous fashion.
-            # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py
-
-            if "query_key_value" in name:
-                qkv = data_torch.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head)
-                q = qkv[:, :-2].reshape(n_head * head_dim, head_dim * n_head)
-                k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head)
-                v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head)
-                data_torch = torch.cat((q, k, v)).reshape_as(data_torch)
-
-            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)
-
-
-class StarCoderModel(Model):
-    def set_gguf_parameters(self):
-        block_count = self.hparams["n_layer"]
-
-        self.gguf_writer.add_name("StarCoder")
-        self.gguf_writer.add_context_length(self.hparams["n_positions"])
-        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
-        self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
-        self.gguf_writer.add_block_count(block_count)
-        self.gguf_writer.add_head_count(self.hparams["n_head"])
-        self.gguf_writer.add_head_count_kv(1)
-        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
-        self.gguf_writer.add_file_type(self.ftype)
-
-
-class RefactModel(Model):
-    def set_gguf_parameters(self):
-        hidden_dim = self.hparams["n_embd"]
-        inner_dim = 4 * hidden_dim
-        hidden_dim = int(2 * inner_dim / 3)
-        multiple_of = 256
-        ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
-
-        block_count = self.hparams["n_layer"]
-
-        self.gguf_writer.add_name("Refact")
-        # refact uses Alibi. So this is from config.json which might be used by training.
-        self.gguf_writer.add_context_length(self.hparams["n_positions"])
-        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
-
-        self.gguf_writer.add_feed_forward_length(ff_dim)
-        self.gguf_writer.add_block_count(block_count)
-        self.gguf_writer.add_head_count(self.hparams["n_head"])
-        self.gguf_writer.add_head_count_kv(1)
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
-        self.gguf_writer.add_file_type(self.ftype)
-
-    def write_tensors(self):
-        hidden_dim = self.hparams["n_embd"]
-        inner_dim = 4 * hidden_dim
-        hidden_dim = int(2 * inner_dim / 3)
-        multiple_of = 256
-        ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
-        n_head = self.hparams["n_head"]
-        n_head_kv = 1
-        head_dim = self.hparams["n_embd"] // n_head
-        block_count = self.hparams["n_layer"]
-
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
-
-        tensors = dict(self.get_tensors())
-        for i in range(block_count):
-            if (w := tensors.get(f"transformer.h.{i}.attn.kv.weight")) is not None:
-                tensors[f"model.layers.{i}.self_attn.k_proj.weight"] = w[:n_head_kv * head_dim]
-                tensors[f"model.layers.{i}.self_attn.v_proj.weight"] = w[n_head_kv * head_dim:]
-                del tensors[f"transformer.h.{i}.attn.kv.weight"]
-            if (w := tensors.get(f"transformer.h.{i}.attn.q.weight")) is not None:
-                tensors[f"model.layers.{i}.self_attn.q_proj.weight"] = w
-                del tensors[f"transformer.h.{i}.attn.q.weight"]
-            if (w := tensors.get(f"transformer.h.{i}.mlp.gate_up_proj.weight")) is not None:
-                tensors[f"model.layers.{i}.mlp.gate_proj.weight"] = w[:ff_dim]
-                tensors[f"model.layers.{i}.mlp.up_proj.weight"] = w[ff_dim:]
-                del tensors[f"transformer.h.{i}.mlp.gate_up_proj.weight"]
-
-        for name, data_torch in tensors.items():
-            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",))
-            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)
-
-
-class PersimmonModel(Model):
-    def set_gguf_parameters(self):
-        block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers"))
-        head_count = self.hparams["num_attention_heads"]
-        head_count_kv = head_count
-        hidden_size = self.hparams["hidden_size"]
-
-        self.gguf_writer.add_name('persimmon-8b-chat')
-        self.gguf_writer.add_embedding_length(hidden_size)
-        self.gguf_writer.add_block_count(block_count)
-        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
-        self.gguf_writer.add_rope_dimension_count(hidden_size // head_count)
-        self.gguf_writer.add_head_count(head_count)
-        self.gguf_writer.add_head_count_kv(head_count_kv)
-        self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"])
-        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
-
-    def set_vocab(self):
-        self._set_vocab_sentencepiece()
-        # self.gguf_writer.add_bos_token_id(71013)
-        # self.gguf_writer.add_eos_token_id(71013)
-
-    def write_tensors(self):
-        block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers"))
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
-
-        for name, data_torch in self.get_tensors():
-            if name.endswith(".self_attention.rotary_emb.inv_freq"):
-                continue
-            old_dtype = data_torch.dtype
-            # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?)
-            data = data_torch.to(torch.float32).squeeze().numpy()
-            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)
-            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
-            self.gguf_writer.add_tensor(new_name, data)
-
-
-###### CONVERSION LOGIC ######
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description="Convert a huggingface model to a GGML compatible file")
-    parser.add_argument(
-        "--vocab-only", action="store_true",
-        help="extract only the vocab",
-    )
-    parser.add_argument(
-        "--outfile", type=Path,
-        help="path to write to; default: based on input",
-    )
-    parser.add_argument(
-        "--outtype", type=str, choices=["f32", "f16"], default="f16",
-        help="output format - use f32 for float32, f16 for float16",
-    )
-    parser.add_argument("--bigendian", action="store_true", help="model is executed on big endian machine")
-    parser.add_argument(
-        "model", type=Path,
-        help="directory containing model file",
-    )
-
-    return parser.parse_args()
-
-
-args = parse_args()
-
-dir_model = args.model
-if not dir_model.is_dir():
-    print(f'Error: {args.model} is not a directory', file=sys.stderr)
-    sys.exit(1)
-
-ftype_map = {
-    "f32": gguf.GGMLQuantizationType.F32,
-    "f16": gguf.GGMLQuantizationType.F16,
-}
-
-if args.outfile is not None:
-    fname_out = args.outfile
-else:
-    # output in the same directory as the model by default
-    fname_out = dir_model / f'ggml-model-{args.outtype}.gguf'
-
-print(f"Loading model: {dir_model.name}")
-
-hparams = Model.load_hparams(dir_model)
-
-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 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()
-
-print(f"Model successfully exported to '{fname_out}'")

convert-llama-ggml-to-gguf.py

@@ -12,9 +12,29 @@ import numpy as np
 
 import os
 if 'NO_LOCAL_GGUF' not in os.environ:
-    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
 import gguf
 
+# Note: Does not support GGML_QKK_64
+QK_K = 256
+# Items here are (block size, type size)
+GGML_QUANT_SIZES = {
+    gguf.GGMLQuantizationType.F32  : (1, 4),
+    gguf.GGMLQuantizationType.F16  : (1, 2),
+    gguf.GGMLQuantizationType.Q4_0 : (32, 2 + 16),
+    gguf.GGMLQuantizationType.Q4_1 : (32, 2 + 2 + 16),
+    gguf.GGMLQuantizationType.Q5_0 : (32, 2 + 4 + 16),
+    gguf.GGMLQuantizationType.Q5_1 : (32, 2 + 2 + 4 + 16),
+    gguf.GGMLQuantizationType.Q8_0 : (32, 2 + 32),
+    gguf.GGMLQuantizationType.Q8_1 : (32, 4 + 4 + 32),
+    gguf.GGMLQuantizationType.Q2_K : (256, 2 + 2 + QK_K // 16 + QK_K // 4),
+    gguf.GGMLQuantizationType.Q3_K : (256, 2 + QK_K // 4 + QK_K // 8 + 12),
+    gguf.GGMLQuantizationType.Q4_K : (256, 2 + 2 + QK_K // 2 + 12),
+    gguf.GGMLQuantizationType.Q5_K : (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12),
+    gguf.GGMLQuantizationType.Q6_K : (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16),
+    gguf.GGMLQuantizationType.Q8_K : (256, 4 + QK_K + QK_K // 8),
+}
+
 class GGMLFormat(IntEnum):
     GGML = 0
     GGMF = 1
@@ -105,7 +125,7 @@ class Tensor:
         (n_dims, name_len, dtype) = struct.unpack('<3I', data[offset:offset + 12])
         assert n_dims >= 0 and n_dims <= 4, f'Invalid tensor dimensions {n_dims}'
         assert name_len < 4096, 'Absurd tensor name length'
-        quant = gguf.GGML_QUANT_SIZES.get(dtype)
+        quant = GGML_QUANT_SIZES.get(dtype)
         assert quant is not None, 'Unknown tensor type'
         (blksize, tysize) = quant
         offset += 12

convert-mpt-hf-to-gguf.py

@@ -0,0 +1,227 @@
+#!/usr/bin/env python3
+# HF mpt--> gguf conversion
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import struct
+import sys
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+from transformers import AutoTokenizer  # type: ignore[import]
+
+if 'NO_LOCAL_GGUF' not in os.environ:
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
+import gguf
+
+
+def count_model_parts(dir_model: Path) -> int:
+    num_parts = 0
+    for filename in os.listdir(dir_model):
+        if filename.startswith("pytorch_model-"):
+            num_parts += 1
+
+    if num_parts > 0:
+        print("gguf: found " + str(num_parts) + " model parts")
+    return num_parts
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Convert an MPT model to a GGML compatible file")
+    parser.add_argument(
+        "--vocab-only", action="store_true",
+        help="extract only the vocab",
+    )
+    parser.add_argument(
+        "--outfile", type=Path,
+        help="path to write to; default: based on input",
+    )
+    parser.add_argument(
+        "model", type=Path,
+        help="directory containing model file, or model file itself (*.bin)",
+    )
+    parser.add_argument(
+        "ftype", type=int, choices=[0, 1], default=1, nargs='?',
+        help="output format - use 0 for float32, 1 for float16",
+    )
+    return parser.parse_args()
+
+args = parse_args()
+
+dir_model = args.model
+ftype = args.ftype
+if not dir_model.is_dir():
+    print(f'Error: {args.model} is not a directory', file = sys.stderr)
+    sys.exit(1)
+
+# possible tensor data types
+#   ftype == 0 -> float32
+#   ftype == 1 -> float16
+
+# map from ftype to string
+ftype_str = ["f32", "f16"]
+
+if args.outfile is not None:
+    fname_out = args.outfile
+else:
+    # output in the same directory as the model by default
+    fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
+
+print("gguf: loading model "+dir_model.name)
+
+with open(dir_model / "config.json", "r", encoding="utf-8") as f:
+    hparams = json.load(f)
+
+if hparams["architectures"][0] != "MPTForCausalLM":
+    print("Model architecture not supported: " + hparams["architectures"][0])
+
+    sys.exit()
+
+# get number of model parts
+num_parts = count_model_parts(dir_model)
+
+ARCH=gguf.MODEL_ARCH.MPT
+gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
+
+print("gguf: get model metadata")
+
+block_count = hparams["n_layers"]
+
+gguf_writer.add_name(dir_model.name)
+gguf_writer.add_context_length(hparams["max_seq_len"])
+gguf_writer.add_embedding_length(hparams["d_model"])
+gguf_writer.add_block_count(block_count)
+gguf_writer.add_feed_forward_length(4 * hparams["d_model"])
+gguf_writer.add_head_count(hparams["n_heads"])
+if kv_n_heads := hparams["attn_config"].get("kv_n_heads"):
+    gguf_writer.add_head_count_kv(kv_n_heads)
+gguf_writer.add_layer_norm_eps(1e-05)
+if hparams["attn_config"]["clip_qkv"] is not None:
+    gguf_writer.add_clamp_kqv(hparams["attn_config"]["clip_qkv"])
+gguf_writer.add_max_alibi_bias(hparams["attn_config"]["alibi_bias_max"])
+
+# TOKENIZATION
+
+print("gguf: get tokenizer metadata")
+
+tokens: list[bytearray] = []
+scores: list[float] = []
+toktypes: list[int] = []
+
+# gpt2 tokenizer
+gguf_writer.add_tokenizer_model("gpt2")
+
+print("gguf: get gpt2 tokenizer vocab")
+
+# MPT token embedding tensors have dimension 50432 (hparams["vocab_size"]), but
+# there are only 50254 (len(tokenizer.vocab)) tokens in the vocab, presumably to
+# accomodate some "reserved" tokens; this is causing problems down the line in
+# llama.cpp, so we pad the vocab with dummy tokens:
+
+vocab_size = hparams["vocab_size"]
+
+# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
+tokenizer = AutoTokenizer.from_pretrained(dir_model)
+
+added_vocab = tokenizer.get_added_vocab()
+reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
+
+for i in range(vocab_size):
+    if i not in reverse_vocab:
+        tokens.append(f"[PAD{i}]")
+        toktypes.append(gguf.TokenType.USER_DEFINED)
+    elif reverse_vocab[i] in added_vocab:
+        tokens.append(reverse_vocab[i])
+        if tokenizer.added_tokens_decoder[i].special:
+            toktypes.append(gguf.TokenType.CONTROL)
+        else:
+            toktypes.append(gguf.TokenType.USER_DEFINED)
+    else:
+        tokens.append(reverse_vocab[i])
+        toktypes.append(gguf.TokenType.NORMAL)
+
+gguf_writer.add_token_list(tokens)
+gguf_writer.add_token_types(toktypes)
+
+special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens))
+special_vocab.add_to_gguf(gguf_writer)
+
+# TENSORS
+
+tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
+
+# tensor info
+print("gguf: get tensor metadata")
+
+if num_parts == 0:
+    part_names = iter(("pytorch_model.bin",))
+else:
+    part_names = (
+        f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
+    )
+
+for part_name in part_names:
+    if args.vocab_only:
+        break
+    print("gguf: loading model part '" + part_name + "'")
+    model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
+
+    for name in model_part.keys():
+        data = model_part[name]
+
+        old_dtype = data.dtype
+
+        # convert any unsupported data types to float32
+        if data.dtype != torch.float16 and data.dtype != torch.float32:
+            data = data.to(torch.float32)
+
+        data = data.squeeze().numpy()
+
+        # map tensor names
+        new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
+        if new_name is None:
+            print("Cannot map tensor '" + name + "'")
+            continue # for the sake of compatibility with some old published models, don't quit
+            sys.exit()
+
+        n_dims = len(data.shape)
+        data_dtype = data.dtype
+
+        # if f32 desired, convert any float16 to float32
+        if 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 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 ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+            data = data.astype(np.float16)
+
+        print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
+
+        gguf_writer.add_tensor(new_name, data)
+
+        # note: MPT output is tied to (same as) wte in original model;
+        # for easier implementation in llama.cpp it's duplicated in GGUF, though :/
+        if new_name == "token_embd.weight":
+            gguf_writer.add_tensor("output.weight", data)
+
+print("gguf: write header")
+gguf_writer.write_header_to_file()
+print("gguf: write metadata")
+gguf_writer.write_kv_data_to_file()
+if not args.vocab_only:
+    print("gguf: write tensors")
+    gguf_writer.write_tensors_to_file()
+
+gguf_writer.close()
+
+print(f"gguf: model successfully exported to '{fname_out}'")
+print("")

convert-persimmon-to-gguf.py

@@ -6,7 +6,7 @@ import argparse
 from pathlib import Path
 from sentencepiece import SentencePieceProcessor
 if 'NO_LOCAL_GGUF' not in os.environ:
-    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
 import gguf
 
 def _flatten_dict(dct, tensors, prefix=None):

convert-refact-hf-to-gguf.py

@@ -0,0 +1,272 @@
+#!/usr/bin/env python3
+# HF refact--> gguf conversion
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import sys
+from pathlib import Path
+
+import numpy as np
+import torch
+from transformers import AutoTokenizer  # type: ignore[import]
+
+if "NO_LOCAL_GGUF" not in os.environ:
+    sys.path.insert(1, str(Path(__file__).parent / "gguf-py" / "gguf"))
+import gguf
+
+def count_model_parts(dir_model: Path) -> int:
+    num_parts = 0
+    for filename in os.listdir(dir_model):
+        if filename.startswith("pytorch_model-"):
+            num_parts += 1
+
+    if num_parts > 0:
+        print("gguf: found " + str(num_parts) + " model parts")
+    return num_parts
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Convert a Refact model to a GGML compatible file"
+    )
+    parser.add_argument(
+        "--vocab-only",
+        action="store_true",
+        help="extract only the vocab",
+    )
+    parser.add_argument(
+        "--outfile",
+        type=Path,
+        help="path to write to; default: based on input",
+    )
+    parser.add_argument(
+        "model",
+        type=Path,
+        help="directory containing model file, or model file itself (*.bin)",
+    )
+    parser.add_argument(
+        "ftype",
+        type=int,
+        choices=[0, 1],
+        default=1,
+        nargs="?",
+        help="output format - use 0 for float32, 1 for float16",
+    )
+    return parser.parse_args()
+
+
+args = parse_args()
+
+dir_model = args.model
+ftype = args.ftype
+if not dir_model.is_dir():
+    print(f"Error: {args.model} is not a directory", file=sys.stderr)
+    sys.exit(1)
+
+# possible tensor data types
+#   ftype == 0 -> float32
+#   ftype == 1 -> float16
+
+# map from ftype to string
+ftype_str = ["f32", "f16"]
+
+if args.outfile is not None:
+    fname_out = args.outfile
+else:
+    # output in the same directory as the model by default
+    fname_out = dir_model / f"ggml-model-{ftype_str[ftype]}.gguf"
+
+print("gguf: loading model " + dir_model.name)
+
+with open(dir_model / "config.json", "r", encoding="utf-8") as f:
+    hparams = json.load(f)
+
+if hparams["architectures"][0] != "GPTRefactForCausalLM":
+    print("Model architecture not supported: " + hparams["architectures"][0])
+
+    sys.exit(1)
+
+# get number of model parts
+num_parts = count_model_parts(dir_model)
+
+ARCH = gguf.MODEL_ARCH.REFACT
+gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
+
+print("gguf: get model metadata")
+
+# Get refact feed forward dimension
+hidden_dim = hparams["n_embd"]
+inner_dim = 4 * hidden_dim
+hidden_dim = int(2 * inner_dim / 3)
+multiple_of = 256
+ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+
+block_count = hparams["n_layer"]
+
+gguf_writer.add_name("Refact")
+# refact uses Alibi. So this is from config.json which might be used by training.
+gguf_writer.add_context_length(hparams["n_positions"])
+gguf_writer.add_embedding_length(hparams["n_embd"])
+
+gguf_writer.add_feed_forward_length(ff_dim)
+gguf_writer.add_block_count(block_count)
+gguf_writer.add_head_count(hparams["n_head"])
+gguf_writer.add_head_count_kv(1)
+gguf_writer.add_layer_norm_rms_eps(hparams["layer_norm_epsilon"])
+gguf_writer.add_file_type(ftype)
+
+# TOKENIZATION
+
+print("gguf: get tokenizer metadata")
+
+tokens: list[bytearray] = []
+scores: list[float] = []
+toktypes: list[int] = []
+
+# gpt2 tokenizer
+gguf_writer.add_tokenizer_model("gpt2")
+
+print("gguf: get gpt2 tokenizer vocab")
+
+# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
+tokenizer = AutoTokenizer.from_pretrained(dir_model)
+
+# The number of tokens in tokenizer.json can differ from the expected vocab size.
+# This causes downstream issues with mismatched tensor sizes when running the inference
+vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
+assert max(tokenizer.vocab.values()) < vocab_size
+
+added_vocab = tokenizer.get_added_vocab()
+reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
+
+for i in range(vocab_size):
+    if i not in reverse_vocab:
+        tokens.append(f"[PAD{i}]")
+        toktypes.append(gguf.TokenType.USER_DEFINED)
+    elif reverse_vocab[i] in added_vocab:
+        tokens.append(reverse_vocab[i])
+        if tokenizer.added_tokens_decoder[i].special:
+            toktypes.append(gguf.TokenType.CONTROL)
+        else:
+            toktypes.append(gguf.TokenType.USER_DEFINED)
+    else:
+        tokens.append(reverse_vocab[i])
+        toktypes.append(gguf.TokenType.NORMAL)
+
+gguf_writer.add_token_list(tokens)
+gguf_writer.add_token_types(toktypes)
+
+special_vocab = gguf.SpecialVocab(dir_model, load_merges=True, n_vocab = len(tokens))
+special_vocab.add_to_gguf(gguf_writer)
+
+# TENSORS
+
+tensor_map = gguf.get_tensor_name_map(ARCH, block_count)
+
+# params for qkv transform
+n_head = hparams["n_head"]
+n_head_kv = 1
+
+head_dim = hparams["n_embd"] // n_head
+
+# tensor info
+print("gguf: get tensor metadata")
+
+if num_parts == 0:
+    part_names = iter(("pytorch_model.bin",))
+else:
+    part_names = (
+        f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
+    )
+for part_name in part_names:
+    if args.vocab_only:
+        break
+    print("gguf: loading model part '" + part_name + "'")
+    model_part = torch.load(dir_model / part_name, map_location="cpu")
+
+    for i in range(block_count):
+        if f"transformer.h.{i}.attn.kv.weight" in model_part:
+            data = model_part[f"transformer.h.{i}.attn.kv.weight"]
+            model_part[f"model.layers.{i}.self_attn.k_proj.weight"] = data[
+                : n_head_kv * head_dim
+            ]
+            model_part[f"model.layers.{i}.self_attn.v_proj.weight"] = data[
+                n_head_kv * head_dim :
+            ]
+            del model_part[f"transformer.h.{i}.attn.kv.weight"]
+        if f"transformer.h.{i}.attn.q.weight" in model_part:
+            model_part[f"model.layers.{i}.self_attn.q_proj.weight"] = model_part[
+                f"transformer.h.{i}.attn.q.weight"
+            ]
+            del model_part[f"transformer.h.{i}.attn.q.weight"]
+        if f"transformer.h.{i}.mlp.gate_up_proj.weight" in model_part:
+            data = model_part[f"transformer.h.{i}.mlp.gate_up_proj.weight"]
+            model_part[f"model.layers.{i}.mlp.gate_proj.weight"] = data[:ff_dim]
+            model_part[f"model.layers.{i}.mlp.up_proj.weight"] = data[ff_dim:]
+            del model_part[f"transformer.h.{i}.mlp.gate_up_proj.weight"]
+
+    for name in model_part.keys():
+        data = model_part[name]
+
+        old_dtype = data.dtype
+
+        # convert any unsupported data types to float32
+        if data.dtype != torch.float16 and data.dtype != torch.float32:
+            data = data.to(torch.float32)
+
+        data = data.squeeze().numpy()
+
+        # map tensor names
+        new_name = tensor_map.get_name(name, try_suffixes=(".weight",))
+        if new_name is None:
+            print("Can not map tensor '" + name + "'")
+            sys.exit()
+
+        n_dims = len(data.shape)
+        data_dtype = data.dtype
+
+        # if f32 desired, convert any float16 to float32
+        if 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 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 (
+            ftype == 1
+            and data_dtype == np.float32
+            and name.endswith(".weight")
+            and n_dims == 2
+        ):
+            data = data.astype(np.float16)
+
+        print(
+            new_name
+            + ", n_dims = "
+            + str(n_dims)
+            + ", "
+            + str(old_dtype)
+            + " --> "
+            + str(data.dtype)
+        )
+
+        gguf_writer.add_tensor(new_name, data)
+
+
+print("gguf: write header")
+gguf_writer.write_header_to_file()
+print("gguf: write metadata")
+gguf_writer.write_kv_data_to_file()
+if not args.vocab_only:
+    print("gguf: write tensors")
+    gguf_writer.write_tensors_to_file()
+
+gguf_writer.close()
+
+print(f"gguf: model successfully exported to '{fname_out}'")
+print("")

convert-starcoder-hf-to-gguf.py

@@ -0,0 +1,210 @@
+#!/usr/bin/env python3
+# HF starcoder --> gguf conversion
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import struct
+import sys
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+from transformers import AutoTokenizer  # type: ignore[import]
+
+if 'NO_LOCAL_GGUF' not in os.environ:
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
+import gguf
+
+
+def count_model_parts(dir_model: Path) -> int:
+    num_parts = 0
+    for filename in os.listdir(dir_model):
+        if filename.startswith("pytorch_model-"):
+            num_parts += 1
+
+    if num_parts > 0:
+        print("gguf: found " + str(num_parts) + " model parts")
+    return num_parts
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Convert a StarCoder model to a GGML compatible file")
+    parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
+    parser.add_argument("--outfile",    type=Path,           help="path to write to; default: based on input")
+    parser.add_argument("model",        type=Path,           help="directory containing model file, or model file itself (*.bin)")
+    parser.add_argument("ftype",        type=int,            help="output format - use 0 for float32, 1 for float16", choices=[0, 1], default = 1)
+    return parser.parse_args()
+
+args = parse_args()
+
+dir_model = args.model
+ftype = args.ftype
+if not dir_model.is_dir():
+    print(f'Error: {args.model} is not a directory', file = sys.stderr)
+    sys.exit(1)
+
+# possible tensor data types
+#   ftype == 0 -> float32
+#   ftype == 1 -> float16
+
+# map from ftype to string
+ftype_str = ["f32", "f16"]
+
+if args.outfile is not None:
+    fname_out = args.outfile
+else:
+    # output in the same directory as the model by default
+    fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
+
+print("gguf: loading model "+dir_model.name)
+
+with open(dir_model / "config.json", "r", encoding="utf-8") as f:
+    hparams = json.load(f)
+
+if hparams["architectures"][0] != "GPTBigCodeForCausalLM":
+    print("Model architecture not supported: " + hparams["architectures"][0])
+
+    sys.exit(1)
+
+# get number of model parts
+num_parts = count_model_parts(dir_model)
+
+ARCH=gguf.MODEL_ARCH.STARCODER
+gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
+
+print("gguf: get model metadata")
+
+block_count = hparams["n_layer"]
+
+gguf_writer.add_name("StarCoder")
+gguf_writer.add_context_length(hparams["n_positions"])
+gguf_writer.add_embedding_length(hparams["n_embd"])
+gguf_writer.add_feed_forward_length(4 * hparams["n_embd"])
+gguf_writer.add_block_count(block_count)
+gguf_writer.add_head_count(hparams["n_head"])
+gguf_writer.add_head_count_kv(1)
+gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"])
+gguf_writer.add_file_type(ftype)
+
+# TOKENIZATION
+
+print("gguf: get tokenizer metadata")
+
+tokens: list[bytearray] = []
+scores: list[float] = []
+toktypes: list[int] = []
+
+# gpt2 tokenizer
+gguf_writer.add_tokenizer_model("gpt2")
+
+print("gguf: get gpt2 tokenizer vocab")
+
+# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
+tokenizer = AutoTokenizer.from_pretrained(dir_model)
+
+# The number of tokens in tokenizer.json can differ from the expected vocab size.
+# This causes downstream issues with mismatched tensor sizes when running the inference
+vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
+assert max(tokenizer.vocab.values()) < vocab_size
+
+added_vocab = tokenizer.get_added_vocab()
+reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
+
+for i in range(vocab_size):
+    if i not in reverse_vocab:
+        tokens.append(f"[PAD{i}]")
+        toktypes.append(gguf.TokenType.USER_DEFINED)
+    elif reverse_vocab[i] in added_vocab:
+        tokens.append(reverse_vocab[i])
+        if tokenizer.added_tokens_decoder[i].special:
+            toktypes.append(gguf.TokenType.CONTROL)
+        else:
+            toktypes.append(gguf.TokenType.USER_DEFINED)
+    else:
+        tokens.append(reverse_vocab[i])
+        toktypes.append(gguf.TokenType.NORMAL)
+
+gguf_writer.add_token_list(tokens)
+gguf_writer.add_token_types(toktypes)
+special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens))
+special_vocab.add_to_gguf(gguf_writer)
+
+# TENSORS
+
+tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
+
+# params for qkv transform
+n_head    = hparams["n_head"]
+n_head_kv = hparams["n_head_kv"] if "n_head_kv" in hparams else 1
+
+head_dim = hparams["n_embd"] // n_head
+
+# tensor info
+print("gguf: get tensor metadata")
+
+if num_parts == 0:
+    part_names = iter(("pytorch_model.bin",))
+else:
+    part_names = (
+        f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
+    )
+
+for part_name in part_names:
+    if args.vocab_only:
+        break
+    print("gguf: loading model part '" + part_name + "'")
+    model_part = torch.load(dir_model / part_name, map_location="cpu")
+
+    for name in model_part.keys():
+        data = model_part[name]
+
+        old_dtype = data.dtype
+
+        # convert any unsupported data types to float32
+        if data.dtype != torch.float16 and data.dtype != torch.float32:
+            data = data.to(torch.float32)
+
+        data = data.squeeze().numpy()
+
+        # map tensor names
+        new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
+        if new_name is None:
+            print("Can not map tensor '" + name + "'")
+            sys.exit()
+
+        n_dims = len(data.shape)
+        data_dtype = data.dtype
+
+        # if f32 desired, convert any float16 to float32
+        if 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 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 ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+            data = data.astype(np.float16)
+
+        print(name, "=>", new_name + ", shape = " + str(data.shape) + ", " + str(old_dtype) + " --> " + str(data.dtype))
+
+        gguf_writer.add_tensor(new_name, data)
+
+
+print("gguf: write header")
+gguf_writer.write_header_to_file()
+print("gguf: write metadata")
+gguf_writer.write_kv_data_to_file()
+if not args.vocab_only:
+    print("gguf: write tensors")
+    gguf_writer.write_tensors_to_file()
+
+gguf_writer.close()
+
+print(f"gguf: model successfully exported to '{fname_out}'")
+print("")

convert.py

@@ -3,9 +3,11 @@ from __future__ import annotations
 
 import argparse
 import concurrent.futures
+import copy
 import enum
 import faulthandler
 import functools
+import io
 import itertools
 import json
 import math
@@ -21,14 +23,14 @@ from abc import ABCMeta, abstractmethod
 from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
 from dataclasses import dataclass
 from pathlib import Path
-from typing import IO, TYPE_CHECKING, Any, Callable, Iterable, Literal, TypeVar
+from typing import IO, TYPE_CHECKING, Any, Callable, Generator, Iterable, Literal, Sequence, TypeVar
 
 import numpy as np
-from sentencepiece import SentencePieceProcessor
+from sentencepiece import SentencePieceProcessor  # type: ignore[import]
 
 import os
 if 'NO_LOCAL_GGUF' not in os.environ:
-    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
 import gguf
 
 if TYPE_CHECKING:
@@ -326,7 +328,7 @@ class BpeVocab:
 
     def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
         tokenizer = self.bpe_tokenizer
-        from transformers.models.gpt2 import tokenization_gpt2
+        from transformers.models.gpt2 import tokenization_gpt2  # type: ignore[import]
         reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.items()}
 
         for i, _ in enumerate(tokenizer):
@@ -849,7 +851,7 @@ class OutputFile:
         elif isinstance(vocab, BpeVocab):
             self.gguf.add_tokenizer_model("gpt2")
         else:
-            raise ValueError('Unknown vocab type: Not BpeVocab or SentencePieceVocab')
+            raise ValueError(f'Unknown vocab type: Not BpeVocab or SentencePieceVocab')
         self.gguf.add_token_list(tokens)
         self.gguf.add_token_scores(scores)
         self.gguf.add_token_types(toktypes)
@@ -903,7 +905,7 @@ class OutputFile:
         return dt.quantize(arr)
 
     @staticmethod
-    def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE) -> None:
+    def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess=gguf.GGUFEndian.LITTLE) -> None:
         check_vocab_size(params, vocab)
 
         of = OutputFile(fname_out, endianess=endianess)
@@ -1112,15 +1114,11 @@ def do_dump_model(model_plus: ModelPlus) -> None:
 
 
 def main(args_in: list[str] | None = None) -> None:
-    output_choices = ["f32", "f16"]
-    if np.uint32(1) == np.uint32(1).newbyteorder("<"):
-        # We currently only support Q8_0 output on little endian systems.
-        output_choices.append("q8_0")
     parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file")
     parser.add_argument("--dump",        action="store_true",    help="don't convert, just show what's in the model")
     parser.add_argument("--dump-single", action="store_true",    help="don't convert, just show what's in a single model file")
     parser.add_argument("--vocab-only",  action="store_true",    help="extract only the vocab")
-    parser.add_argument("--outtype",     choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
+    parser.add_argument("--outtype",     choices=["f32", "f16", "q8_0"], help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
     parser.add_argument("--vocab-dir",   type=Path,              help="directory containing tokenizer.model, if separate from model file")
     parser.add_argument("--outfile",     type=Path,              help="path to write to; default: based on input")
     parser.add_argument("model",         type=Path,              help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")

examples/finetune/finetune.cpp

@@ -643,7 +643,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
 
         return ggml_rope_custom(ctx,
             t, KQ_pos, n_rot, rope_mode, n_ctx, 0,
-            rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f
+            rope_freq_base, rope_freq_scale, 0.0f, 0.0f, 0.0f, 0.0f
         );
     };
 

examples/llava/CMakeLists.txt

@@ -1,36 +1,14 @@
-add_library(llava OBJECT
-            llava.cpp
-            llava.h
-            clip.cpp
-            clip.h
-            )
-
-target_link_libraries(llava PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT})
-
-target_include_directories(llava PUBLIC .)
-target_include_directories(llava PUBLIC ../..)
-target_include_directories(llava PUBLIC ../../common)
-
-target_compile_features(llava PRIVATE cxx_std_11)
-
-add_library(llava_static STATIC $<TARGET_OBJECTS:llava>)
-if (BUILD_SHARED_LIBS)
-    set_target_properties(llava PROPERTIES POSITION_INDEPENDENT_CODE ON)
-    target_compile_definitions(llava PRIVATE LLAMA_SHARED LLAMA_BUILD)
-    add_library(llava_shared SHARED $<TARGET_OBJECTS:llava>)
-    target_link_libraries(llava_shared PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT})
-    install(TARGETS llava_shared LIBRARY)
-endif()
-
+set(TARGET clip)
+add_library(${TARGET} clip.cpp clip.h)
+install(TARGETS ${TARGET} LIBRARY)
+target_link_libraries(${TARGET} PRIVATE common ggml ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
 if (NOT MSVC)
-    target_compile_options(llava PRIVATE -Wno-cast-qual) # stb_image.h
-    endif()
-if(TARGET BUILD_INFO)
-    add_dependencies(llava BUILD_INFO)
+    target_compile_options(${TARGET} PRIVATE -Wno-cast-qual) # stb_image.h
 endif()
 
-set(TARGET llava-cli)
-add_executable(llava-cli llava-cli.cpp)
-install(TARGETS llava-cli RUNTIME)
-target_link_libraries(llava-cli PRIVATE common llama llava ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(llava PRIVATE cxx_std_11)
+set(TARGET llava)
+add_executable(${TARGET} llava.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama clip ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)

examples/llava/README.md

@@ -9,12 +9,12 @@ models are available.
 After API is confirmed, more models will be supported / uploaded.
 
 ## Usage
-Build with cmake or run `make llava-cli` to build it.
+Build with cmake or run `make llava` to build it.
 
-After building, run: `./llava-cli` to see the usage. For example:
+After building, run: `./llava` to see the usage. For example:
 
 ```sh
-./llava-cli -m llava-v1.5-7b/ggml-model-q5_k.gguf --mmproj llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg
+./llava -m llava-v1.5-7b/ggml-model-q5_k.gguf --mmproj llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg
 ```
 
 **note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so.
@@ -51,6 +51,7 @@ Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` director
 
 ## TODO
 
+- [ ] Support server mode.
 - [ ] Support non-CPU backend for the image encoding part.
 - [ ] Support different sampling methods.
 - [ ] Support more model variants.

examples/llava/clip.cpp

@@ -680,44 +680,26 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
     return new_clip;
 }
 
-clip_image_u8 * make_clip_image_u8() {
-    auto img = new clip_image_u8();
-    return img;
-}
+clip_image_u8 * make_clip_image_u8() { return new clip_image_u8(); }
+
 clip_image_f32 * make_clip_image_f32() { return new clip_image_f32(); }
 
-void clip_image_u8_free(clip_image_u8 * img) { if (img->data) { delete[] img->data; } delete img; }
-void clip_image_f32_free(clip_image_f32 * img) { if (img->data) { delete[] img->data; } delete img; }
-
-static void build_clip_img_from_data(const stbi_uc * data, int nx, int ny, clip_image_u8 * img) {
-    img->nx = nx;
-    img->ny = ny;
-    img->size = nx * ny * 3;
-    img->data = new uint8_t[img->size]();
-    memcpy(img->data, data, img->size);
-}
-
 bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) {
     int nx, ny, nc;
     auto data = stbi_load(fname, &nx, &ny, &nc, 3);
     if (!data) {
-        fprintf(stderr, "%s: failed to load image '%s'\n", __func__, fname);
+        fprintf(stderr, "%s: failed to load '%s'\n", __func__, fname);
         return false;
     }
-    build_clip_img_from_data(data, nx, ny, img);
-    stbi_image_free(data);
-    return true;
-}
 
-bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img) {
-    int nx, ny, nc;
-    auto data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3);
-    if (!data) {
-        fprintf(stderr, "%s: failed to decode image bytes\n", __func__);
-        return false;
-    }
-    build_clip_img_from_data(data, nx, ny, img);
+    img->nx = nx;
+    img->ny = ny;
+    img->size = nx * ny * 3;
+    img->data = new uint8_t[img->size]();
+    memcpy(img->data, data, img->size);
+
     stbi_image_free(data);
+
     return true;
 }
 
@@ -732,40 +714,39 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
     // the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104)
     // see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156
 
-    clip_image_u8 * temp = make_clip_image_u8(); // we will keep the input image data here temporarily
+    clip_image_u8 temp; // we will keep the input image data here temporarily
     if (pad2square && img->nx != img->ny) {
         int longer_side = std::max(img->nx, img->ny);
-        temp->nx = longer_side;
-        temp->ny = longer_side;
-        temp->size = 3 * longer_side * longer_side;
-        temp->data = new uint8_t[temp->size]();
+        temp.nx = longer_side;
+        temp.ny = longer_side;
+        temp.size = 3 * longer_side * longer_side;
+        temp.data = new uint8_t[temp.size]();
         uint8_t bc[3] = {122, 116, 104}; // bakground color in RGB from LLaVA
 
         // fill with background color
-        for (size_t i = 0; i < temp->size; i++) {
-            temp->data[i] = bc[i % 3];
+        for (size_t i = 0; i < temp.size; i++) {
+            temp.data[i] = bc[i % 3];
         }
 
         // copy from the input image
         for (int y = 0; y < img->ny; y++) {
             for (int x = 0; x < img->nx; x++) {
                 const int i = 3 * (y * img->nx + x);
-                const int j = 3 * (y * temp->nx + x);
-                temp->data[j] = img->data[i];
-                temp->data[j+1] = img->data[i+1];
-                temp->data[j+2] = img->data[i+2];
+                const int j = 3 * (y * temp.nx + x);
+                temp.data[j] = img->data[i];
+                temp.data[j+1] = img->data[i+1];
+                temp.data[j+2] = img->data[i+2];
             }
         }
     } else {
-        temp->nx   = img->nx;
-        temp->ny   = img->ny;
-        temp->size = img->size;
-        temp->data = new uint8_t[temp->size]();
-        *temp->data = *img->data; // copy
+        temp.nx   = img->nx;
+        temp.ny   = img->ny;
+        temp.size = img->size;
+        temp.data = img->data;
     }
 
-    const int nx = temp->nx;
-    const int ny = temp->ny;
+    const int nx = temp.nx;
+    const int ny = temp.ny;
 
     const int nx2 = ctx->vision_model.hparams.image_size;
     const int ny2 = ctx->vision_model.hparams.image_size;
@@ -804,10 +785,10 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
                 const int j10 = 3 * (y1 * nx + x0) + c;
                 const int j11 = 3 * (y1 * nx + x1) + c;
 
-                const float v00 = temp->data[j00];
-                const float v01 = temp->data[j01];
-                const float v10 = temp->data[j10];
-                const float v11 = temp->data[j11];
+                const float v00 = temp.data[j00];
+                const float v01 = temp.data[j01];
+                const float v10 = temp.data[j10];
+                const float v11 = temp.data[j11];
 
                 const float v0 = v00 * (1.0f - dx) + v01 * dx;
                 const float v1 = v10 * (1.0f - dx) + v11 * dx;
@@ -822,7 +803,6 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
             }
         }
     }
-    clip_image_u8_free(temp);
 
     return true;
 }
@@ -1069,16 +1049,16 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
     return true;
 }
 
-int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
+int clip_n_mmproj_embd(struct clip_ctx * ctx) {
     return ctx->vision_model.mm_2_b->ne[0];
 }
 
-int clip_n_patches(const struct clip_ctx * ctx) {
+int clip_n_patches(struct clip_ctx * ctx) {
     auto & params = ctx->vision_model.hparams;
 
     return (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
 }
 
-size_t clip_embd_nbytes(const struct clip_ctx * ctx) {
+size_t clip_embd_nbytes(struct clip_ctx * ctx) {
     return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float);
 }

examples/llava/clip.h

@@ -1,22 +1,7 @@
 #ifndef CLIP_H
 #define CLIP_H
 
-#include <stddef.h>
-#include <stdint.h>
-
-#ifdef LLAMA_SHARED
-#    if defined(_WIN32) && !defined(__MINGW32__)
-#        ifdef LLAMA_BUILD
-#            define CLIP_API __declspec(dllexport)
-#        else
-#            define CLIP_API __declspec(dllimport)
-#        endif
-#    else
-#        define CLIP_API __attribute__ ((visibility ("default")))
-#    endif
-#else
-#    define CLIP_API
-#endif
+#include "ggml.h"
 
 struct clip_ctx;
 
@@ -35,20 +20,19 @@ struct clip_vision_hparams {
     float eps;
 };
 
-/** load mmproj model */
-CLIP_API struct clip_ctx * clip_model_load(const char * fname, const int verbosity);
-/** free mmproj model */
-CLIP_API void clip_free(struct clip_ctx * ctx);
+struct clip_ctx * clip_model_load(const char * fname, const int verbosity);
 
-size_t clip_embd_nbytes(const struct clip_ctx * ctx);
-int clip_n_patches(const struct clip_ctx * ctx);
-int clip_n_mmproj_embd(const struct clip_ctx * ctx);
+void clip_free(struct clip_ctx * ctx);
+
+size_t clip_embd_nbytes(struct clip_ctx * ctx);
+int clip_n_patches(struct clip_ctx * ctx);
+int clip_n_mmproj_embd(struct clip_ctx * ctx);
 
 // RGB uint8 image
 struct clip_image_u8 {
     int nx;
     int ny;
-    uint8_t * data = NULL;
+    uint8_t * data;
     size_t size;
 };
 
@@ -57,7 +41,7 @@ struct clip_image_u8 {
 struct clip_image_f32 {
     int nx;
     int ny;
-    float * data = NULL;
+    float * data;
     size_t size;
 };
 
@@ -73,12 +57,7 @@ struct clip_image_f32_batch {
 
 struct clip_image_u8 * make_clip_image_u8();
 struct clip_image_f32 * make_clip_image_f32();
-CLIP_API void clip_image_u8_free(clip_image_u8 * img);
-CLIP_API void clip_image_f32_free(clip_image_f32 * img);
-CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img);
-/** interpret bytes as an image file with length bytes_length, and use the result to populate img */
-CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img);
-
+bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img);
 bool clip_image_preprocess(const struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32 * res, const bool pad2square);
 bool clip_image_encode(const struct clip_ctx * ctx, const int n_threads, struct clip_image_f32 * img, float * vec);
 

examples/llava/llava-cli.cpp

@@ -1,313 +0,0 @@
-#include "ggml.h"
-#include "common.h"
-#include "clip.h"
-#include "llava.h"
-#include "llama.h"
-
-#include "base64.hpp"
-
-#include <cstdio>
-#include <cstdlib>
-#include <vector>
-
-static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_token> tokens, int n_batch, int * n_past) {
-    int N = (int) tokens.size();
-    for (int i = 0; i < N; i += n_batch) {
-        int n_eval = (int) tokens.size() - i;
-        if (n_eval > n_batch) {
-            n_eval = n_batch;
-        }
-        if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) {
-            fprintf(stderr, "%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
-            return false;
-        }
-        *n_past += n_eval;
-    }
-    return true;
-}
-
-static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) {
-    std::vector<llama_token> tokens;
-    tokens.push_back(id);
-    return eval_tokens(ctx_llama, tokens, 1, n_past);
-}
-
-static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){
-    std::string              str2     = str;
-    std::vector<llama_token> embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos);
-    eval_tokens(ctx_llama, embd_inp, n_batch, n_past);
-    return true;
-}
-
-// TODO: use common/sampling.h
-static llama_token sample_id(llama_context * ctx_llama, gpt_params & params) {
-    auto & sparams = params.sparams;
-
-    // out of user input, sample next token
-    const float   temp      = sparams.temp;
-    const int32_t top_k     = sparams.top_k <= 0 ? llama_n_vocab(llama_get_model(ctx_llama)) : sparams.top_k;
-    const float   top_p     = sparams.top_p;
-    const float   tfs_z     = sparams.tfs_z;
-    const float   typical_p = sparams.typical_p;
-    // const int32_t repeat_last_n   = sparams.repeat_last_n < 0 ? n_ctx : sparams.repeat_last_n;
-    // const float   repeat_penalty  = sparams.repeat_penalty;
-    // const float   alpha_presence  = sparams.presence_penalty;
-    // const float   alpha_frequency = sparams.frequency_penalty;
-    const int     mirostat     = sparams.mirostat;
-    const float   mirostat_tau = sparams.mirostat_tau;
-    const float   mirostat_eta = sparams.mirostat_eta;
-    // const bool    penalize_nl     = sparams.penalize_nl;
-
-    llama_token id = 0;
-    {
-        auto logits  = llama_get_logits(ctx_llama);
-        auto n_vocab = llama_n_vocab(llama_get_model(ctx_llama));
-
-        // Apply params.logit_bias map
-        for (auto it = sparams.logit_bias.begin(); it != sparams.logit_bias.end(); it++) {
-            logits[it->first] += it->second;
-        }
-
-        std::vector<llama_token_data> candidates;
-        candidates.reserve(n_vocab);
-        for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-            candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
-        }
-
-        llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
-
-        if (temp <= 0) {
-              // Greedy sampling
-            id = llama_sample_token_greedy(ctx_llama, &candidates_p);
-        } else {
-            if (mirostat == 1) {
-                static float mirostat_mu = 2.0f * mirostat_tau;
-                const  int mirostat_m    = 100;
-                llama_sample_temp(ctx_llama, &candidates_p, temp);
-                id = llama_sample_token_mirostat(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
-            } else if (mirostat == 2) {
-                static float mirostat_mu = 2.0f * mirostat_tau;
-                llama_sample_temp(ctx_llama, &candidates_p, temp);
-                id = llama_sample_token_mirostat_v2(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
-            } else {
-                  // Temperature sampling
-                llama_sample_top_k(ctx_llama, &candidates_p, top_k, 1);
-                llama_sample_tail_free(ctx_llama, &candidates_p, tfs_z, 1);
-                llama_sample_typical(ctx_llama, &candidates_p, typical_p, 1);
-                llama_sample_top_p(ctx_llama, &candidates_p, top_p, 1);
-                llama_sample_temp(ctx_llama, &candidates_p, temp);
-                id = llama_sample_token(ctx_llama, &candidates_p);
-            }
-        }
-    }
-
-    return id;
-}
-
-static const char * sample(struct llama_context * ctx_llama, gpt_params & params, int * n_past) {
-    int id = sample_id(ctx_llama, params);
-    static std::string ret;
-    if (id == llama_token_eos(llama_get_model(ctx_llama))) {
-        ret = "</s>";
-    } else {
-        ret = llama_token_to_piece(ctx_llama, id);
-    }
-    eval_id(ctx_llama, id, n_past);
-    return ret.c_str();
-}
-
-static const char* IMG_BASE64_TAG_BEGIN = "<img src=\"data:image/jpeg;base64,";
-static const char* IMG_BASE64_TAG_END = "\">";
-
-static void find_image_tag_in_prompt(const std::string& prompt, size_t& begin_out, size_t& end_out) {
-    begin_out = prompt.find(IMG_BASE64_TAG_BEGIN);
-    end_out = prompt.find(IMG_BASE64_TAG_END, (begin_out == std::string::npos) ? 0UL : begin_out);
-}
-
-static bool prompt_contains_image(const std::string& prompt) {
-    size_t begin, end;
-    find_image_tag_in_prompt(prompt, begin, end);
-    return (begin != std::string::npos);
-}
-
-// replaces the base64 image tag in the prompt with `replacement`
-static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip_ctx * ctx_clip, int n_threads, const std::string& prompt) {
-    size_t img_base64_str_start, img_base64_str_end;
-    find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end);
-    if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) {
-        fprintf(stderr, "%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
-        return NULL;
-    }
-
-    auto base64_bytes_start = img_base64_str_start + strlen(IMG_BASE64_TAG_BEGIN);
-    auto base64_bytes_count = img_base64_str_end - base64_bytes_start;
-    auto base64_str = prompt.substr(base64_bytes_start, base64_bytes_count );
-
-    auto required_bytes = base64::required_encode_size(base64_str.size());
-    auto img_bytes = std::vector<unsigned char>(required_bytes);
-    base64::decode(base64_str.begin(), base64_str.end(), img_bytes.begin());
-
-    auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size());
-    if (!embed) {
-        fprintf(stderr, "%s: could not load image from base64 string.\n", __func__);
-        return NULL;
-    }
-
-    return embed;
-}
-
-static std::string remove_image_from_prompt(const std::string& prompt, const char * replacement = "") {
-    size_t begin, end;
-    find_image_tag_in_prompt(prompt, begin, end);
-    if (begin == std::string::npos || end == std::string::npos) {
-        return prompt;
-    }
-    auto pre = prompt.substr(0, begin);
-    auto post = prompt.substr(end + strlen(IMG_BASE64_TAG_END));
-    return pre + replacement + post;
-}
-
-struct llava_context {
-    struct clip_ctx * ctx_clip = NULL;
-    struct llama_context * ctx_llama = NULL;
-    struct llama_model * model = NULL;
-};
-
-static void show_additional_info(int /*argc*/, char ** argv) {
-    printf("\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
-    printf("  note: a lower temperature value like 0.1 is recommended for better quality.\n");
-}
-
-static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params) {
-
-    // load and preprocess the image
-    llava_image_embed * embed = NULL;
-    auto prompt = params->prompt;
-    if (prompt_contains_image(prompt)) {
-        if (!params->image.empty()) {
-            printf("using base64 encoded image instead of command line image path\n");
-        }
-        embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->n_threads, prompt);
-        if (!embed) {
-            fprintf(stderr, "%s: can't load image from prompt\n", __func__);
-            return NULL;
-        }
-        params->prompt = remove_image_from_prompt(prompt);
-    } else {
-        embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, params->image.c_str());
-        if (!embed) {
-            fprintf(stderr, "%s: is %s really an image file?\n", __func__, params->image.c_str());
-            return NULL;
-        }
-    }
-
-    return embed;
-}
-
-static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, gpt_params * params, const std::string & prompt) {
-    int n_past = 0;
-
-    const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict;
-
-    // llava chat format is "<system_prompt>\nUSER:<image_embeddings>\n<textual_prompt>\nASSISTANT:"
-    eval_string(ctx_llava->ctx_llama, "A chat between a curious human and an artificial intelligence assistant.  The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:", params->n_batch, &n_past, true);
-    llava_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past);
-    eval_string(ctx_llava->ctx_llama, (prompt + "\nASSISTANT:").c_str(), params->n_batch, &n_past, false);
-
-    // generate the response
-
-    printf("\n");
-
-    for (int i = 0; i < max_tgt_len; i++) {
-        const char * tmp = sample(ctx_llava->ctx_llama, *params, &n_past);
-        if (strcmp(tmp, "</s>") == 0) break;
-
-        printf("%s", tmp);
-        fflush(stdout);
-    }
-
-    printf("\n");
-}
-
-
-static struct llava_context * llava_init(gpt_params * params) {
-    const char * clip_path = params->mmproj.c_str();
-
-    auto prompt = params->prompt;
-    if (prompt.empty()) {
-        prompt = "describe the image in detail.";
-    }
-
-    auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
-
-    llama_backend_init(params->numa);
-
-    llama_model_params model_params = llama_model_params_from_gpt_params(*params);
-
-    llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
-    if (model == NULL) {
-        fprintf(stderr , "%s: error: unable to load model\n" , __func__);
-        return NULL;
-    }
-
-    llama_context_params ctx_params = llama_context_params_from_gpt_params(*params);
-    ctx_params.n_ctx           = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings
-
-    llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
-
-    if (ctx_llama == NULL) {
-        fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
-        return NULL;
-    }
-
-    auto ctx_llava = (struct llava_context *)malloc(sizeof(llava_context));
-
-    ctx_llava->ctx_llama = ctx_llama;
-    ctx_llava->ctx_clip = ctx_clip;
-    ctx_llava->model = model;
-    return ctx_llava;
-}
-
-static void llava_free(struct llava_context * ctx_llava) {
-    if (ctx_llava->ctx_clip) {
-        clip_free(ctx_llava->ctx_clip);
-        ctx_llava->ctx_clip = NULL;
-    }
-
-    llama_free(ctx_llava->ctx_llama);
-    llama_free_model(ctx_llava->model);
-    llama_backend_free();
-}
-
-int main(int argc, char ** argv) {
-    ggml_time_init();
-
-    gpt_params params;
-
-    if (!gpt_params_parse(argc, argv, params)) {
-        show_additional_info(argc, argv);
-        return 1;
-    }
-    if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
-        gpt_print_usage(argc, argv, params);
-        show_additional_info(argc, argv);
-        return 1;
-    }
-
-    auto ctx_llava = llava_init(&params);
-    if (ctx_llava == NULL) {
-        fprintf(stderr, "%s: error: failed to init llava\n", __func__);
-        return 1;
-    }
-
-    auto image_embed = load_image(ctx_llava, &params);
-
-    // process the prompt
-    process_prompt(ctx_llava, image_embed, &params, params.prompt);
-
-    llama_print_timings(ctx_llava->ctx_llama);
-
-    llava_image_embed_free(image_embed);
-    llava_free(ctx_llava);
-    return 0;
-}

examples/llava/llava-utils.h

@@ -0,0 +1,147 @@
+#pragma once
+
+// this one and clip lib will be eventually merged to a single lib, let's keep it this way for now
+
+#include "common.h"
+#include "llama.h"
+
+#include <cstdio>
+#include <cstdlib>
+#include <vector>
+
+inline bool eval_image_embd(llama_context * ctx_llama, float * embd, int N, int n_batch, int * n_past) {
+    int n_embd  = llama_n_embd(llama_get_model(ctx_llama));
+
+    for (int i = 0; i < N; i += n_batch) {
+        int n_eval = N - i;
+        if (n_eval > n_batch) {
+            n_eval = n_batch;
+        }
+        llama_batch batch = {int32_t(n_eval), nullptr, (embd+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };
+        if (llama_decode(ctx_llama, batch)) {
+            fprintf(stderr, "%s : failed to eval\n", __func__);
+            return false;
+        }
+        *n_past += n_eval;
+    }
+    return true;
+}
+
+inline bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_token> tokens, int n_batch, int * n_past) {
+    int N = (int) tokens.size();
+    for (int i = 0; i < N; i += n_batch) {
+        int n_eval = (int) tokens.size() - i;
+        if (n_eval > n_batch) {
+            n_eval = n_batch;
+        }
+        if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) {
+            fprintf(stderr, "%s : failed to eval\n", __func__);
+            return false;
+        }
+        *n_past += n_eval;
+    }
+    return true;
+}
+
+inline bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) {
+    std::vector<llama_token> tokens;
+    tokens.push_back(id);
+    return eval_tokens(ctx_llama, tokens, 1, n_past);
+}
+
+inline bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){
+    std::string              str2     = str;
+    std::vector<llama_token> embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos);
+    eval_tokens(ctx_llama, embd_inp, n_batch, n_past);
+    return true;
+}
+
+// TODO: use common/sampling.h
+inline llama_token sample_id(llama_context * ctx_llama, gpt_params & params) {
+    auto & sparams = params.sparams;
+
+    // out of user input, sample next token
+    const float   temp      = sparams.temp;
+    const int32_t top_k     = sparams.top_k <= 0 ? llama_n_vocab(llama_get_model(ctx_llama)) : sparams.top_k;
+    const float   top_p     = sparams.top_p;
+    const float   tfs_z     = sparams.tfs_z;
+    const float   typical_p = sparams.typical_p;
+    // const int32_t repeat_last_n   = sparams.repeat_last_n < 0 ? n_ctx : sparams.repeat_last_n;
+    // const float   repeat_penalty  = sparams.repeat_penalty;
+    // const float   alpha_presence  = sparams.presence_penalty;
+    // const float   alpha_frequency = sparams.frequency_penalty;
+    const int     mirostat     = sparams.mirostat;
+    const float   mirostat_tau = sparams.mirostat_tau;
+    const float   mirostat_eta = sparams.mirostat_eta;
+    // const bool    penalize_nl     = sparams.penalize_nl;
+
+    llama_token id = 0;
+    {
+        auto logits  = llama_get_logits(ctx_llama);
+        auto n_vocab = llama_n_vocab(llama_get_model(ctx_llama));
+
+        // Apply params.logit_bias map
+        for (auto it = sparams.logit_bias.begin(); it != sparams.logit_bias.end(); it++) {
+            logits[it->first] += it->second;
+        }
+
+        std::vector<llama_token_data> candidates;
+        candidates.reserve(n_vocab);
+        for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+            candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
+        }
+
+        llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+
+        // TODO: Apply penalties
+        // float nl_logit = logits[llama_token_nl(ctx)];
+        // auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
+        // llama_sample_repetition_penalty(ctx, &candidates_p,
+        //      last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
+        //      last_n_repeat, repeat_penalty);
+        // llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
+        // last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
+        // last_n_repeat, alpha_frequency, alpha_presence);
+        // if (!penalize_nl) {
+        //     logits[llama_token_nl(ctx)] = nl_logit;
+        // }
+
+        if (temp <= 0) {
+              // Greedy sampling
+            id = llama_sample_token_greedy(ctx_llama, &candidates_p);
+        } else {
+            if (mirostat == 1) {
+                static float mirostat_mu = 2.0f * mirostat_tau;
+                const  int mirostat_m    = 100;
+                llama_sample_temp(ctx_llama, &candidates_p, temp);
+                id = llama_sample_token_mirostat(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
+            } else if (mirostat == 2) {
+                static float mirostat_mu = 2.0f * mirostat_tau;
+                llama_sample_temp(ctx_llama, &candidates_p, temp);
+                id = llama_sample_token_mirostat_v2(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
+            } else {
+                  // Temperature sampling
+                llama_sample_top_k(ctx_llama, &candidates_p, top_k, 1);
+                llama_sample_tail_free(ctx_llama, &candidates_p, tfs_z, 1);
+                llama_sample_typical(ctx_llama, &candidates_p, typical_p, 1);
+                llama_sample_top_p(ctx_llama, &candidates_p, top_p, 1);
+                llama_sample_temp(ctx_llama, &candidates_p, temp);
+                id = llama_sample_token(ctx_llama, &candidates_p);
+            }
+        }
+    }
+
+    return id;
+}
+
+inline const char * sample(struct llama_context * ctx_llama, gpt_params & params, int * n_past) {
+    int id = sample_id(ctx_llama, params);
+    static std::string ret;
+    if (id == llama_token_eos(llama_get_model(ctx_llama))) {
+        ret = "</s>";
+    } else {
+        ret = llama_token_to_piece(ctx_llama, id);
+    }
+    eval_id(ctx_llama, id, n_past);
+    return ret.c_str();
+}

examples/llava/llava.cpp

@@ -1,156 +1,164 @@
 #include "clip.h"
+#include "llava-utils.h"
 #include "common.h"
 #include "llama.h"
-#include "llava.h"
 
 #include <cstdio>
 #include <cstdlib>
 #include <vector>
 
-#include "base64.hpp"
-
-static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) {
-    clip_image_f32 * img_res = make_clip_image_f32();
-    if (!clip_image_preprocess(ctx_clip, img, img_res, /*pad2square =*/ true)) {
-        fprintf(stderr, "%s: unable to preprocess image\n", __func__);
-        clip_image_f32_free(img_res);
-        return false;
-    }
-
-    *n_img_pos = clip_n_patches(ctx_clip);
-
-    const int64_t t_img_enc_start_us = ggml_time_us();
-    bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd);
-    clip_image_f32_free(img_res);
-    if (!encoded) {
-        fprintf(stderr, "Unable to encode image\n");
-
-        return false;
-    }
-
-    const int64_t t_img_enc_end_us = ggml_time_us();
-    float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0;
-
-    printf("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos);
-
-    return true;
+static void show_additional_info(int /*argc*/, char ** argv) {
+    printf("\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
+    printf("  note: a lower temperature value like 0.1 is recommended for better quality.\n");
 }
 
-bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip) {
-        // make sure that the correct mmproj was used, i.e., compare apples to apples
-    int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama));
-    auto n_image_embd = clip_n_mmproj_embd(ctx_clip);
-    if (n_image_embd != n_llama_embd) {
-        printf("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
-        return false;
-    }
-    return true;
-}
+int main(int argc, char ** argv) {
+    ggml_time_init();
+
+    gpt_params params;
+
+    if (!gpt_params_parse(argc, argv, params)) {
+        show_additional_info(argc, argv);
+        return 1;
+    }
+
+    if (params.mmproj.empty() || params.image.empty()) {
+        gpt_print_usage(argc, argv, params);
+        show_additional_info(argc, argv);
+        return 1;
+    }
+
+    const char * clip_path = params.mmproj.c_str();
+    const char * img_path = params.image.c_str();
+
+    if (params.prompt.empty()) {
+        params.prompt = "describe the image in detail.";
+    }
+
+    auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
+
+    // load and preprocess the image
+    clip_image_u8 img;
+    clip_image_f32 img_res;
+
+    if (!clip_image_load_from_file(img_path, &img)) {
+        fprintf(stderr, "%s: is %s really an image file?\n", __func__, img_path);
+
+        clip_free(ctx_clip);
+        return 1;
+    }
+
+    if (!clip_image_preprocess(ctx_clip, &img, &img_res, /*pad2square =*/ true)) {
+        fprintf(stderr, "%s: unable to preprocess %s\n", __func__, img_path);
+
+        clip_free(ctx_clip);
+        return 1;
+    }
+
+    int n_img_pos  = clip_n_patches(ctx_clip);
+    int n_img_embd = clip_n_mmproj_embd(ctx_clip);
 
-static bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) {
     float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip));
+
     if (!image_embd) {
         fprintf(stderr, "Unable to allocate memory for image embeddings\n");
+
+        return 1;
+    }
+
+    const int64_t t_img_enc_start_us = ggml_time_us();
+    if (!clip_image_encode(ctx_clip, params.n_threads, &img_res, image_embd)) {
+        fprintf(stderr, "Unable to encode image\n");
+
+        return 1;
+    }
+    const int64_t t_img_enc_end_us = ggml_time_us();
+
+    // we get the embeddings, free up the memory required for CLIP
+    clip_free(ctx_clip);
+
+    llama_backend_init(params.numa);
+
+    llama_model_params model_params              = llama_model_default_params();
+                       model_params.n_gpu_layers = params.n_gpu_layers;
+                       model_params.main_gpu     = params.main_gpu;
+                       model_params.tensor_split = params.tensor_split;
+                       model_params.use_mmap     = params.use_mmap;
+                       model_params.use_mlock    = params.use_mlock;
+
+    llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
+    if (model == NULL) {
+        fprintf(stderr , "%s: error: unable to load model\n" , __func__);
+        return 1;
+    }
+
+    llama_context_params ctx_params = llama_context_default_params();
+
+    ctx_params.n_ctx           = params.n_ctx < 2048 ? 2048 : params.n_ctx; // we need a longer context size to process image embeddings
+    ctx_params.n_threads       = params.n_threads;
+    ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
+    ctx_params.seed            = params.seed;
+
+    llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
+
+    if (ctx_llama == NULL) {
+        fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
+        return 1;
+    }
+
+    // make sure that the correct mmproj was used, i.e., compare apples to apples
+    const int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama));
+
+    if (n_img_embd != n_llama_embd) {
+        printf("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_img_embd, n_llama_embd);
+
+        llama_free(ctx_llama);
+        llama_free_model(model);
+        llama_backend_free();
         free(image_embd);
-        return false;
+
+        return 1;
     }
 
-    int n_img_pos;
-    if (!encode_image_with_clip(ctx_clip, n_threads, img, image_embd, &n_img_pos)) {
-        fprintf(stderr, "%s: cannot encode image, aborting\n", __func__);
-        free(image_embd);
-        return false;
-    }
-    *image_embd_out = image_embd;
-    *n_img_pos_out = n_img_pos;
+    // process the prompt
+    // llava chat format is "<system_prompt>USER: <image_embeddings>\n<textual_prompt>\nASSISTANT:"
 
-    return true;
-}
-
-bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, int n_batch, int * n_past) {
-    int n_embd  = llama_n_embd(llama_get_model(ctx_llama));
-
-    for (int i = 0; i < image_embed->n_image_pos; i += n_batch) {
-        int n_eval = image_embed->n_image_pos - i;
-        if (n_eval > n_batch) {
-            n_eval = n_batch;
-        }
-        llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };
-        if (llama_decode(ctx_llama, batch)) {
-            fprintf(stderr, "%s : failed to eval\n", __func__);
-            return false;
-        }
-        *n_past += n_eval;
-    }
-    return true;
-}
-
-LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length) {
-    clip_image_u8 * img = make_clip_image_u8();
-    if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) {
-        clip_image_u8_free(img);
-        fprintf(stderr, "%s: can't load image from bytes, is it a valid image?", __func__);
-        return NULL;
-    }
-
-    float* image_embed = NULL;
-    int n_image_pos = 0;
-    bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos);
-    if (!image_embed_result) {
-        clip_image_u8_free(img);
-        fprintf(stderr, "%s: coulnd't embed the image\n", __func__);
-        return NULL;
-    }
-
-    clip_image_u8_free(img);
-    auto result = (llava_image_embed*)malloc(sizeof(llava_image_embed));
-    result->embed = image_embed;
-    result->n_image_pos = n_image_pos;
-    return result;
-}
-
-static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long *sizeOut) {
-    auto file = fopen(path, "rb");
-    if (file == NULL) {
-        fprintf(stderr, "%s: can't read file %s\n", __func__, path);
-        return false;
-    }
-
-    fseek(file, 0, SEEK_END);
-    auto fileSize = ftell(file);
-    fseek(file, 0, SEEK_SET);
-
-    auto buffer = (unsigned char *)malloc(fileSize); // Allocate memory to hold the file data
-    if (buffer == NULL) {
-        fprintf(stderr, "%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path);
-        perror("Memory allocation error");
-        fclose(file);
-        return false;
-    }
-    fread(buffer, 1, fileSize, file); // Read the file into the buffer
-    fclose(file); // Close the file
-
-    *bytesOut = buffer;
-    *sizeOut = fileSize;
-    return true;
-}
-
-LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path) {
-    unsigned char* image_bytes;
-    long image_bytes_length;
-    auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length);
-    if (!loaded) {
-        fprintf(stderr, "%s: failed to load %s\n", __func__, image_path);
-        return NULL;
-    }
-
-    auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, image_bytes, image_bytes_length);
-    free(image_bytes);
-
-    return embed;
-}
-
-LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed) {
-    free(embed->embed);
-    free(embed);
+    int n_past = 0;
+
+    const int max_tgt_len = params.n_predict < 0 ? 256 : params.n_predict;
+
+    eval_string(ctx_llama, "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:", params.n_batch, &n_past, true);
+    eval_image_embd(ctx_llama, image_embd, n_img_pos, params.n_batch, &n_past);
+    eval_string(ctx_llama, (params.prompt + "\nASSISTANT:").c_str(), params.n_batch, &n_past, false);
+
+    // generate the response
+
+    printf("\n");
+    printf("prompt: '%s'\n", params.prompt.c_str());
+    printf("\n");
+
+    for (int i = 0; i < max_tgt_len; i++) {
+        const char * tmp = sample(ctx_llama, params, &n_past);
+        if (strcmp(tmp, "</s>") == 0) break;
+
+        printf("%s", tmp);
+        fflush(stdout);
+    }
+
+    printf("\n");
+
+    {
+        const float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0;
+
+        printf("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / n_img_pos);
+    }
+
+    llama_print_timings(ctx_llama);
+
+    llama_free(ctx_llama);
+    llama_free_model(model);
+    llama_backend_free();
+    free(image_embd);
+
+    return 0;
 }

examples/llava/llava.h

@@ -1,50 +0,0 @@
-#ifndef LLAVA_H
-#define LLAVA_H
-
-#include "ggml.h"
-
-
-#ifdef LLAMA_SHARED
-#    if defined(_WIN32) && !defined(__MINGW32__)
-#        ifdef LLAMA_BUILD
-#            define LLAVA_API __declspec(dllexport)
-#        else
-#            define LLAVA_API __declspec(dllimport)
-#        endif
-#    else
-#        define LLAVA_API __attribute__ ((visibility ("default")))
-#    endif
-#else
-#    define LLAVA_API
-#endif
-
-struct clip_ctx;
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-struct llava_image_embed {
-    float * embed;
-    int n_image_pos;
-};
-
-/** sanity check for clip <-> llava embed size match */
-LLAVA_API bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip);
-
-/** build an image embed from image file bytes */
-LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length);
-/** build an image embed from a path to an image filename */
-LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path);
-LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed);
-/** free an embedding made with llava_image_embed_make_* */
-
-/** write the image represented by embed into the llama context with batch size n_batch, starting at context pos n_past. on completion, n_past points to the next position in the context after the image embed. */
-LLAVA_API bool llava_eval_image_embed(struct llama_context * ctx_llama, const struct llava_image_embed * embed, int n_batch, int * n_past);
-
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif

examples/server/CMakeLists.txt

@@ -6,7 +6,7 @@ install(TARGETS ${TARGET} RUNTIME)
 target_compile_definitions(${TARGET} PRIVATE
     SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
 )
-target_link_libraries(${TARGET} PRIVATE common llama llava ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE common llama clip ${CMAKE_THREAD_LIBS_INIT})
 if (WIN32)
     TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
 endif()

examples/server/README.md

@@ -7,7 +7,7 @@ Command line options:
 -   `--threads N`, `-t N`: Set the number of threads to use during generation.
 -   `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation.
 -   `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`).
--   `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
+-   `-m ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
 -   `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096.
 -   `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
 -   `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.
@@ -122,8 +122,6 @@ node index.js
 
     `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.95).
 
-    `min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token (default: 0.05).
-
     `n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. (default: -1, -1 = infinity).
 
     `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded.

examples/server/public/index.html

@@ -160,11 +160,6 @@
       height: 10em;
     }
 
-    [contenteditable] {
-      display: inline-block;
-      white-space: pre-wrap;
-      outline: 0px solid transparent;
-    }
 
     @keyframes loading-bg-wipe {
       0% {
@@ -224,7 +219,6 @@
       repeat_penalty: 1.18, // 1.0 = disabled
       top_k: 40, // <= 0 to use vocab size
       top_p: 0.5, // 1.0 = disabled
-      min_p: 0.05, // 0 = disabled
       tfs_z: 1.0, // 1.0 = disabled
       typical_p: 1.0, // 1.0 = disabled
       presence_penalty: 0.0, // 0.0 = disabled
@@ -467,23 +461,18 @@
       }, "{{char}}");
     }
 
-    const runCompletion = () => {
+    const runCompletion = async () => {
       if (controller.value) {
         console.log('already running...');
         return;
       }
       const { prompt } = session.value;
       transcriptUpdate([...session.value.transcript, ["", prompt]]);
-      runLlama(prompt, {
+      await runLlama(prompt, {
         ...params.value,
         slot_id: slot_id,
         stop: [],
-      }, "").finally(() => {
-        session.value.prompt = session.value.transcript.map(([_, data]) =>
-          Array.isArray(data) ? data.map(msg => msg.content).join('') : data
-        ).join('');
-        session.value.transcript = [];
-      })
+      }, "");
     }
 
     const stop = (e) => {
@@ -583,7 +572,6 @@
         }
       }, [messages])
 
-      const isCompletionMode = session.value.type === 'completion'
       const chatLine = ([user, data], index) => {
         let message
         const isArrayMessage = Array.isArray(data)
@@ -593,31 +581,20 @@
           const text = isArrayMessage ?
             data.map(msg => msg.content).join('').replace(/^\s+/, '') :
             data;
-          message = isCompletionMode ?
-            text :
-            html`<${Markdownish} text=${template(text)} />`
+          message = html`<${Markdownish} text=${template(text)} />`
         }
         if (user) {
           return html`<p key=${index}><strong>${template(user)}:</strong> ${message}</p>`
         } else {
-          return isCompletionMode ?
-            html`<span key=${index}>${message}</span>` :
-            html`<p key=${index}>${message}</p>`
+          return html`<p key=${index}>${message}</p>`
         }
       };
 
-      const handleCompletionEdit = (e) => {
-        session.value.prompt = e.target.innerText;
-        session.value.transcript = [];
-      }
-
       return html`
-        <div id="chat" ref=${container} key=${messages.length}>
+        <section id="chat" ref=${container}>
           <img style="width: 60%;${!session.value.image_selected ? `display: none;` : ``}" src="${session.value.image_selected}"/>
-          <span contenteditable=${isCompletionMode} ref=${container} oninput=${handleCompletionEdit}>
-            ${messages.flatMap(chatLine)}
-          </span>
-        </div>`;
+          ${messages.flatMap(chatLine)}
+        </section>`;
     };
 
     const ConfigForm = (props) => {
@@ -767,7 +744,6 @@
             ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
             ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
             ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
-            ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}
           </fieldset>
           <details>
             <summary>More options</summary>

examples/server/server.cpp

@@ -679,7 +679,6 @@ struct llama_server_context
         slot->params.n_predict        = json_value(data, "n_predict",         default_params.n_predict);
         slot->sparams.top_k           = json_value(data, "top_k",             default_sparams.top_k);
         slot->sparams.top_p           = json_value(data, "top_p",             default_sparams.top_p);
-        slot->sparams.min_p           = json_value(data, "min_p",             default_sparams.min_p);
         slot->sparams.tfs_z           = json_value(data, "tfs_z",             default_sparams.tfs_z);
         slot->sparams.typical_p       = json_value(data, "typical_p",         default_sparams.typical_p);
         slot->sparams.temp            = json_value(data, "temperature",       default_sparams.temp);
@@ -1114,7 +1113,6 @@ struct llama_server_context
             {"temp",              slot.sparams.temp},
             {"top_k",             slot.sparams.top_k},
             {"top_p",             slot.sparams.top_p},
-            {"min_p",             slot.sparams.min_p},
             {"tfs_z",             slot.sparams.tfs_z},
             {"typical_p",         slot.sparams.typical_p},
             {"repeat_last_n",     slot.sparams.penalty_last_n},
@@ -1557,35 +1555,6 @@ struct llama_server_context
 
                     slot.num_prompt_tokens = prompt_tokens.size();
 
-                    if (slot.params.n_keep < 0)
-                    {
-                        slot.params.n_keep = slot.num_prompt_tokens;
-                    }
-                    slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep);
-
-                    // if input prompt is too big, truncate it
-                    if (slot.num_prompt_tokens >= slot.n_ctx)
-                    {
-                        const int n_left = slot.n_ctx - slot.params.n_keep;
-                        const int n_block_size = n_left / 2;
-                        const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size;
-
-                        std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep);
-                        new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end());
-
-                        LOG_VERBOSE("input truncated", {
-                            {"n_ctx",  slot.n_ctx},
-                            {"n_keep", slot.params.n_keep},
-                            {"n_left", n_left},
-                            {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
-                        });
-                        slot.truncated = true;
-                        prompt_tokens = new_tokens;
-
-                        slot.num_prompt_tokens = prompt_tokens.size();
-                        GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx);
-                    }
-
                     if (!slot.params.cache_prompt)
                     {
                         llama_sampling_reset(slot.ctx_sampling);
@@ -1595,6 +1564,35 @@ struct llama_server_context
                     }
                     else
                     {
+                        if (slot.params.n_keep < 0)
+                        {
+                            slot.params.n_keep = slot.num_prompt_tokens;
+                        }
+                        slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep);
+
+                        // if input prompt is too big, truncate it
+                        if (slot.num_prompt_tokens >= slot.n_ctx)
+                        {
+                            const int n_left = slot.n_ctx - slot.params.n_keep;
+                            const int n_block_size = n_left / 2;
+                            const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size;
+
+                            std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep);
+                            new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end());
+
+                            LOG_VERBOSE("input truncated", {
+                                                            {"n_ctx",  slot.n_ctx},
+                                                            {"n_keep", slot.params.n_keep},
+                                                            {"n_left", n_left},
+                                                            {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
+                                                        });
+                            slot.truncated = true;
+                            prompt_tokens = new_tokens;
+
+                            slot.num_prompt_tokens = prompt_tokens.size();
+                            GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx);
+                        }
+
                         // push the prompt into the sampling context (do not apply grammar)
                         for (auto &token : prompt_tokens)
                         {

examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py

@@ -9,7 +9,7 @@ import numpy as np
 from pathlib import Path
 
 if 'NO_LOCAL_GGUF' not in os.environ:
-    sys.path.insert(1, str(Path(__file__).parent / '..' / '..' / 'gguf-py'))
+    sys.path.insert(1, str(Path(__file__).parent / '..' / '..' / 'gguf-py' / 'gguf'))
 import gguf
 
 # gguf constants

ggml-alloc.c

@@ -378,13 +378,9 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
     }
 }
 
-static void init_view(struct ggml_allocr * alloc, struct ggml_tensor * view, bool update_backend) {
+static void init_view(struct ggml_allocr * alloc, struct ggml_tensor * view) {
     assert(view->view_src != NULL && view->view_src->data != NULL);
-
-    if (update_backend) {
-        view->backend = view->view_src->backend;
-    }
-
+    view->backend = view->view_src->backend;
     view->buffer  = view->view_src->buffer;
     view->data    = (char *)view->view_src->data + view->view_offs;
 
@@ -398,7 +394,7 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
     struct hash_node * ht = alloc->hash_table;
     if (node->data == NULL) {
         if (ggml_is_view(node)) {
-            init_view(alloc, node, true);
+            init_view(alloc, node);
         } else {
             // see if we can reuse a parent's buffer (inplace)
             if (ggml_op_can_inplace(node->op)) {
@@ -428,14 +424,15 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
                                 AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name);
                                 node->view_src = view_src;
                                 view_src_hn->n_views += 1;
-                                init_view(alloc, node, false);
+                                init_view(alloc, node);
                                 return;
                             }
-                        } else {
+                        }
+                        else {
                             AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name);
                             node->view_src = parent;
                             p_hn->n_views += 1;
-                            init_view(alloc, node, false);
+                            init_view(alloc, node);
                             return;
                         }
                     }
@@ -466,7 +463,7 @@ size_t ggml_allocr_alloc_graph_n(
                 hash_get(ht, view_src)->n_views += 1;
                 if (node->buffer == NULL && node->data != NULL) {
                     // view of a pre-allocated tensor, didn't call init_view() yet
-                    init_view(alloc, node, true);
+                    init_view(alloc, node);
                 }
             }
 
@@ -477,7 +474,7 @@ size_t ggml_allocr_alloc_graph_n(
                 }
                 hash_get(ht, parent)->n_children += 1;
                 if (ggml_is_view(parent) && parent->buffer == NULL && parent->data != NULL) {
-                    init_view(alloc, parent, true);
+                    init_view(alloc, parent);
                 }
             }
         }

ggml-cuda.cu

@@ -5790,11 +5790,6 @@ static void ggml_cuda_pool_free(void * ptr, size_t size) {
     CUDA_CHECK(cudaFree(ptr));
 }
 
-static bool g_cublas_loaded = false;
-
-bool ggml_cublas_loaded(void) {
-    return g_cublas_loaded;
-}
 
 void ggml_init_cublas() {
     static bool initialized = false;
@@ -5808,12 +5803,7 @@ void ggml_init_cublas() {
         CUDA_CHECK(cudaDeviceSynchronize());
 #endif
 
-        if (cudaGetDeviceCount(&g_device_count) != cudaSuccess) {
-            initialized = true;
-            g_cublas_loaded = false;
-            return;
-        }
-
+        CUDA_CHECK(cudaGetDeviceCount(&g_device_count));
         GGML_ASSERT(g_device_count <= GGML_CUDA_MAX_DEVICES);
         int64_t total_vram = 0;
 #if defined(GGML_CUDA_FORCE_MMQ)
@@ -5861,7 +5851,6 @@ void ggml_init_cublas() {
         // CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr));
 
         initialized = true;
-        g_cublas_loaded = true;
     }
 }
 
@@ -7169,8 +7158,6 @@ static void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src
 }
 
 bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
-    if (!g_cublas_loaded) return false;
-
     const int64_t ne10 = src1->ne[0];
 
     const int64_t ne0 = dst->ne[0];
@@ -7427,8 +7414,6 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
         (src1->backend == GGML_BACKEND_GPU) &&
         ( dst->backend == GGML_BACKEND_GPU);
 
-    const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
-
     int64_t min_compute_capability = INT_MAX;
     for (int64_t id = 0; id < g_device_count; ++id) {
         if (min_compute_capability > g_compute_capabilities[id] && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) {
@@ -7450,13 +7435,13 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
     //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
     //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
 
-    if (!split && all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
+    if (all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
         // KQ single-batch
         ggml_cuda_mul_mat_vec_p021(src0, src1, dst);
-    } else if (!split && all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
+    } else if (all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
         // KQV single-batch
         ggml_cuda_mul_mat_vec_nc(src0, src1, dst);
-    } else if (!split && all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
+    } else if (all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
         // KQ + KQV multi-batch
         ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst);
     } else if (src0->type == GGML_TYPE_F32) {
@@ -7856,8 +7841,6 @@ void ggml_cuda_free_scratch() {
 }
 
 bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
-    if (!g_cublas_loaded) return false;
-
     ggml_cuda_func_t func;
     const bool any_on_device = tensor->backend == GGML_BACKEND_GPU
         || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT))

ggml-cuda.h

@@ -17,12 +17,7 @@ extern "C" {
 
 #define GGML_CUDA_MAX_DEVICES       16
 
-// Always success. To check if CUDA is actually loaded, use `ggml_cublas_loaded`.
 GGML_API void   ggml_init_cublas(void);
-
-// Returns `true` if there are available CUDA devices and cublas loads successfully; otherwise, it returns `false`.
-GGML_API bool   ggml_cublas_loaded(void);
-
 GGML_API void * ggml_cuda_host_malloc(size_t size);
 GGML_API void   ggml_cuda_host_free(void * ptr);
 

ggml.c

@@ -4970,13 +4970,8 @@ struct ggml_tensor * ggml_rope_back(
         int                   n_dims,
         int                   mode,
         int                   n_ctx,
-        int                   n_orig_ctx,
         float                 freq_base,
         float                 freq_scale,
-        float                 ext_factor,
-        float                 attn_factor,
-        float                 beta_fast,
-        float                 beta_slow,
         float                 xpos_base,
         bool                  xpos_down) {
     GGML_ASSERT(ggml_is_vector(b));
@@ -4993,15 +4988,11 @@ struct ggml_tensor * ggml_rope_back(
 
     struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
 
-    int32_t params[13] = { /*n_past*/ 0, n_dims, mode, n_ctx, n_orig_ctx };
-    memcpy(params +  5, &freq_base,    sizeof(float));
-    memcpy(params +  6, &freq_scale,   sizeof(float));
-    memcpy(params +  7, &ext_factor,   sizeof(float));
-    memcpy(params +  8, &attn_factor,  sizeof(float));
-    memcpy(params +  9, &beta_fast,    sizeof(float));
-    memcpy(params + 10, &beta_slow,    sizeof(float));
-    memcpy(params + 11, &xpos_base,    sizeof(float));
-    memcpy(params + 12, &xpos_down,    sizeof(bool));
+    int32_t params[8] = { /*n_past*/ 0, n_dims, mode, n_ctx };
+    memcpy(params + 4, &freq_base,  sizeof(float));
+    memcpy(params + 5, &freq_scale, sizeof(float));
+    memcpy(params + 6, &xpos_base,  sizeof(float));
+    memcpy(params + 7, &xpos_down,  sizeof(bool));
     ggml_set_op_params(result, params, sizeof(params));
 
     result->op   = GGML_OP_ROPE_BACK;
@@ -10983,8 +10974,7 @@ static void ggml_compute_forward_rope_f32(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0,
         const struct ggml_tensor * src1,
-        struct ggml_tensor * dst,
-        const bool forward) {
+        struct ggml_tensor * dst) {
     if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
         return;
     }
@@ -11043,11 +11033,6 @@ static void ggml_compute_forward_rope_f32(
     const bool is_neox = mode & 2;
     const bool is_glm  = mode & 4;
 
-    // backward process uses inverse rotation by cos and sin.
-    // cos and sin build a rotation matrix, where the inverse is the transpose.
-    // this essentially just switches the sign of sin.
-    const float sin_sign = forward ? 1.0f : -1.0f;
-
     const int32_t * pos = (const int32_t *) src1->data;
 
     for (int64_t i3 = 0; i3 < ne3; i3++) {
@@ -11064,9 +11049,9 @@ static void ggml_compute_forward_rope_f32(
                     float block_theta = MAX(p - (n_ctx - 2), 0);
                     for (int64_t i0 = 0; i0 < ne0 / 4; i0++) {
                         const float cos_theta = cosf(theta_base);
-                        const float sin_theta = sinf(theta_base) * sin_sign;
+                        const float sin_theta = sinf(theta_base);
                         const float cos_block_theta = cosf(block_theta);
-                        const float sin_block_theta = sinf(block_theta) * sin_sign;
+                        const float sin_block_theta = sinf(block_theta);
 
                         theta_base *= theta_scale;
                         block_theta *= theta_scale;
@@ -11090,7 +11075,6 @@ static void ggml_compute_forward_rope_f32(
                         rope_yarn(
                             theta_base, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta
                         );
-                        sin_theta *= sin_sign;
 
                         // zeta scaling for xPos only:
                         float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f;
@@ -11121,7 +11105,6 @@ static void ggml_compute_forward_rope_f32(
                                 theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor,
                                 &cos_theta, &sin_theta
                             );
-                            sin_theta *= sin_sign;
 
                             theta_base *= theta_scale;
 
@@ -11147,8 +11130,7 @@ static void ggml_compute_forward_rope_f16(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0,
         const struct ggml_tensor * src1,
-        struct ggml_tensor * dst,
-        const bool forward) {
+        struct ggml_tensor * dst) {
     if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
         return;
     }
@@ -11200,11 +11182,6 @@ static void ggml_compute_forward_rope_f16(
     const bool is_neox = mode & 2;
     const bool is_glm  = mode & 4;
 
-    // backward process uses inverse rotation by cos and sin.
-    // cos and sin build a rotation matrix, where the inverse is the transpose.
-    // this essentially just switches the sign of sin.
-    const float sin_sign = forward ? 1.0f : -1.0f;
-
     const int32_t * pos = (const int32_t *) src1->data;
 
     for (int64_t i3 = 0; i3 < ne3; i3++) {
@@ -11221,9 +11198,9 @@ static void ggml_compute_forward_rope_f16(
                     float block_theta = MAX(p - (n_ctx - 2), 0);
                     for (int64_t i0 = 0; i0 < ne0 / 4; i0++) {
                         const float cos_theta = cosf(theta_base);
-                        const float sin_theta = sinf(theta_base) * sin_sign;
+                        const float sin_theta = sinf(theta_base);
                         const float cos_block_theta = cosf(block_theta);
-                        const float sin_block_theta = sinf(block_theta) * sin_sign;
+                        const float sin_block_theta = sinf(block_theta);
 
                         theta_base *= theta_scale;
                         block_theta *= theta_scale;
@@ -11247,7 +11224,6 @@ static void ggml_compute_forward_rope_f16(
                         rope_yarn(
                             theta_base, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta
                         );
-                        sin_theta *= sin_sign;
 
                         theta_base *= theta_scale;
 
@@ -11274,7 +11250,6 @@ static void ggml_compute_forward_rope_f16(
                                 theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor,
                                 &cos_theta, &sin_theta
                             );
-                            sin_theta *= sin_sign;
 
                             theta_base *= theta_scale;
 
@@ -11304,11 +11279,11 @@ static void ggml_compute_forward_rope(
     switch (src0->type) {
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_rope_f16(params, src0, src1, dst, true);
+                ggml_compute_forward_rope_f16(params, src0, src1, dst);
             } break;
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_rope_f32(params, src0, src1, dst, true);
+                ggml_compute_forward_rope_f32(params, src0, src1, dst);
             } break;
         default:
             {
@@ -11319,6 +11294,216 @@ static void ggml_compute_forward_rope(
 
 // ggml_compute_forward_rope_back
 
+static void ggml_compute_forward_rope_back_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // y = rope(x, src1)
+    // dx = rope_back(dy, src1)
+    // src0 is dy, src1 contains options
+
+    float freq_base;
+    float freq_scale;
+
+    // these two only relevant for xPos RoPE:
+    float xpos_base;
+    bool xpos_down;
+
+    //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];
+    const int n_ctx  = ((int32_t *) dst->op_params)[3]; UNUSED(n_ctx);
+    memcpy(&freq_base,  (int32_t *) dst->op_params + 4, sizeof(float));
+    memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
+    memcpy(&xpos_base,  (int32_t *) dst->op_params + 6, sizeof(float));
+    memcpy(&xpos_down,  (int32_t *) dst->op_params + 7, sizeof(bool));
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
+    //printf("n_past = %d, ne2 = %d\n", n_past, ne2);
+
+    assert(nb0 == sizeof(float));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr = ggml_nrows(dst);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    // row index used to determine which thread to use
+    int ir = 0;
+
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+
+    const bool is_neox = mode & 2;
+
+    const int32_t * pos = (const int32_t *) src1->data;
+
+    for (int64_t i3 = 0; i3 < ne3; i3++) {
+        for (int64_t i2 = 0; i2 < ne2; i2++) {
+            const int64_t p = pos[i2];
+            for (int64_t i1 = 0; i1 < ne1; i1++) {
+                if (ir++ < ir0) continue;
+                if (ir   > ir1) break;
+
+                float theta_base = freq_scale * (float)p;
+
+                if (!is_neox) {
+                    for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
+                        const float cos_theta = cosf(theta_base);
+                        const float sin_theta = sinf(theta_base);
+
+                        // zeta scaling for xPos only:
+                        float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f;
+                        if (xpos_down) zeta = 1.0f / zeta;
+
+                        theta_base *= theta_scale;
+
+                        const float * const dy  = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+                              float *       dx  = (float *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+                        const float dy0 = dy[0];
+                        const float dy1 = dy[1];
+
+                        dx[0] =   dy0*cos_theta*zeta + dy1*sin_theta*zeta;
+                        dx[1] = - dy0*sin_theta*zeta + dy1*cos_theta*zeta;
+                    }
+                } else {
+                    for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
+                        for (int64_t ic = 0; ic < n_dims; ic += 2) {
+                            const float cos_theta = cosf(theta_base);
+                            const float sin_theta = sinf(theta_base);
+
+                            theta_base *= theta_scale;
+
+                            const int64_t i0 = ib*n_dims + ic/2;
+
+                            const float * const dy  = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+                                  float *       dx  = (float *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+                            const float dy0 = dy[0];
+                            const float dy1 = dy[n_dims/2];
+
+                            dx[0]        =   dy0*cos_theta + dy1*sin_theta;
+                            dx[n_dims/2] = - dy0*sin_theta + dy1*cos_theta;
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_rope_back_f16(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // y = rope(x, src1)
+    // dx = rope_back(dy, src1)
+    // src0 is dy, src1 contains options
+
+    //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];
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
+    //printf("n_past = %d, ne2 = %d\n", n_past, ne2);
+
+    assert(nb0 == sizeof(ggml_fp16_t));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr = ggml_nrows(dst);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    // row index used to determine which thread to use
+    int ir = 0;
+
+    const float theta_scale = powf(10000.0, -2.0f/n_dims);
+
+    const bool is_neox = mode & 2;
+
+    const int32_t * pos = (const int32_t *) src1->data;
+
+    for (int64_t i3 = 0; i3 < ne3; i3++) {
+        for (int64_t i2 = 0; i2 < ne2; i2++) {
+            const int64_t p = pos[i2];
+            for (int64_t i1 = 0; i1 < ne1; i1++) {
+                if (ir++ < ir0) continue;
+                if (ir   > ir1) break;
+
+                float theta_base = (float)p;
+
+                if (!is_neox) {
+                    for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
+                        const float cos_theta = cosf(theta_base);
+                        const float sin_theta = sinf(theta_base);
+
+                        theta_base *= theta_scale;
+
+                        const ggml_fp16_t * const dy  = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+                              ggml_fp16_t *       dx  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+                        const float dy0 = GGML_FP16_TO_FP32(dy[0]);
+                        const float dy1 = GGML_FP16_TO_FP32(dy[1]);
+
+                        dx[0] = GGML_FP32_TO_FP16( dy0*cos_theta + dy1*sin_theta);
+                        dx[1] = GGML_FP32_TO_FP16(-dy0*sin_theta + dy1*cos_theta);
+                    }
+                } else {
+                    for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
+                        for (int64_t ic = 0; ic < n_dims; ic += 2) {
+                            const float cos_theta = cosf(theta_base);
+                            const float sin_theta = sinf(theta_base);
+
+                            theta_base *= theta_scale;
+
+                            const int64_t i0 = ib*n_dims + ic/2;
+
+                            const ggml_fp16_t * const dy  = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+                                  ggml_fp16_t *       dx  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+                            const float dy0 = GGML_FP16_TO_FP32(dy[0]);
+                            const float dy1 = GGML_FP16_TO_FP32(dy[n_dims/2]);
+
+                            dx[0]        = GGML_FP32_TO_FP16( dy0*cos_theta + dy1*sin_theta);
+                            dx[n_dims/2] = GGML_FP32_TO_FP16(-dy0*sin_theta + dy1*cos_theta);
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
 static void ggml_compute_forward_rope_back(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0,
@@ -11327,11 +11512,11 @@ static void ggml_compute_forward_rope_back(
     switch (src0->type) {
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_rope_f16(params, src0, src1, dst, false);
+                ggml_compute_forward_rope_back_f16(params, src0, src1, dst);
             } break;
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_rope_f32(params, src0, src1, dst, false);
+                ggml_compute_forward_rope_back_f32(params, src0, src1, dst);
             } break;
         default:
             {
@@ -15374,20 +15559,17 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                 // necessary for llama
                 if (src0->grad) {
                     //const int n_past = ((int32_t *) tensor->op_params)[0];
-                    const int n_dims     = ((int32_t *) tensor->op_params)[1];
-                    const int mode       = ((int32_t *) tensor->op_params)[2];
-                    const int n_ctx      = ((int32_t *) tensor->op_params)[3];
-                    const int n_orig_ctx = ((int32_t *) tensor->op_params)[4];
-                    float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, xpos_base, xpos_down;
-
-                    memcpy(&freq_base,   (int32_t *) tensor->op_params +  5, sizeof(float));
-                    memcpy(&freq_scale,  (int32_t *) tensor->op_params +  6, sizeof(float));
-                    memcpy(&ext_factor,  (int32_t *) tensor->op_params +  7, sizeof(float));
-                    memcpy(&attn_factor, (int32_t *) tensor->op_params +  8, sizeof(float));
-                    memcpy(&beta_fast,   (int32_t *) tensor->op_params +  9, sizeof(float));
-                    memcpy(&beta_slow,   (int32_t *) tensor->op_params + 10, sizeof(float));
-                    memcpy(&xpos_base,   (int32_t *) tensor->op_params + 11, sizeof(float));
-                    memcpy(&xpos_down,   (int32_t *) tensor->op_params + 12, sizeof(bool));
+                    const int n_dims = ((int32_t *) tensor->op_params)[1];
+                    const int mode   = ((int32_t *) tensor->op_params)[2];
+                    const int n_ctx  = ((int32_t *) tensor->op_params)[3];
+                    float freq_base;
+                    float freq_scale;
+                    float xpos_base;
+                    bool  xpos_down;
+                    memcpy(&freq_base,  (int32_t *) tensor->op_params + 4, sizeof(float));
+                    memcpy(&freq_scale, (int32_t *) tensor->op_params + 5, sizeof(float));
+                    memcpy(&xpos_base,  (int32_t *) tensor->op_params + 6, sizeof(float));
+                    memcpy(&xpos_down,  (int32_t *) tensor->op_params + 7, sizeof(bool));
 
                     src0->grad = ggml_add_or_set(ctx,
                             src0->grad,
@@ -15397,13 +15579,8 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                                 n_dims,
                                 mode,
                                 n_ctx,
-                                n_orig_ctx,
                                 freq_base,
                                 freq_scale,
-                                ext_factor,
-                                attn_factor,
-                                beta_fast,
-                                beta_slow,
                                 xpos_base,
                                 xpos_down),
                             zero_table);
@@ -15413,20 +15590,17 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             {
                 if (src0->grad) {
                     //const int n_past = ((int32_t *) tensor->op_params)[0];
-                    const int n_dims     = ((int32_t *) tensor->op_params)[1];
-                    const int mode       = ((int32_t *) tensor->op_params)[2];
-                    const int n_ctx      = ((int32_t *) tensor->op_params)[3];
-                    const int n_orig_ctx = ((int32_t *) tensor->op_params)[4];
-                    float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, xpos_base, xpos_down;
-
-                    memcpy(&freq_base,   (int32_t *) tensor->op_params +  5, sizeof(float));
-                    memcpy(&freq_scale,  (int32_t *) tensor->op_params +  6, sizeof(float));
-                    memcpy(&ext_factor,  (int32_t *) tensor->op_params +  7, sizeof(float));
-                    memcpy(&attn_factor, (int32_t *) tensor->op_params +  8, sizeof(float));
-                    memcpy(&beta_fast,   (int32_t *) tensor->op_params +  9, sizeof(float));
-                    memcpy(&beta_slow,   (int32_t *) tensor->op_params + 10, sizeof(float));
-                    memcpy(&xpos_base,   (int32_t *) tensor->op_params + 11, sizeof(float));
-                    memcpy(&xpos_down,   (int32_t *) tensor->op_params + 12, sizeof(bool));
+                    const int n_dims = ((int32_t *) tensor->op_params)[1];
+                    const int mode   = ((int32_t *) tensor->op_params)[2];
+                    const int n_ctx  = ((int32_t *) tensor->op_params)[3];
+                    float freq_base;
+                    float freq_scale;
+                    float xpos_base;
+                    bool  xpos_down;
+                    memcpy(&freq_base,  (int32_t *) tensor->op_params + 4, sizeof(float));
+                    memcpy(&freq_scale, (int32_t *) tensor->op_params + 5, sizeof(float));
+                    memcpy(&xpos_base,  (int32_t *) tensor->op_params + 6, sizeof(float));
+                    memcpy(&xpos_down,  (int32_t *) tensor->op_params + 7, sizeof(bool));
 
                     src0->grad = ggml_add_or_set(ctx,
                             src0->grad,
@@ -15435,14 +15609,14 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                                 src1,
                                 n_dims,
                                 mode,
+                                0,
                                 n_ctx,
-                                n_orig_ctx,
                                 freq_base,
                                 freq_scale,
-                                ext_factor,
-                                attn_factor,
-                                beta_fast,
-                                beta_slow,
+                                0.0f,
+                                1.0f,
+                                0.0f,
+                                0.0f,
                                 xpos_base,
                                 xpos_down,
                                 false),

ggml.h

@@ -1372,13 +1372,8 @@ extern "C" {
             int                   n_dims,
             int                   mode,
             int                   n_ctx,
-            int                   n_orig_ctx,
             float                 freq_base,
             float                 freq_scale,
-            float                 ext_factor,
-            float                 attn_factor,
-            float                 beta_fast,
-            float                 beta_slow,
             float                 xpos_base,
             bool                  xpos_down);
 

gguf-py/README.md

@@ -11,16 +11,6 @@ as an example for its usage.
 pip install gguf
 ```
 
-## API Examples/Simple Tools
-
-[examples/writer.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/examples/writer.py) — Generates `example.gguf` in the current directory to demonstrate generating a GGUF file. Note that this file cannot be used as a model.
-
-[scripts/gguf-dump.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-dump.py) — Dumps a GGUF file's metadata to the console.
-
-[scripts/gguf-set-metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-set-metadata.py) — Allows changing simple metadata values in a GGUF file by key.
-
-[scripts/gguf-convert-endian.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-convert-endian.py) — Allows converting the endianness of GGUF files.
-
 ## Development
 Maintainers who participate in development of this package are advised to install it in editable mode:
 

gguf-py/examples/writer.py

@@ -1,40 +0,0 @@
-#!/usr/bin/env python3
-import sys
-from pathlib import Path
-
-import numpy as np
-
-# Necessary to load the local gguf package
-sys.path.insert(0, str(Path(__file__).parent.parent))
-
-from gguf import GGUFWriter  # noqa: E402
-
-
-# Example usage:
-def writer_example() -> None:
-    # Example usage with a file
-    gguf_writer = GGUFWriter("example.gguf", "llama")
-
-    gguf_writer.add_architecture()
-    gguf_writer.add_block_count(12)
-    gguf_writer.add_uint32("answer", 42)  # Write a 32-bit integer
-    gguf_writer.add_float32("answer_in_float", 42.0)  # Write a 32-bit float
-    gguf_writer.add_custom_alignment(64)
-
-    tensor1 = np.ones((32,), dtype=np.float32) * 100.0
-    tensor2 = np.ones((64,), dtype=np.float32) * 101.0
-    tensor3 = np.ones((96,), dtype=np.float32) * 102.0
-
-    gguf_writer.add_tensor("tensor1", tensor1)
-    gguf_writer.add_tensor("tensor2", tensor2)
-    gguf_writer.add_tensor("tensor3", tensor3)
-
-    gguf_writer.write_header_to_file()
-    gguf_writer.write_kv_data_to_file()
-    gguf_writer.write_tensors_to_file()
-
-    gguf_writer.close()
-
-
-if __name__ == '__main__':
-    writer_example()

gguf-py/gguf/__init__.py

@@ -1,5 +1 @@
-from .constants import *
-from .gguf_reader import *
-from .gguf_writer import *
-from .tensor_mapping import *
-from .vocab import *
+from .gguf import *

gguf-py/gguf/constants.py

@@ -1,470 +0,0 @@
-from __future__ import annotations
-
-import sys
-from enum import Enum, IntEnum, auto
-from typing import Any
-
-#
-# constants
-#
-
-GGUF_MAGIC             = 0x46554747  # "GGUF"
-GGUF_VERSION           = 3
-GGUF_DEFAULT_ALIGNMENT = 32
-
-#
-# metadata keys
-#
-
-
-class Keys:
-    class General:
-        ARCHITECTURE         = "general.architecture"
-        QUANTIZATION_VERSION = "general.quantization_version"
-        ALIGNMENT            = "general.alignment"
-        NAME                 = "general.name"
-        AUTHOR               = "general.author"
-        URL                  = "general.url"
-        DESCRIPTION          = "general.description"
-        LICENSE              = "general.license"
-        SOURCE_URL           = "general.source.url"
-        SOURCE_HF_REPO       = "general.source.huggingface.repository"
-        FILE_TYPE            = "general.file_type"
-
-    class LLM:
-        CONTEXT_LENGTH        = "{arch}.context_length"
-        EMBEDDING_LENGTH      = "{arch}.embedding_length"
-        BLOCK_COUNT           = "{arch}.block_count"
-        FEED_FORWARD_LENGTH   = "{arch}.feed_forward_length"
-        USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
-        TENSOR_DATA_LAYOUT    = "{arch}.tensor_data_layout"
-
-    class Attention:
-        HEAD_COUNT        = "{arch}.attention.head_count"
-        HEAD_COUNT_KV     = "{arch}.attention.head_count_kv"
-        MAX_ALIBI_BIAS    = "{arch}.attention.max_alibi_bias"
-        CLAMP_KQV         = "{arch}.attention.clamp_kqv"
-        LAYERNORM_EPS     = "{arch}.attention.layer_norm_epsilon"
-        LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
-
-    class Rope:
-        DIMENSION_COUNT      = "{arch}.rope.dimension_count"
-        FREQ_BASE            = "{arch}.rope.freq_base"
-        SCALING_TYPE         = "{arch}.rope.scaling.type"
-        SCALING_FACTOR       = "{arch}.rope.scaling.factor"
-        SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length"
-        SCALING_FINETUNED    = "{arch}.rope.scaling.finetuned"
-
-    class Tokenizer:
-        MODEL      = "tokenizer.ggml.model"
-        LIST       = "tokenizer.ggml.tokens"
-        TOKEN_TYPE = "tokenizer.ggml.token_type"
-        SCORES     = "tokenizer.ggml.scores"
-        MERGES     = "tokenizer.ggml.merges"
-        BOS_ID     = "tokenizer.ggml.bos_token_id"
-        EOS_ID     = "tokenizer.ggml.eos_token_id"
-        UNK_ID     = "tokenizer.ggml.unknown_token_id"
-        SEP_ID     = "tokenizer.ggml.seperator_token_id"
-        PAD_ID     = "tokenizer.ggml.padding_token_id"
-        ADD_BOS    = "tokenizer.ggml.add_bos_token"
-        ADD_EOS    = "tokenizer.ggml.add_eos_token"
-        HF_JSON    = "tokenizer.huggingface.json"
-        RWKV       = "tokenizer.rwkv.world"
-
-
-#
-# recommended mapping of model tensor names for storage in gguf
-#
-
-
-class MODEL_ARCH(IntEnum):
-    LLAMA     = auto()
-    FALCON    = auto()
-    BAICHUAN  = auto()
-    GPT2      = auto()
-    GPTJ      = auto()
-    GPTNEOX   = auto()
-    MPT       = auto()
-    STARCODER = auto()
-    PERSIMMON = auto()
-    REFACT    = auto()
-    BERT      = auto()
-    BLOOM     = auto()
-
-
-class MODEL_TENSOR(IntEnum):
-    TOKEN_EMBD      = auto()
-    TOKEN_EMBD_NORM = auto()
-    TOKEN_TYPES     = auto()
-    POS_EMBD        = auto()
-    OUTPUT          = auto()
-    OUTPUT_NORM     = auto()
-    ROPE_FREQS      = auto()
-    ATTN_Q          = auto()
-    ATTN_K          = auto()
-    ATTN_V          = auto()
-    ATTN_QKV        = auto()
-    ATTN_OUT        = auto()
-    ATTN_NORM       = auto()
-    ATTN_NORM_2     = auto()
-    ATTN_ROT_EMBD   = auto()
-    FFN_GATE        = auto()
-    FFN_DOWN        = auto()
-    FFN_UP          = auto()
-    FFN_NORM        = auto()
-    ATTN_Q_NORM     = auto()
-    ATTN_K_NORM     = auto()
-
-
-MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
-    MODEL_ARCH.LLAMA:          "llama",
-    MODEL_ARCH.FALCON:         "falcon",
-    MODEL_ARCH.BAICHUAN:       "baichuan",
-    MODEL_ARCH.GPT2:           "gpt2",
-    MODEL_ARCH.GPTJ:           "gptj",
-    MODEL_ARCH.GPTNEOX:        "gptneox",
-    MODEL_ARCH.MPT:            "mpt",
-    MODEL_ARCH.STARCODER:      "starcoder",
-    MODEL_ARCH.PERSIMMON:      "persimmon",
-    MODEL_ARCH.REFACT:         "refact",
-    MODEL_ARCH.BERT:           "bert",
-    MODEL_ARCH.BLOOM:          "bloom",
-}
-
-TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
-    MODEL_TENSOR.TOKEN_EMBD:      "token_embd",
-    MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm",
-    MODEL_TENSOR.TOKEN_TYPES:     "token_types",
-    MODEL_TENSOR.POS_EMBD:        "position_embd",
-    MODEL_TENSOR.OUTPUT_NORM:     "output_norm",
-    MODEL_TENSOR.OUTPUT:          "output",
-    MODEL_TENSOR.ROPE_FREQS:      "rope_freqs",
-    MODEL_TENSOR.ATTN_NORM:       "blk.{bid}.attn_norm",
-    MODEL_TENSOR.ATTN_NORM_2:     "blk.{bid}.attn_norm_2",
-    MODEL_TENSOR.ATTN_QKV:        "blk.{bid}.attn_qkv",
-    MODEL_TENSOR.ATTN_Q:          "blk.{bid}.attn_q",
-    MODEL_TENSOR.ATTN_K:          "blk.{bid}.attn_k",
-    MODEL_TENSOR.ATTN_V:          "blk.{bid}.attn_v",
-    MODEL_TENSOR.ATTN_OUT:        "blk.{bid}.attn_output",
-    MODEL_TENSOR.ATTN_ROT_EMBD:   "blk.{bid}.attn_rot_embd",
-    MODEL_TENSOR.ATTN_Q_NORM:     "blk.{bid}.attn_q_norm",
-    MODEL_TENSOR.ATTN_K_NORM:     "blk.{bid}.attn_k_norm",
-    MODEL_TENSOR.FFN_NORM:        "blk.{bid}.ffn_norm",
-    MODEL_TENSOR.FFN_GATE:        "blk.{bid}.ffn_gate",
-    MODEL_TENSOR.FFN_DOWN:        "blk.{bid}.ffn_down",
-    MODEL_TENSOR.FFN_UP:          "blk.{bid}.ffn_up",
-}
-
-MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
-    MODEL_ARCH.LLAMA: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.ROPE_FREQS,
-        MODEL_TENSOR.ATTN_NORM,
-        MODEL_TENSOR.ATTN_Q,
-        MODEL_TENSOR.ATTN_K,
-        MODEL_TENSOR.ATTN_V,
-        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.GPTNEOX: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.ATTN_NORM,
-        MODEL_TENSOR.ATTN_QKV,
-        MODEL_TENSOR.ATTN_OUT,
-        MODEL_TENSOR.FFN_NORM,
-        MODEL_TENSOR.FFN_DOWN,
-        MODEL_TENSOR.FFN_UP,
-    ],
-    MODEL_ARCH.FALCON: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.ATTN_NORM,
-        MODEL_TENSOR.ATTN_NORM_2,
-        MODEL_TENSOR.ATTN_QKV,
-        MODEL_TENSOR.ATTN_OUT,
-        MODEL_TENSOR.FFN_DOWN,
-        MODEL_TENSOR.FFN_UP,
-    ],
-    MODEL_ARCH.BAICHUAN: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.ROPE_FREQS,
-        MODEL_TENSOR.ATTN_NORM,
-        MODEL_TENSOR.ATTN_Q,
-        MODEL_TENSOR.ATTN_K,
-        MODEL_TENSOR.ATTN_V,
-        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.STARCODER: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.POS_EMBD,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.ATTN_NORM,
-        MODEL_TENSOR.ATTN_QKV,
-        MODEL_TENSOR.ATTN_OUT,
-        MODEL_TENSOR.FFN_NORM,
-        MODEL_TENSOR.FFN_DOWN,
-        MODEL_TENSOR.FFN_UP,
-    ],
-    MODEL_ARCH.BERT: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.TOKEN_TYPES,
-        MODEL_TENSOR.POS_EMBD,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.ATTN_NORM,
-        MODEL_TENSOR.ATTN_Q,
-        MODEL_TENSOR.ATTN_K,
-        MODEL_TENSOR.ATTN_V,
-        MODEL_TENSOR.ATTN_OUT,
-        MODEL_TENSOR.FFN_NORM,
-        MODEL_TENSOR.FFN_DOWN,
-        MODEL_TENSOR.FFN_UP,
-    ],
-    MODEL_ARCH.MPT: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.ATTN_NORM,
-        MODEL_TENSOR.ATTN_QKV,
-        MODEL_TENSOR.ATTN_OUT,
-        MODEL_TENSOR.FFN_NORM,
-        MODEL_TENSOR.FFN_DOWN,
-        MODEL_TENSOR.FFN_UP,
-    ],
-    MODEL_ARCH.GPTJ: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.ATTN_NORM,
-        MODEL_TENSOR.ATTN_Q,
-        MODEL_TENSOR.ATTN_K,
-        MODEL_TENSOR.ATTN_V,
-        MODEL_TENSOR.ATTN_OUT,
-        MODEL_TENSOR.FFN_DOWN,
-        MODEL_TENSOR.FFN_UP,
-    ],
-    MODEL_ARCH.PERSIMMON: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.ATTN_NORM,
-        MODEL_TENSOR.ATTN_QKV,
-        MODEL_TENSOR.ATTN_OUT,
-        MODEL_TENSOR.FFN_NORM,
-        MODEL_TENSOR.FFN_DOWN,
-        MODEL_TENSOR.FFN_UP,
-        MODEL_TENSOR.ATTN_Q_NORM,
-        MODEL_TENSOR.ATTN_K_NORM,
-        MODEL_TENSOR.ATTN_ROT_EMBD,
-    ],
-    MODEL_ARCH.REFACT: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.ATTN_NORM,
-        MODEL_TENSOR.ATTN_Q,
-        MODEL_TENSOR.ATTN_K,
-        MODEL_TENSOR.ATTN_V,
-        MODEL_TENSOR.ATTN_OUT,
-        MODEL_TENSOR.FFN_NORM,
-        MODEL_TENSOR.FFN_GATE,
-        MODEL_TENSOR.FFN_DOWN,
-        MODEL_TENSOR.FFN_UP,
-    ],
-    MODEL_ARCH.BLOOM: [
-        MODEL_TENSOR.TOKEN_EMBD,
-        MODEL_TENSOR.TOKEN_EMBD_NORM,
-        MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.OUTPUT,
-        MODEL_TENSOR.ATTN_NORM,
-        MODEL_TENSOR.ATTN_QKV,
-        MODEL_TENSOR.ATTN_OUT,
-        MODEL_TENSOR.FFN_NORM,
-        MODEL_TENSOR.FFN_DOWN,
-        MODEL_TENSOR.FFN_UP,
-    ],
-    MODEL_ARCH.GPT2: [
-        # TODO
-    ],
-    # TODO
-}
-
-# tensors that will not be serialized
-MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
-    MODEL_ARCH.LLAMA: [
-        MODEL_TENSOR.ROPE_FREQS,
-        MODEL_TENSOR.ATTN_ROT_EMBD,
-    ],
-    MODEL_ARCH.BAICHUAN: [
-        MODEL_TENSOR.ROPE_FREQS,
-        MODEL_TENSOR.ATTN_ROT_EMBD,
-    ],
-    MODEL_ARCH.PERSIMMON: [
-        MODEL_TENSOR.ROPE_FREQS,
-    ],
-}
-
-#
-# types
-#
-
-
-class TokenType(IntEnum):
-    NORMAL       = 1
-    UNKNOWN      = 2
-    CONTROL      = 3
-    USER_DEFINED = 4
-    UNUSED       = 5
-    BYTE         = 6
-
-
-class RopeScalingType(Enum):
-    NONE   = 'none'
-    LINEAR = 'linear'
-    YARN   = 'yarn'
-
-
-class GGMLQuantizationType(IntEnum):
-    F32  = 0
-    F16  = 1
-    Q4_0 = 2
-    Q4_1 = 3
-    Q5_0 = 6
-    Q5_1 = 7
-    Q8_0 = 8
-    Q8_1 = 9
-    Q2_K = 10
-    Q3_K = 11
-    Q4_K = 12
-    Q5_K = 13
-    Q6_K = 14
-    Q8_K = 15
-
-
-class GGUFEndian(IntEnum):
-    LITTLE = 0
-    BIG = 1
-
-
-class GGUFValueType(IntEnum):
-    UINT8   = 0
-    INT8    = 1
-    UINT16  = 2
-    INT16   = 3
-    UINT32  = 4
-    INT32   = 5
-    FLOAT32 = 6
-    BOOL    = 7
-    STRING  = 8
-    ARRAY   = 9
-    UINT64  = 10
-    INT64   = 11
-    FLOAT64 = 12
-
-    @staticmethod
-    def get_type(val: Any) -> GGUFValueType:
-        if isinstance(val, (str, bytes, bytearray)):
-            return GGUFValueType.STRING
-        elif isinstance(val, list):
-            return GGUFValueType.ARRAY
-        elif isinstance(val, float):
-            return GGUFValueType.FLOAT32
-        elif isinstance(val, bool):
-            return GGUFValueType.BOOL
-        elif isinstance(val, int):
-            return GGUFValueType.INT32
-        # TODO: need help with 64-bit types in Python
-        else:
-            print("Unknown type:", type(val))
-            sys.exit()
-
-
-# Note: Does not support GGML_QKK_64
-QK_K = 256
-# Items here are (block size, type size)
-GGML_QUANT_SIZES = {
-    GGMLQuantizationType.F32:  (1, 4),
-    GGMLQuantizationType.F16:  (1, 2),
-    GGMLQuantizationType.Q4_0: (32, 2 + 16),
-    GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16),
-    GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16),
-    GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16),
-    GGMLQuantizationType.Q8_0: (32, 2 + 32),
-    GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32),
-    GGMLQuantizationType.Q2_K: (256, 2 + 2 + QK_K // 16 + QK_K // 4),
-    GGMLQuantizationType.Q3_K: (256, 2 + QK_K // 4 + QK_K // 8 + 12),
-    GGMLQuantizationType.Q4_K: (256, 2 + 2 + QK_K // 2 + 12),
-    GGMLQuantizationType.Q5_K: (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12),
-    GGMLQuantizationType.Q6_K: (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16),
-    GGMLQuantizationType.Q8_K: (256, 4 + QK_K + QK_K // 8),
-}
-
-
-# Aliases for backward compatibility.
-
-# general
-KEY_GENERAL_ARCHITECTURE         = Keys.General.ARCHITECTURE
-KEY_GENERAL_QUANTIZATION_VERSION = Keys.General.QUANTIZATION_VERSION
-KEY_GENERAL_ALIGNMENT            = Keys.General.ALIGNMENT
-KEY_GENERAL_NAME                 = Keys.General.NAME
-KEY_GENERAL_AUTHOR               = Keys.General.AUTHOR
-KEY_GENERAL_URL                  = Keys.General.URL
-KEY_GENERAL_DESCRIPTION          = Keys.General.DESCRIPTION
-KEY_GENERAL_LICENSE              = Keys.General.LICENSE
-KEY_GENERAL_SOURCE_URL           = Keys.General.SOURCE_URL
-KEY_GENERAL_SOURCE_HF_REPO       = Keys.General.SOURCE_HF_REPO
-KEY_GENERAL_FILE_TYPE            = Keys.General.FILE_TYPE
-
-# LLM
-KEY_CONTEXT_LENGTH        = Keys.LLM.CONTEXT_LENGTH
-KEY_EMBEDDING_LENGTH      = Keys.LLM.EMBEDDING_LENGTH
-KEY_BLOCK_COUNT           = Keys.LLM.BLOCK_COUNT
-KEY_FEED_FORWARD_LENGTH   = Keys.LLM.FEED_FORWARD_LENGTH
-KEY_USE_PARALLEL_RESIDUAL = Keys.LLM.USE_PARALLEL_RESIDUAL
-KEY_TENSOR_DATA_LAYOUT    = Keys.LLM.TENSOR_DATA_LAYOUT
-
-# attention
-KEY_ATTENTION_HEAD_COUNT        = Keys.Attention.HEAD_COUNT
-KEY_ATTENTION_HEAD_COUNT_KV     = Keys.Attention.HEAD_COUNT_KV
-KEY_ATTENTION_MAX_ALIBI_BIAS    = Keys.Attention.MAX_ALIBI_BIAS
-KEY_ATTENTION_CLAMP_KQV         = Keys.Attention.CLAMP_KQV
-KEY_ATTENTION_LAYERNORM_EPS     = Keys.Attention.LAYERNORM_EPS
-KEY_ATTENTION_LAYERNORM_RMS_EPS = Keys.Attention.LAYERNORM_RMS_EPS
-
-# RoPE
-KEY_ROPE_DIMENSION_COUNT      = Keys.Rope.DIMENSION_COUNT
-KEY_ROPE_FREQ_BASE            = Keys.Rope.FREQ_BASE
-KEY_ROPE_SCALING_TYPE         = Keys.Rope.SCALING_TYPE
-KEY_ROPE_SCALING_FACTOR       = Keys.Rope.SCALING_FACTOR
-KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN
-KEY_ROPE_SCALING_FINETUNED    = Keys.Rope.SCALING_FINETUNED
-
-# tokenization
-KEY_TOKENIZER_MODEL      = Keys.Tokenizer.MODEL
-KEY_TOKENIZER_LIST       = Keys.Tokenizer.LIST
-KEY_TOKENIZER_TOKEN_TYPE = Keys.Tokenizer.TOKEN_TYPE
-KEY_TOKENIZER_SCORES     = Keys.Tokenizer.SCORES
-KEY_TOKENIZER_MERGES     = Keys.Tokenizer.MERGES
-KEY_TOKENIZER_BOS_ID     = Keys.Tokenizer.BOS_ID
-KEY_TOKENIZER_EOS_ID     = Keys.Tokenizer.EOS_ID
-KEY_TOKENIZER_UNK_ID     = Keys.Tokenizer.UNK_ID
-KEY_TOKENIZER_SEP_ID     = Keys.Tokenizer.SEP_ID
-KEY_TOKENIZER_PAD_ID     = Keys.Tokenizer.PAD_ID
-KEY_TOKENIZER_HF_JSON    = Keys.Tokenizer.HF_JSON
-KEY_TOKENIZER_RWKV       = Keys.Tokenizer.RWKV

gguf-py/gguf/gguf_reader.py

@@ -1,264 +0,0 @@
-#
-# GGUF file reading/modification support. For API usage information,
-# please see the files scripts/ for some fairly simple examples.
-#
-from __future__ import annotations
-
-import os
-from collections import OrderedDict
-from typing import Any, Literal, NamedTuple, TypeVar, Union
-
-import numpy as np
-import numpy.typing as npt
-
-if __name__ == "__main__":
-    import sys
-    from pathlib import Path
-
-    # Allow running file in package as a script.
-    sys.path.insert(0, str(Path(__file__).parent.parent))
-
-from gguf.constants import (
-    GGML_QUANT_SIZES,
-    GGUF_DEFAULT_ALIGNMENT,
-    GGUF_MAGIC,
-    GGUF_VERSION,
-    GGMLQuantizationType,
-    GGUFValueType,
-)
-
-
-READER_SUPPORTED_VERSIONS = [2, GGUF_VERSION]
-
-
-class ReaderField(NamedTuple):
-    # Offset to start of this field.
-    offset: int
-
-    # Name of the field (not necessarily from file data).
-    name: str
-
-    # Data parts. Some types have multiple components, such as strings
-    # that consist of a length followed by the string data.
-    parts: list[npt.NDArray[Any]] = []
-
-    # Indexes into parts that we can call the actual data. For example
-    # an array of strings will be populated with indexes to the actual
-    # string data.
-    data: list[int] = [-1]
-
-    types: list[GGUFValueType] = []
-
-
-class ReaderTensor(NamedTuple):
-    name: str
-    tensor_type: GGMLQuantizationType
-    shape: npt.NDArray[np.uint32]
-    n_elements: int
-    n_bytes: int
-    data_offset: int
-    data: npt.NDArray[Any]
-    field: ReaderField
-
-
-class GGUFReader:
-    # I - same as host, S - swapped
-    byte_order: Literal['I' | 'S'] = 'I'
-    alignment: int = GGUF_DEFAULT_ALIGNMENT
-
-    # Note: Internal helper, API may change.
-    gguf_scalar_to_np: dict[GGUFValueType, type[np.generic]] = {
-        GGUFValueType.UINT8:   np.uint8,
-        GGUFValueType.INT8:    np.int8,
-        GGUFValueType.UINT16:  np.uint16,
-        GGUFValueType.INT16:   np.int16,
-        GGUFValueType.UINT32:  np.uint32,
-        GGUFValueType.INT32:   np.int32,
-        GGUFValueType.FLOAT32: np.float32,
-        GGUFValueType.UINT64:  np.uint64,
-        GGUFValueType.INT64:   np.int64,
-        GGUFValueType.FLOAT64: np.float64,
-        GGUFValueType.BOOL:    np.bool_,
-    }
-
-    def __init__(self, path: os.PathLike[str] | str, mode: Literal['r' | 'r+' | 'c'] = 'r'):
-        self.data = np.memmap(path, mode = mode)
-        offs = 0
-        if self._get(offs, np.uint32, override_order = '<')[0] != GGUF_MAGIC:
-            raise ValueError('GGUF magic invalid')
-        offs += 4
-        temp_version = self._get(offs, np.uint32)
-        if temp_version[0] & 65535 == 0:
-            # If we get 0 here that means it's (probably) a GGUF file created for
-            # the opposite byte order of the machine this script is running on.
-            self.byte_order = 'S'
-            temp_version = temp_version.newbyteorder(self.byte_order)
-        version = temp_version[0]
-        if version not in READER_SUPPORTED_VERSIONS:
-            raise ValueError(f'Sorry, file appears to be version {version} which we cannot handle')
-        self.fields: OrderedDict[str, ReaderField] = OrderedDict()
-        self.tensors: list[ReaderTensor] = []
-        offs += self._push_field(ReaderField(offs, 'GGUF.version', [temp_version], [0], [GGUFValueType.UINT32]))
-        temp_counts = self._get(offs, np.uint64, 2)
-        offs += self._push_field(ReaderField(offs, 'GGUF.tensor_count', [temp_counts[:1]], [0], [GGUFValueType.UINT64]))
-        offs += self._push_field(ReaderField(offs, 'GGUF.kv_count', [temp_counts[1:]], [0], [GGUFValueType.UINT64]))
-        tensor_count, kv_count = temp_counts
-        offs = self._build_fields(offs, kv_count)
-        offs, tensors_fields = self._build_tensors_fields(offs, tensor_count)
-        new_align = self.fields.get('general.alignment')
-        if new_align is not None:
-            if new_align.types != [GGUFValueType.UINT64]:
-                raise ValueError('Bad type for general.alignment field')
-            self.alignment = new_align.parts[-1][0]
-        padding = offs % self.alignment
-        if padding != 0:
-            offs += self.alignment - padding
-        self._build_tensors(offs, tensors_fields)
-
-    _DT = TypeVar('_DT', bound = npt.DTypeLike)
-
-    # Fetch a key/value metadata field by key.
-    def get_field(self, key: str) -> Union[ReaderField, None]:
-        return self.fields.get(key, None)
-
-    # Fetch a tensor from the list by index.
-    def get_tensor(self, idx: int) -> ReaderTensor:
-        return self.tensors[idx]
-
-    def _get(
-        self, offset: int, dtype: npt.DTypeLike, count: int = 1, override_order: None | Literal['I' | 'S' | '<'] = None,
-    ) -> npt.NDArray[Any]:
-        count = int(count)
-        itemsize = int(np.empty([], dtype = dtype).itemsize)
-        end_offs = offset + itemsize * count
-        return (
-            self.data[offset:end_offs]
-            .view(dtype = dtype)[:count]
-            .newbyteorder(override_order or self.byte_order)
-        )
-
-    def _push_field(self, field: ReaderField, skip_sum: bool = False) -> int:
-        if field.name in self.fields:
-            raise KeyError(f'Duplicate {field.name} already in list at offset {field.offset}')
-        self.fields[field.name] = field
-        return 0 if skip_sum else sum(int(part.nbytes) for part in field.parts)
-
-    def _get_str(self, offset: int) -> tuple[npt.NDArray[np.uint64], npt.NDArray[np.uint8]]:
-        slen = self._get(offset, np.uint64)
-        return slen, self._get(offset + 8, np.uint8, slen[0])
-
-    def _get_field_parts(
-        self, orig_offs: int, raw_type: int,
-    ) -> tuple[int, list[npt.NDArray[Any]], list[int], list[GGUFValueType]]:
-        offs = orig_offs
-        types: list[GGUFValueType] = []
-        gtype = GGUFValueType(raw_type)
-        types.append(gtype)
-        # Handle strings.
-        if gtype == GGUFValueType.STRING:
-            sparts: list[npt.NDArray[Any]] = list(self._get_str(offs))
-            size = sum(int(part.nbytes) for part in sparts)
-            return size, sparts, [1], types
-        # Check if it's a simple scalar type.
-        nptype = self.gguf_scalar_to_np.get(gtype)
-        if nptype is not None:
-            val = self._get(offs, nptype)
-            return int(val.nbytes), [val], [0], types
-        # Handle arrays.
-        if gtype == GGUFValueType.ARRAY:
-            raw_itype = self._get(offs, np.uint32)
-            offs += int(raw_itype.nbytes)
-            alen = self._get(offs, np.uint64)
-            offs += int(alen.nbytes)
-            aparts: list[npt.NDArray[Any]] = [raw_itype, alen]
-            data_idxs: list[int] = []
-            for idx in range(alen[0]):
-                curr_size, curr_parts, curr_idxs, curr_types = self._get_field_parts(offs, raw_itype[0])
-                if idx == 0:
-                    types += curr_types
-                idxs_offs = len(aparts)
-                aparts += curr_parts
-                data_idxs += (idx + idxs_offs for idx in curr_idxs)
-                offs += curr_size
-            return offs - orig_offs, aparts, data_idxs, types
-        # We can't deal with this one.
-        raise ValueError('Unknown/unhandled field type {gtype}')
-
-    def _get_tensor(self, orig_offs: int) -> ReaderField:
-        offs = orig_offs
-        name_len, name_data = self._get_str(offs)
-        offs += int(name_len.nbytes + name_data.nbytes)
-        n_dims = self._get(offs, np.uint32)
-        offs += int(n_dims.nbytes)
-        dims = self._get(offs, np.uint64, n_dims[0])
-        offs += int(dims.nbytes)
-        raw_dtype = self._get(offs, np.uint32)
-        offs += int(raw_dtype.nbytes)
-        offset_tensor = self._get(offs, np.uint64)
-        offs += int(offset_tensor.nbytes)
-        return ReaderField(
-            orig_offs,
-            str(bytes(name_data), encoding = 'utf-8'),
-            [name_len, name_data, n_dims, dims, raw_dtype, offset_tensor],
-            [1, 3, 4, 5],
-        )
-
-    def _build_fields(self, offs: int, count: int) -> int:
-        for _ in range(count):
-            orig_offs = offs
-            kv_klen, kv_kdata = self._get_str(offs)
-            offs += int(kv_klen.nbytes + kv_kdata.nbytes)
-            raw_kv_type = self._get(offs, np.uint32)
-            offs += int(raw_kv_type.nbytes)
-            parts: list[npt.NDArray[Any]] = [kv_klen, kv_kdata, raw_kv_type]
-            idxs_offs = len(parts)
-            field_size, field_parts, field_idxs, field_types = self._get_field_parts(offs, raw_kv_type[0])
-            parts += field_parts
-            self._push_field(ReaderField(
-                orig_offs,
-                str(bytes(kv_kdata), encoding = 'utf-8'),
-                parts,
-                [idx + idxs_offs for idx in field_idxs],
-                field_types,
-            ), skip_sum = True)
-            offs += field_size
-        return offs
-
-    def _build_tensors_fields(self, offs: int, count: int) -> tuple[int, list[ReaderField]]:
-        tensor_fields = []
-        for _ in range(count):
-            field = self._get_tensor(offs)
-            offs += sum(int(part.nbytes) for part in field.parts)
-            tensor_fields.append(field)
-        return offs, tensor_fields
-
-    def _build_tensors(self, start_offs: int, fields: list[ReaderField]) -> None:
-        tensors = []
-        for field in fields:
-            _name_len, name_data, _n_dims, dims, raw_dtype, offset_tensor = field.parts
-            ggml_type = GGMLQuantizationType(raw_dtype[0])
-            n_elems = np.prod(dims)
-            block_size, type_size = GGML_QUANT_SIZES[ggml_type]
-            n_bytes = n_elems * type_size // block_size
-            data_offs = int(start_offs + offset_tensor[0])
-            item_type: npt.DTypeLike
-            if ggml_type == GGMLQuantizationType.F32:
-                item_count = n_elems
-                item_type = np.float32
-            elif ggml_type == GGMLQuantizationType.F16:
-                item_count = n_elems
-                item_type = np.float16
-            else:
-                item_count = n_bytes
-                item_type = np.uint8
-            tensors.append(ReaderTensor(
-                name = str(bytes(name_data), encoding = 'utf-8'),
-                tensor_type = ggml_type,
-                shape = dims,
-                n_elements = n_elems,
-                n_bytes = n_bytes,
-                data_offset = data_offs,
-                data = self._get(data_offs, item_type, item_count),
-                field = field,
-            ))
-        self.tensors = tensors

gguf-py/gguf/gguf_writer.py

@@ -1,409 +0,0 @@
-from __future__ import annotations
-
-import os
-import shutil
-import struct
-import tempfile
-from enum import Enum, auto
-from io import BufferedWriter
-from typing import IO, Any, Sequence
-
-import numpy as np
-
-from .constants import (
-    GGUF_DEFAULT_ALIGNMENT,
-    GGUF_MAGIC,
-    GGUF_VERSION,
-    GGMLQuantizationType,
-    GGUFEndian,
-    GGUFValueType,
-    Keys,
-    RopeScalingType,
-    TokenType,
-)
-
-
-class WriterState(Enum):
-    EMPTY   = auto()
-    HEADER  = auto()
-    KV_DATA = auto()
-    TI_DATA = auto()
-
-
-class GGUFWriter:
-    fout: BufferedWriter
-    temp_file: tempfile.SpooledTemporaryFile[bytes] | None
-    tensors: list[np.ndarray[Any, Any]]
-    _simple_value_packing = {
-        GGUFValueType.UINT8:   "B",
-        GGUFValueType.INT8:    "b",
-        GGUFValueType.UINT16:  "H",
-        GGUFValueType.INT16:   "h",
-        GGUFValueType.UINT32:  "I",
-        GGUFValueType.INT32:   "i",
-        GGUFValueType.FLOAT32: "f",
-        GGUFValueType.UINT64:  "Q",
-        GGUFValueType.INT64:   "q",
-        GGUFValueType.FLOAT64: "d",
-        GGUFValueType.BOOL:    "?",
-    }
-
-    def __init__(
-        self, path: os.PathLike[str] | str, arch: str, use_temp_file: bool = True,
-        endianess: GGUFEndian = GGUFEndian.LITTLE,
-    ):
-        self.fout = open(path, "wb")
-        self.arch = arch
-        self.endianess = endianess
-        self.offset_tensor = 0
-        self.data_alignment = GGUF_DEFAULT_ALIGNMENT
-        self.kv_data = b""
-        self.kv_data_count = 0
-        self.ti_data = b""
-        self.ti_data_count = 0
-        self.use_temp_file = use_temp_file
-        self.temp_file = None
-        self.tensors = []
-        print("gguf: This GGUF file is for {0} Endian only".format(
-            "Big" if self.endianess == GGUFEndian.BIG else "Little",
-        ))
-        self.state = WriterState.EMPTY
-
-        self.add_architecture()
-
-    def write_header_to_file(self) -> None:
-        if self.state is not WriterState.EMPTY:
-            raise ValueError(f'Expected output file to be empty, got {self.state}')
-
-        self._write_packed("<I", GGUF_MAGIC, skip_pack_prefix = True)
-        self._write_packed("I", GGUF_VERSION)
-        self._write_packed("Q", self.ti_data_count)
-        self._write_packed("Q", self.kv_data_count)
-        self.flush()
-        self.state = WriterState.HEADER
-
-    def write_kv_data_to_file(self) -> None:
-        if self.state is not WriterState.HEADER:
-            raise ValueError(f'Expected output file to contain the header, got {self.state}')
-
-        self.fout.write(self.kv_data)
-        self.flush()
-        self.state = WriterState.KV_DATA
-
-    def write_ti_data_to_file(self) -> None:
-        if self.state is not WriterState.KV_DATA:
-            raise ValueError(f'Expected output file to contain KV data, got {self.state}')
-
-        self.fout.write(self.ti_data)
-        self.flush()
-        self.state = WriterState.TI_DATA
-
-    def add_key(self, key: str) -> None:
-        self.add_val(key, GGUFValueType.STRING, add_vtype=False)
-
-    def add_uint8(self, key: str, val: int) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.UINT8)
-
-    def add_int8(self, key: str, val: int) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.INT8)
-
-    def add_uint16(self, key: str, val: int) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.UINT16)
-
-    def add_int16(self, key: str, val: int) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.INT16)
-
-    def add_uint32(self, key: str, val: int) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.UINT32)
-
-    def add_int32(self, key: str, val: int) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.INT32)
-
-    def add_float32(self, key: str, val: float) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.FLOAT32)
-
-    def add_uint64(self, key: str, val: int) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.UINT64)
-
-    def add_int64(self, key: str, val: int) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.INT64)
-
-    def add_float64(self, key: str, val: float) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.FLOAT64)
-
-    def add_bool(self, key: str, val: bool) -> None:
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.BOOL)
-
-    def add_string(self, key: str, val: str) -> None:
-        if not val:
-            return
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.STRING)
-
-    def add_array(self, key: str, val: Sequence[Any]) -> None:
-        if not isinstance(val, Sequence):
-            raise ValueError("Value must be a sequence for array type")
-
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.ARRAY)
-
-    def add_val(self, val: Any, vtype: GGUFValueType | None = None, add_vtype: bool = True) -> None:
-        if vtype is None:
-            vtype = GGUFValueType.get_type(val)
-
-        if add_vtype:
-            self.kv_data += self._pack("I", vtype)
-            self.kv_data_count += 1
-
-        pack_fmt = self._simple_value_packing.get(vtype)
-        if pack_fmt is not None:
-            self.kv_data += self._pack(pack_fmt, val, skip_pack_prefix = vtype == GGUFValueType.BOOL)
-        elif vtype == GGUFValueType.STRING:
-            encoded_val = val.encode("utf8") if isinstance(val, str) else val
-            self.kv_data += self._pack("Q", len(encoded_val))
-            self.kv_data += encoded_val
-        elif vtype == GGUFValueType.ARRAY and isinstance(val, Sequence) and val:
-            ltype = GGUFValueType.get_type(val[0])
-            if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]):
-                raise ValueError("All items in a GGUF array should be of the same type")
-            self.kv_data += self._pack("I", ltype)
-            self.kv_data += self._pack("Q", len(val))
-            for item in val:
-                self.add_val(item, add_vtype=False)
-        else:
-            raise ValueError("Invalid GGUF metadata value type or value")
-
-    @staticmethod
-    def ggml_pad(x: int, n: int) -> int:
-        return ((x + n - 1) // n) * n
-
-    def add_tensor_info(
-        self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype[np.float16] | np.dtype[np.float32],
-        tensor_nbytes: int, raw_dtype: GGMLQuantizationType | None = None,
-    ) -> None:
-        if self.state is not WriterState.EMPTY:
-            raise ValueError(f'Expected output file to be empty, got {self.state}')
-
-        if raw_dtype is None and tensor_dtype not in (np.float32, np.float16):
-            raise ValueError("Only F32 and F16 tensors are supported for now")
-
-        encoded_name = name.encode("utf8")
-        self.ti_data += self._pack("Q", len(encoded_name))
-        self.ti_data += encoded_name
-        n_dims = len(tensor_shape)
-        self.ti_data += self._pack("I", n_dims)
-        for i in range(n_dims):
-            self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i])
-        if raw_dtype is None:
-            dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
-        else:
-            dtype = raw_dtype
-        self.ti_data += self._pack("I", dtype)
-        self.ti_data += self._pack("Q", self.offset_tensor)
-        self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment)
-        self.ti_data_count += 1
-
-    def add_tensor(
-        self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None,
-        raw_dtype: GGMLQuantizationType | None = None,
-    ) -> None:
-        if self.endianess == GGUFEndian.BIG:
-            tensor.byteswap(inplace=True)
-        if self.use_temp_file and self.temp_file is None:
-            fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024)
-            fp.seek(0)
-            self.temp_file = fp
-
-        shape: Sequence[int] = raw_shape if raw_shape is not None else tensor.shape
-        self.add_tensor_info(name, shape, tensor.dtype, tensor.nbytes, raw_dtype = raw_dtype)
-
-        if self.temp_file is None:
-            self.tensors.append(tensor)
-            return
-
-        tensor.tofile(self.temp_file)
-        self.write_padding(self.temp_file, tensor.nbytes)
-
-    def write_padding(self, fp: IO[bytes], n: int, align: int | None = None) -> None:
-        pad = GGUFWriter.ggml_pad(n, align if align is not None else self.data_alignment) - n
-        if pad != 0:
-            fp.write(bytes([0] * pad))
-
-    def write_tensor_data(self, tensor: np.ndarray[Any, Any]) -> None:
-        if self.state is not WriterState.TI_DATA:
-            raise ValueError(f'Expected output file to contain tensor info, got {self.state}')
-
-        if self.endianess == GGUFEndian.BIG:
-            tensor.byteswap(inplace=True)
-        self.write_padding(self.fout, self.fout.tell())
-        tensor.tofile(self.fout)
-        self.write_padding(self.fout, tensor.nbytes)
-
-    def write_tensors_to_file(self) -> None:
-        self.write_ti_data_to_file()
-
-        self.write_padding(self.fout, self.fout.tell())
-
-        if self.temp_file is None:
-            while True:
-                try:
-                    tensor = self.tensors.pop(0)
-                except IndexError:
-                    break
-                tensor.tofile(self.fout)
-                self.write_padding(self.fout, tensor.nbytes)
-            return
-
-        self.temp_file.seek(0)
-
-        shutil.copyfileobj(self.temp_file, self.fout)
-        self.flush()
-        self.temp_file.close()
-
-    def flush(self) -> None:
-        self.fout.flush()
-
-    def close(self) -> None:
-        self.fout.close()
-
-    def add_architecture(self) -> None:
-        self.add_string(Keys.General.ARCHITECTURE, self.arch)
-
-    def add_author(self, author: str) -> None:
-        self.add_string(Keys.General.AUTHOR, author)
-
-    def add_tensor_data_layout(self, layout: str) -> None:
-        self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
-
-    def add_url(self, url: str) -> None:
-        self.add_string(Keys.General.URL, url)
-
-    def add_description(self, description: str) -> None:
-        self.add_string(Keys.General.DESCRIPTION, description)
-
-    def add_source_url(self, url: str) -> None:
-        self.add_string(Keys.General.SOURCE_URL, url)
-
-    def add_source_hf_repo(self, repo: str) -> None:
-        self.add_string(Keys.General.SOURCE_HF_REPO, repo)
-
-    def add_file_type(self, ftype: int) -> None:
-        self.add_uint32(Keys.General.FILE_TYPE, ftype)
-
-    def add_name(self, name: str) -> None:
-        self.add_string(Keys.General.NAME, name)
-
-    def add_quantization_version(self, quantization_version: GGMLQuantizationType) -> None:
-        self.add_uint32(
-            Keys.General.QUANTIZATION_VERSION, quantization_version)
-
-    def add_custom_alignment(self, alignment: int) -> None:
-        self.data_alignment = alignment
-        self.add_uint32(Keys.General.ALIGNMENT, alignment)
-
-    def add_context_length(self, length: int) -> None:
-        self.add_uint32(Keys.LLM.CONTEXT_LENGTH.format(arch=self.arch), length)
-
-    def add_embedding_length(self, length: int) -> None:
-        self.add_uint32(Keys.LLM.EMBEDDING_LENGTH.format(arch=self.arch), length)
-
-    def add_block_count(self, length: int) -> None:
-        self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length)
-
-    def add_feed_forward_length(self, length: int) -> None:
-        self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length)
-
-    def add_parallel_residual(self, use: bool) -> None:
-        self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
-
-    def add_head_count(self, count: int) -> None:
-        self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count)
-
-    def add_head_count_kv(self, count: int) -> None:
-        self.add_uint32(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count)
-
-    def add_max_alibi_bias(self, bias: float) -> None:
-        self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias)
-
-    def add_clamp_kqv(self, value: float) -> None:
-        self.add_float32(Keys.Attention.CLAMP_KQV.format(arch=self.arch), value)
-
-    def add_layer_norm_eps(self, value: float) -> None:
-        self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value)
-
-    def add_layer_norm_rms_eps(self, value: float) -> None:
-        self.add_float32(Keys.Attention.LAYERNORM_RMS_EPS.format(arch=self.arch), value)
-
-    def add_rope_dimension_count(self, count: int) -> None:
-        self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count)
-
-    def add_rope_freq_base(self, value: float) -> None:
-        self.add_float32(Keys.Rope.FREQ_BASE.format(arch=self.arch), value)
-
-    def add_rope_scaling_type(self, value: RopeScalingType) -> None:
-        self.add_string(Keys.Rope.SCALING_TYPE.format(arch=self.arch), value.value)
-
-    def add_rope_scaling_factor(self, value: float) -> None:
-        self.add_float32(Keys.Rope.SCALING_FACTOR.format(arch=self.arch), value)
-
-    def add_rope_scaling_orig_ctx_len(self, value: int) -> None:
-        self.add_uint32(Keys.Rope.SCALING_ORIG_CTX_LEN.format(arch=self.arch), value)
-
-    def add_rope_scaling_finetuned(self, value: bool) -> None:
-        self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value)
-
-    def add_tokenizer_model(self, model: str) -> None:
-        self.add_string(Keys.Tokenizer.MODEL, model)
-
-    def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None:
-        self.add_array(Keys.Tokenizer.LIST, tokens)
-
-    def add_token_merges(self, merges: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None:
-        self.add_array(Keys.Tokenizer.MERGES, merges)
-
-    def add_token_types(self, types: Sequence[TokenType] | Sequence[int]) -> None:
-        self.add_array(Keys.Tokenizer.TOKEN_TYPE, types)
-
-    def add_token_scores(self, scores: Sequence[float]) -> None:
-        self.add_array(Keys.Tokenizer.SCORES, scores)
-
-    def add_bos_token_id(self, id: int) -> None:
-        self.add_uint32(Keys.Tokenizer.BOS_ID, id)
-
-    def add_eos_token_id(self, id: int) -> None:
-        self.add_uint32(Keys.Tokenizer.EOS_ID, id)
-
-    def add_unk_token_id(self, id: int) -> None:
-        self.add_uint32(Keys.Tokenizer.UNK_ID, id)
-
-    def add_sep_token_id(self, id: int) -> None:
-        self.add_uint32(Keys.Tokenizer.SEP_ID, id)
-
-    def add_pad_token_id(self, id: int) -> None:
-        self.add_uint32(Keys.Tokenizer.PAD_ID, id)
-
-    def add_add_bos_token(self, value: bool) -> None:
-        self.add_bool(Keys.Tokenizer.ADD_BOS, value)
-
-    def add_add_eos_token(self, value: bool) -> None:
-        self.add_bool(Keys.Tokenizer.ADD_EOS, value)
-
-    def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes:
-        pack_prefix = ''
-        if not skip_pack_prefix:
-            pack_prefix = '<' if self.endianess == GGUFEndian.LITTLE else '>'
-        return struct.pack(f'{pack_prefix}{fmt}', value)
-
-    def _write_packed(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> None:
-        self.fout.write(self._pack(fmt, value, skip_pack_prefix))

gguf-py/gguf/tensor_mapping.py

@@ -1,257 +0,0 @@
-from __future__ import annotations
-
-from typing import Sequence
-
-from .constants import MODEL_ARCH, MODEL_TENSOR, MODEL_TENSORS, TENSOR_NAMES
-
-
-class TensorNameMap:
-    mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
-        # Token embeddings
-        MODEL_TENSOR.TOKEN_EMBD: (
-            "gpt_neox.embed_in",                         # gptneox
-            "transformer.wte",                           # gpt2 gpt-j mpt refact
-            "transformer.word_embeddings",               # falcon
-            "word_embeddings",                           # bloom
-            "model.embed_tokens",                        # llama-hf
-            "tok_embeddings",                            # llama-pth
-            "embeddings.word_embeddings",                # bert
-            "language_model.embedding.word_embeddings",  # persimmon
-        ),
-
-        # Token type embeddings
-        MODEL_TENSOR.TOKEN_TYPES: (
-            "embeddings.token_type_embeddings",  # bert
-        ),
-
-        # Normalization of token embeddings
-        MODEL_TENSOR.TOKEN_EMBD_NORM: (
-            "word_embeddings_layernorm",  # bloom
-        ),
-
-        # Position embeddings
-        MODEL_TENSOR.POS_EMBD: (
-            "transformer.wpe",                 # gpt2
-            "embeddings.position_embeddings",  # bert
-        ),
-
-        # Output
-        MODEL_TENSOR.OUTPUT: (
-            "embed_out",                 # gptneox
-            "lm_head",                   # gpt2 mpt falcon llama-hf baichuan
-            "output",                    # llama-pth bloom
-            "word_embeddings_for_head",  # persimmon
-        ),
-
-        # Output norm
-        MODEL_TENSOR.OUTPUT_NORM: (
-            "gpt_neox.final_layer_norm",               # gptneox
-            "transformer.ln_f",                        # gpt2 gpt-j falcon
-            "model.norm",                              # llama-hf baichuan
-            "norm",                                    # llama-pth
-            "embeddings.LayerNorm",                    # bert
-            "transformer.norm_f",                      # mpt
-            "ln_f",                                    # refact bloom
-            "language_model.encoder.final_layernorm",  # persimmon
-        ),
-
-        # Rope frequencies
-        MODEL_TENSOR.ROPE_FREQS: (
-            "rope.freqs",  # llama-pth
-        ),
-    }
-
-    block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
-        # Attention norm
-        MODEL_TENSOR.ATTN_NORM: (
-            "gpt_neox.layers.{bid}.input_layernorm",                # gptneox
-            "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
-            "transformer.h.{bid}.ln_mlp",                           # falcon40b
-            "model.layers.{bid}.input_layernorm",                   # llama-hf
-            "layers.{bid}.attention_norm",                          # llama-pth
-            "encoder.layer.{bid}.attention.output.LayerNorm",       # bert
-            "language_model.encoder.layers.{bid}.input_layernorm",  # persimmon
-            "model.layers.{bid}.ln1",                               # yi
-        ),
-
-        # Attention norm 2
-        MODEL_TENSOR.ATTN_NORM_2: (
-            "transformer.h.{bid}.ln_attn",  # falcon40b
-        ),
-
-        # Attention query-key-value
-        MODEL_TENSOR.ATTN_QKV: (
-            "gpt_neox.layers.{bid}.attention.query_key_value",                     # gptneox
-            "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
-            "language_model.encoder.layers.{bid}.self_attention.query_key_value",  # persimmon
-        ),
-
-        # Attention query
-        MODEL_TENSOR.ATTN_Q: (
-            "model.layers.{bid}.self_attn.q_proj",       # llama-hf
-            "layers.{bid}.attention.wq",                 # llama-pth
-            "encoder.layer.{bid}.attention.self.query",  # bert
-            "transformer.h.{bid}.attn.q_proj",           # gpt-j
-        ),
-
-        # Attention key
-        MODEL_TENSOR.ATTN_K: (
-            "model.layers.{bid}.self_attn.k_proj",     # llama-hf
-            "layers.{bid}.attention.wk",               # llama-pth
-            "encoder.layer.{bid}.attention.self.key",  # bert
-            "transformer.h.{bid}.attn.k_proj",         # gpt-j
-        ),
-
-        # Attention value
-        MODEL_TENSOR.ATTN_V: (
-            "model.layers.{bid}.self_attn.v_proj",       # llama-hf
-            "layers.{bid}.attention.wv",                 # llama-pth
-            "encoder.layer.{bid}.attention.self.value",  # bert
-            "transformer.h.{bid}.attn.v_proj",           # gpt-j
-        ),
-
-        # Attention output
-        MODEL_TENSOR.ATTN_OUT: (
-            "gpt_neox.layers.{bid}.attention.dense",                     # gptneox
-            "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
-            "model.layers.{bid}.self_attn.o_proj",                       # llama-hf
-            "layers.{bid}.attention.wo",                                 # llama-pth
-            "encoder.layer.{bid}.attention.output.dense",                # bert
-            "transformer.h.{bid}.attn.out_proj",                         # gpt-j
-            "language_model.encoder.layers.{bid}.self_attention.dense",  # persimmon
-        ),
-
-        # Rotary embeddings
-        MODEL_TENSOR.ATTN_ROT_EMBD: (
-            "model.layers.{bid}.self_attn.rotary_emb.inv_freq",   # llama-hf
-            "layers.{bid}.attention.inner_attention.rope.freqs",  # llama-pth
-        ),
-
-        # Feed-forward norm
-        MODEL_TENSOR.FFN_NORM: (
-            "gpt_neox.layers.{bid}.post_attention_layernorm",                # gptneox
-            "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
-            "layers.{bid}.ffn_norm",                                         # llama-pth
-            "encoder.layer.{bid}.output.LayerNorm",                          # bert
-            "language_model.encoder.layers.{bid}.post_attention_layernorm",  # persimmon
-            "model.layers.{bid}.ln2",                                        # yi
-        ),
-
-        # Feed-forward up
-        MODEL_TENSOR.FFN_UP: (
-            "gpt_neox.layers.{bid}.mlp.dense_h_to_4h",                # gptneox
-            "transformer.h.{bid}.mlp.c_fc",                           # gpt2
-            "transformer.blocks.{bid}.ffn.up_proj",                   # mpt
-            "transformer.h.{bid}.mlp.dense_h_to_4h",                  # falcon
-            "h.{bid}.mlp.dense_h_to_4h",                              # bloom
-            "model.layers.{bid}.mlp.up_proj",                         # llama-hf refact
-            "layers.{bid}.feed_forward.w3",                           # llama-pth
-            "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
-        ),
-
-        # Feed-forward gate
-        MODEL_TENSOR.FFN_GATE: (
-            "model.layers.{bid}.mlp.gate_proj",  # llama-hf refact
-            "layers.{bid}.feed_forward.w1",      # llama-pth
-        ),
-
-        # 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
-            "transformer.blocks.{bid}.ffn.down_proj",                 # mpt
-            "transformer.h.{bid}.mlp.dense_4h_to_h",                  # falcon
-            "h.{bid}.mlp.dense_4h_to_h",                              # bloom
-            "model.layers.{bid}.mlp.down_proj",                       # llama-hf
-            "layers.{bid}.feed_forward.w2",                           # llama-pth
-            "encoder.layer.{bid}.output.dense",                       # bert
-            "transformer.h.{bid}.mlp.fc_out",                         # gpt-j
-            "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h",  # persimmon
-        ),
-
-        MODEL_TENSOR.ATTN_Q_NORM: (
-            "language_model.encoder.layers.{bid}.self_attention.q_layernorm",
-        ),
-
-        MODEL_TENSOR.ATTN_K_NORM: (
-            "language_model.encoder.layers.{bid}.self_attention.k_layernorm",
-        ),
-
-        MODEL_TENSOR.ROPE_FREQS: (
-            "language_model.encoder.layers.{bid}.self_attention.rotary_emb.inv_freq",  # persimmon
-        ),
-    }
-
-    mapping: dict[str, tuple[MODEL_TENSOR, str]]
-
-    def __init__(self, arch: MODEL_ARCH, n_blocks: int):
-        self.mapping = {}
-        for tensor, keys in self.mappings_cfg.items():
-            if tensor not in MODEL_TENSORS[arch]:
-                continue
-            tensor_name = TENSOR_NAMES[tensor]
-            self.mapping[tensor_name] = (tensor, tensor_name)
-            for key in keys:
-                self.mapping[key] = (tensor, tensor_name)
-        for bid in range(n_blocks):
-            for tensor, keys in self.block_mappings_cfg.items():
-                if tensor not in MODEL_TENSORS[arch]:
-                    continue
-                tensor_name = TENSOR_NAMES[tensor].format(bid = bid)
-                self.mapping[tensor_name] = (tensor, tensor_name)
-                for key in keys:
-                    key = key.format(bid = bid)
-                    self.mapping[key] = (tensor, tensor_name)
-
-    def get_type_and_name(self, key: str, try_suffixes: Sequence[str] = ()) -> tuple[MODEL_TENSOR, str] | None:
-        result = self.mapping.get(key)
-        if result is not None:
-            return result
-        for suffix in try_suffixes:
-            if key.endswith(suffix):
-                result = self.mapping.get(key[:-len(suffix)])
-                if result is not None:
-                    return result[0], result[1] + suffix
-        return None
-
-    def get_name(self, key: str, try_suffixes: Sequence[str] = ()) -> str | None:
-        result = self.get_type_and_name(key, try_suffixes = try_suffixes)
-        if result is None:
-            return None
-        return result[1]
-
-    def get_type(self, key: str, try_suffixes: Sequence[str] = ()) -> MODEL_TENSOR | None:
-        result = self.get_type_and_name(key, try_suffixes = try_suffixes)
-        if result is None:
-            return None
-        return result[0]
-
-    def __getitem__(self, key: str) -> str:
-        try:
-            return self.mapping[key][1]
-        except KeyError:
-            raise KeyError(key)
-
-    def __contains__(self, key: str) -> bool:
-        return key in self.mapping
-
-    def __repr__(self) -> str:
-        return repr(self.mapping)
-
-
-def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> TensorNameMap:
-    return TensorNameMap(arch, n_blocks)

gguf-py/gguf/vocab.py

@@ -1,164 +0,0 @@
-from __future__ import annotations
-
-import json
-import os
-import sys
-from pathlib import Path
-from typing import Any, Callable
-
-from .gguf_writer import GGUFWriter
-
-
-class SpecialVocab:
-    merges: list[str]
-    add_special_token: dict[str, bool]
-    special_token_ids: dict[str, int]
-
-    def __init__(
-        self, path: str | os.PathLike[str], load_merges: bool = False,
-        special_token_types: tuple[str, ...] | None = None,
-        n_vocab: int | None = None,
-    ):
-        self.special_token_ids = {}
-        self.add_special_token = {}
-        self.n_vocab = n_vocab
-        self.load_merges = load_merges
-        self.merges = []
-        if special_token_types is not None:
-            self.special_token_types = special_token_types
-        else:
-            self.special_token_types = ('bos', 'eos', 'unk', 'sep', 'pad')
-        self._load(Path(path))
-
-    def __repr__(self) -> str:
-        return '<SpecialVocab with {} merges, special tokens {}, add special tokens {}>'.format(
-            len(self.merges), self.special_token_ids or "unset", self.add_special_token or "unset",
-        )
-
-    def add_to_gguf(self, gw: GGUFWriter, quiet: bool = False) -> None:
-        if self.merges:
-            if not quiet:
-                print(f'gguf: Adding {len(self.merges)} merge(s).')
-            gw.add_token_merges(self.merges)
-        elif self.load_merges:
-            print(
-                'gguf: WARNING: Adding merges requested but no merges found, output may be non-functional.',
-                file = sys.stderr,
-            )
-        for typ, tokid in self.special_token_ids.items():
-            id_handler: Callable[[int], None] | None = getattr(gw, f'add_{typ}_token_id', None)
-            if id_handler is None:
-                print(
-                    f'gguf: WARNING: No handler for special token type {typ} with id {tokid} - skipping',
-                    file = sys.stderr,
-                )
-                continue
-            if not quiet:
-                print(f'gguf: Setting special token type {typ} to {tokid}')
-            id_handler(tokid)
-        for typ, value in self.add_special_token.items():
-            add_handler: Callable[[bool], None] | None = getattr(gw, f'add_add_{typ}_token', None)
-            if add_handler is None:
-                print(
-                    f'gguf: WARNING: No handler for add_{typ}_token with value {value} - skipping',
-                    file = sys.stderr,
-                )
-                continue
-            if not quiet:
-                print(f'gguf: Setting add_{typ}_token to {value}')
-            add_handler(value)
-
-    def _load(self, path: Path) -> None:
-        self._try_load_from_tokenizer_json(path)
-        self._try_load_from_config_json(path)
-        if self.load_merges and not self.merges:
-            self._try_load_merges_txt(path)
-
-    def _try_load_merges_txt(self, path: Path) -> bool:
-        merges_file = path / 'merges.txt'
-        if not merges_file.is_file():
-            return False
-        with open(merges_file, 'r') as fp:
-            first_line = next(fp, '').strip()
-            if not first_line.startswith('#'):
-                fp.seek(0)
-                line_num = 0
-            else:
-                line_num = 1
-            merges = []
-            for line in fp:
-                line_num += 1
-                line = line.strip()
-                if not line:
-                    continue
-                parts = line.split(None, 3)
-                if len(parts) != 2:
-                    print(
-                        f'gguf: WARNING: {merges_file.name}: Line {line_num}: Entry malformed, ignoring',
-                        file = sys.stderr,
-                    )
-                    continue
-                merges.append(f'{parts[0]} {parts[1]}')
-        self.merges = merges
-        return True
-
-    def _set_special_token(self, typ: str, tid: Any) -> None:
-        if not isinstance(tid, int) or tid < 0:
-            return
-        if self.n_vocab is None or tid < self.n_vocab:
-            if typ in self.special_token_ids:
-                return
-            self.special_token_ids[typ] = tid
-            return
-        print(
-            f'gguf: WARNING: Special token type {typ}, id {tid} out of range, must be under {self.n_vocab} - skipping',
-            file = sys.stderr,
-        )
-
-    def _try_load_from_tokenizer_json(self, path: Path) -> bool:
-        tokenizer_file = path / 'tokenizer.json'
-        if not tokenizer_file.is_file():
-            return False
-        with open(tokenizer_file, encoding = 'utf-8') as f:
-            tokenizer = json.load(f)
-        if self.load_merges:
-            merges = tokenizer.get('model', {}).get('merges')
-            if isinstance(merges, list) and merges and isinstance(merges[0], str):
-                self.merges = merges
-        tokenizer_config_file = path / 'tokenizer_config.json'
-        added_tokens = tokenizer.get('added_tokens')
-        if added_tokens is None or not tokenizer_config_file.is_file():
-            return True
-        with open(tokenizer_config_file, encoding = 'utf-8') as f:
-            tokenizer_config = json.load(f)
-        for typ in self.special_token_types:
-            add_entry = tokenizer_config.get(f'add_{typ}_token')
-            if isinstance(add_entry, bool):
-                self.add_special_token[typ] = add_entry
-            entry = tokenizer_config.get(f'{typ}_token')
-            if isinstance(entry, str):
-                tc_content = entry
-            elif isinstance(entry, dict):
-                entry_content = entry.get('content')
-                if not isinstance(entry_content, str):
-                    continue
-                tc_content = entry_content
-            else:
-                continue
-            # We only need the first match here.
-            maybe_token_id = next(
-                (atok.get('id') for atok in added_tokens if atok.get('content') == tc_content),
-                None,
-            )
-            self._set_special_token(typ, maybe_token_id)
-        return True
-
-    def _try_load_from_config_json(self, path: Path) -> bool:
-        config_file = path / 'config.json'
-        if not config_file.is_file():
-            return False
-        with open(config_file, encoding = 'utf-8') as f:
-            config = json.load(f)
-        for typ in self.special_token_types:
-            self._set_special_token(typ, config.get(f'{typ}_token_id'))
-        return True

gguf-py/pyproject.toml

@@ -1,12 +1,11 @@
 [tool.poetry]
 name = "gguf"
-version = "0.5.0"
+version = "0.4.5"
 description = "Write ML models in GGUF for GGML"
 authors = ["GGML <ggml@ggml.ai>"]
 packages = [
     {include = "gguf"},
     {include = "gguf/py.typed"},
-    {include = "scripts"},
 ]
 readme = "README.md"
 homepage = "https://ggml.ai"
@@ -28,8 +27,3 @@ pytest = "^5.2"
 [build-system]
 requires = ["poetry-core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"
-
-[tool.poetry.scripts]
-gguf-convert-endian = "scripts:gguf_convert_endian_entrypoint"
-gguf-dump = "scripts:gguf_dump_entrypoint"
-gguf-set-metadata = "scripts:gguf_set_metadata_entrypoint"

gguf-py/scripts/__init__.py

@@ -1,12 +0,0 @@
-import os
-
-from importlib import import_module
-
-
-os.environ["NO_LOCAL_GGUF"] = "TRUE"
-
-gguf_convert_endian_entrypoint = import_module("scripts.gguf-convert-endian").main
-gguf_dump_entrypoint           = import_module("scripts.gguf-dump").main
-gguf_set_metadata_entrypoint   = import_module("scripts.gguf-set-metadata").main
-
-del import_module, os

gguf-py/scripts/gguf-convert-endian.py

@@ -1,113 +0,0 @@
-#!/usr/bin/env python3
-from __future__ import annotations
-
-import argparse
-import os
-import sys
-from pathlib import Path
-
-import numpy as np
-
-# Necessary to load the local gguf package
-if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
-    sys.path.insert(0, str(Path(__file__).parent.parent))
-
-import gguf
-
-
-def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None:
-    if np.uint32(1) == np.uint32(1).newbyteorder("<"):
-        # Host is little endian
-        host_endian = "little"
-        swapped_endian = "big"
-    else:
-        # Sorry PDP or other weird systems that don't use BE or LE.
-        host_endian = "big"
-        swapped_endian = "little"
-    if reader.byte_order == "S":
-        file_endian = swapped_endian
-    else:
-        file_endian = host_endian
-    if args.order == "native":
-        order = host_endian
-    print(f"* Host is {host_endian.upper()} endian, GGUF file seems to be {file_endian.upper()} endian")
-    if file_endian == order:
-        print(f"* File is already {order.upper()} endian. Nothing to do.")
-        sys.exit(0)
-    print("* Checking tensors for conversion compatibility")
-    for tensor in reader.tensors:
-        if tensor.tensor_type not in (
-            gguf.GGMLQuantizationType.F32,
-            gguf.GGMLQuantizationType.F16,
-            gguf.GGMLQuantizationType.Q8_0,
-        ):
-            raise ValueError(f"Cannot handle type {tensor.tensor_type.name} for tensor {repr(tensor.name)}")
-    print(f"* Preparing to convert from {file_endian.upper()} to {order.upper()}")
-    if args.dry_run:
-        return
-    print("\n*** Warning *** Warning *** Warning **")
-    print("* This conversion process may damage the file. Ensure you have a backup.")
-    if order != host_endian:
-        print("* Requested endian differs from host, you will not be able to load the model on this machine.")
-    print("* The file will be modified immediately, so if conversion fails or is interrupted")
-    print("* the file will be corrupted. Enter exactly YES if you are positive you want to proceed:")
-    response = input("YES, I am sure> ")
-    if response != "YES":
-        print("You didn't enter YES. Okay then, see ya!")
-        sys.exit(0)
-    print(f"\n* Converting fields ({len(reader.fields)})")
-    for idx, field in enumerate(reader.fields.values()):
-        print(f"- {idx:4}: Converting field {repr(field.name)}, part count: {len(field.parts)}")
-        for part in field.parts:
-            part.byteswap(inplace=True)
-    print(f"\n* Converting tensors ({len(reader.tensors)})")
-    for idx, tensor in enumerate(reader.tensors):
-        print(
-            f"  - {idx:4}: Converting tensor {repr(tensor.name)}, type={tensor.tensor_type.name}, "
-            f"elements={tensor.n_elements}... ",
-            end="",
-        )
-        tensor_type = tensor.tensor_type
-        for part in tensor.field.parts:
-            part.byteswap(inplace=True)
-        if tensor_type != gguf.GGMLQuantizationType.Q8_0:
-            tensor.data.byteswap(inplace=True)
-            print()
-            continue
-        # A Q8_0 block consists of a f16 delta followed by 32 int8 quants, so 34 bytes
-        block_size = 34
-        n_blocks = len(tensor.data) // block_size
-        for block_num in range(n_blocks):
-            block_offs = block_num * block_size
-            # I know I said f16, but it doesn't matter here - any simple 16 bit type works.
-            delta = tensor.data[block_offs:block_offs + 2].view(dtype=np.uint16)
-            delta.byteswap(inplace=True)
-            if block_num % 100000 == 0:
-                print(f"[{(n_blocks - block_num) // 1000}K]", end="")
-                sys.stdout.flush()
-        print()
-    print("* Completion")
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description="Convert GGUF file byte order")
-    parser.add_argument(
-        "model", type=str,
-        help="GGUF format model filename",
-    )
-    parser.add_argument(
-        "order", type=str, choices=['big', 'little', 'native'],
-        help="Requested byte order",
-    )
-    parser.add_argument(
-        "--dry-run", action="store_true",
-        help="Don't actually change anything",
-    )
-    args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"])
-    print(f'* Loading: {args.model}')
-    reader = gguf.GGUFReader(args.model, 'r' if args.dry_run else 'r+')
-    convert_byteorder(reader, args)
-
-
-if __name__ == "__main__":
-    main()

gguf-py/scripts/gguf-dump.py

@@ -1,116 +0,0 @@
-#!/usr/bin/env python3
-from __future__ import annotations
-
-import argparse
-import os
-import sys
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-
-# Necessary to load the local gguf package
-if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
-    sys.path.insert(0, str(Path(__file__).parent.parent))
-
-from gguf import GGUFReader, GGUFValueType  # noqa: E402
-
-
-def get_file_host_endian(reader: GGUFReader) -> tuple[str, str]:
-    host_endian = 'LITTLE' if np.uint32(1) == np.uint32(1).newbyteorder("<") else 'BIG'
-    if reader.byte_order == 'S':
-        file_endian = 'BIG' if host_endian == 'LITTLE' else 'LITTLE'
-    else:
-        file_endian = host_endian
-    return (host_endian, file_endian)
-
-
-# For more information about what field.parts and field.data represent,
-# please see the comments in the modify_gguf.py example.
-def dump_metadata(reader: GGUFReader, args: argparse.Namespace) -> None:
-    host_endian, file_endian = get_file_host_endian(reader)
-    print(f'* File is {file_endian} endian, script is running on a {host_endian} endian host.')
-    print(f'\n* Dumping {len(reader.fields)} key/value pair(s)')
-    for n, field in enumerate(reader.fields.values(), 1):
-        if not field.types:
-            pretty_type = 'N/A'
-        elif field.types[0] == GGUFValueType.ARRAY:
-            nest_count = len(field.types) - 1
-            pretty_type = '[' * nest_count + str(field.types[-1].name) + ']' * nest_count
-        else:
-            pretty_type = str(field.types[-1].name)
-        print(f'  {n:5}: {pretty_type:10} | {len(field.data):8} | {field.name}', end = '')
-        if len(field.types) == 1:
-            curr_type = field.types[0]
-            if curr_type == GGUFValueType.STRING:
-                print(' = {0}'.format(repr(str(bytes(field.parts[-1]), encoding='utf8')[:60])), end = '')
-            elif field.types[0] in reader.gguf_scalar_to_np:
-                print(' = {0}'.format(field.parts[-1][0]), end = '')
-        print()
-    if args.no_tensors:
-        return
-    print(f'\n* Dumping {len(reader.tensors)} tensor(s)')
-    for n, tensor in enumerate(reader.tensors, 1):
-        prettydims = ', '.join('{0:5}'.format(d) for d in list(tensor.shape) + [1] * (4 - len(tensor.shape)))
-        print(f'  {n:5}: {tensor.n_elements:10} | {prettydims} | {tensor.tensor_type.name:7} | {tensor.name}')
-
-
-def dump_metadata_json(reader: GGUFReader, args: argparse.Namespace) -> None:
-    import json
-    host_endian, file_endian = get_file_host_endian(reader)
-    metadata: dict[str, Any] = {}
-    tensors: dict[str, Any] = {}
-    result = {
-        "filename": args.model,
-        "endian": file_endian,
-        "metadata": metadata,
-        "tensors": tensors,
-    }
-    for idx, field in enumerate(reader.fields.values()):
-        curr: dict[str, Any] = {
-            "index": idx,
-            "type": field.types[0].name if field.types else 'UNKNOWN',
-            "offset": field.offset,
-        }
-        metadata[field.name] = curr
-        if field.types[:1] == [GGUFValueType.ARRAY]:
-            curr["array_types"] = [t.name for t in field.types][1:]
-            if not args.json_array:
-                continue
-            itype = field.types[-1]
-            if itype == GGUFValueType.STRING:
-                curr["value"] = [str(bytes(field.parts[idx]), encoding="utf-8") for idx in field.data]
-            else:
-                curr["value"] = [pv for idx in field.data for pv in field.parts[idx].tolist()]
-        elif field.types[0] == GGUFValueType.STRING:
-            curr["value"] = str(bytes(field.parts[-1]), encoding="utf-8")
-        else:
-            curr["value"] = field.parts[-1].tolist()[0]
-    for idx, tensor in enumerate(reader.tensors):
-        tensors[tensor.name] = {
-            "index": idx,
-            "shape": tensor.shape.tolist(),
-            "type": tensor.tensor_type.name,
-            "offset": tensor.field.offset,
-        }
-    json.dump(result, sys.stdout)
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description="Dump GGUF file metadata")
-    parser.add_argument("model",           type=str,            help="GGUF format model filename")
-    parser.add_argument("--no-tensors", action="store_true", help="Don't dump tensor metadata")
-    parser.add_argument("--json",       action="store_true", help="Produce JSON output")
-    parser.add_argument("--json-array", action="store_true", help="Include full array values in JSON output (long)")
-    args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"])
-    if not args.json:
-        print(f'* Loading: {args.model}')
-    reader = GGUFReader(args.model, 'r')
-    if args.json:
-        dump_metadata_json(reader, args)
-    else:
-        dump_metadata(reader, args)
-
-
-if __name__ == '__main__':
-    main()

gguf-py/scripts/gguf-set-metadata.py

@@ -1,90 +0,0 @@
-#!/usr/bin/env python3
-import argparse
-import os
-import sys
-from pathlib import Path
-
-# Necessary to load the local gguf package
-if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
-    sys.path.insert(0, str(Path(__file__).parent.parent))
-
-from gguf import GGUFReader  # noqa: E402
-
-
-def minimal_example(filename: str) -> None:
-    reader = GGUFReader(filename, 'r+')
-    field = reader.fields['tokenizer.ggml.bos_token_id']
-    if field is None:
-        return
-    part_index = field.data[0]
-    field.parts[part_index][0] = 2  # Set tokenizer.ggml.bos_token_id to 2
-    #
-    # So what's this field.data thing? It's helpful because field.parts contains
-    # _every_ part of the GGUF field. For example, tokenizer.ggml.bos_token_id consists
-    # of:
-    #
-    #  Part index 0: Key length (27)
-    #  Part index 1: Key data ("tokenizer.ggml.bos_token_id")
-    #  Part index 2: Field type (4, the id for GGUFValueType.UINT32)
-    #  Part index 3: Field value
-    #
-    # Note also that each part is an NDArray slice, so even a part that
-    # is only a single value like the key length will be a NDArray of
-    # the key length type (numpy.uint32).
-    #
-    # The .data attribute in the Field is a list of relevant part indexes
-    # and doesn't contain internal GGUF details like the key length part.
-    # In this case, .data will be [3] - just the part index of the
-    # field value itself.
-
-
-def set_metadata(reader: GGUFReader, args: argparse.Namespace) -> None:
-    field = reader.get_field(args.key)
-    if field is None:
-        print(f'! Field {repr(args.key)} not found', file = sys.stderr)
-        sys.exit(1)
-    # Note that field.types is a list of types. This is because the GGUF
-    # format supports arrays. For example, an array of UINT32 would
-    # look like [GGUFValueType.ARRAY, GGUFValueType.UINT32]
-    handler = reader.gguf_scalar_to_np.get(field.types[0]) if field.types else None
-    if handler is None:
-        print(
-            f'! This tool only supports changing simple values, {repr(args.key)} has unsupported type {field.types}',
-            file = sys.stderr,
-        )
-        sys.exit(1)
-    current_value = field.parts[field.data[0]][0]
-    new_value = handler(args.value)
-    print(f'* Preparing to change field {repr(args.key)} from {current_value} to {new_value}')
-    if current_value == new_value:
-        print(f'- Key {repr(args.key)} already set to requested value {current_value}')
-        sys.exit(0)
-    if args.dry_run:
-        sys.exit(0)
-    if not args.force:
-        print('*** Warning *** Warning *** Warning **')
-        print('* Changing fields in a GGUF file can make it unusable. Proceed at your own risk.')
-        print('* Enter exactly YES if you are positive you want to proceed:')
-        response = input('YES, I am sure> ')
-        if response != 'YES':
-            print("You didn't enter YES. Okay then, see ya!")
-            sys.exit(0)
-    field.parts[field.data[0]][0] = new_value
-    print('* Field changed. Successful completion.')
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description="Set a simple value in GGUF file metadata")
-    parser.add_argument("model",     type=str,            help="GGUF format model filename")
-    parser.add_argument("key",       type=str,            help="Metadata key to set")
-    parser.add_argument("value",     type=str,            help="Metadata value to set")
-    parser.add_argument("--dry-run", action="store_true", help="Don't actually change anything")
-    parser.add_argument("--force",   action="store_true", help="Change the field without confirmation")
-    args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"])
-    print(f'* Loading: {args.model}')
-    reader = GGUFReader(args.model, 'r' if args.dry_run else 'r+')
-    set_metadata(reader, args)
-
-
-if __name__ == '__main__':
-    main()

gguf-py/tests/test_gguf.py

@@ -1,7 +1,7 @@
-import gguf  # noqa: F401
+import gguf
 
 # TODO: add tests
 
 
-def test_write_gguf() -> None:
+def test_write_gguf():
     pass

llama.cpp

@@ -596,37 +596,19 @@ static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph *
 // llama helpers
 //
 
-inline void * llama_host_malloc(size_t n) {
 #ifdef GGML_USE_CUBLAS
-    if (ggml_cublas_loaded()) {
-        return ggml_cuda_host_malloc(n);
-    } else {
-        return malloc(n);
-    }
+#   define llama_host_malloc(n)  ggml_cuda_host_malloc(n)
+#   define llama_host_free(data) ggml_cuda_host_free(data)
 #elif GGML_USE_METAL
-    return ggml_metal_host_malloc(n);
+#   define llama_host_malloc(n)  ggml_metal_host_malloc(n)
+#   define llama_host_free(data) ggml_metal_host_free(data)
 #elif GGML_USE_CPU_HBM
-    return hbw_malloc(n);
+#   define llama_host_malloc(n)  hbw_malloc(n)
+#   define llama_host_free(data) if (data != NULL) hbw_free(data)
 #else
-    return malloc(n);
+#   define llama_host_malloc(n)  malloc(n)
+#   define llama_host_free(data) free(data)
 #endif
-}
-
-inline void llama_host_free(void * ptr) {
-#ifdef GGML_USE_CUBLAS
-    if (ggml_cublas_loaded()) {
-        return ggml_cuda_host_free(ptr);
-    } else {
-        return free(ptr);
-    }
-#elif GGML_USE_METAL
-    return ggml_metal_host_free(ptr);
-#elif GGML_USE_CPU_HBM
-    return hbw_free(ptr);
-#else
-    return free(ptr);
-#endif
-}
 
 #if defined(_WIN32)
 static std::string llama_format_win_err(DWORD err) {
@@ -1218,11 +1200,9 @@ struct llama_kv_cache {
         }
 
 #ifdef GGML_USE_CUBLAS
-        if (ggml_cublas_loaded()) {
-            ggml_cuda_free_data(k);
-            ggml_cuda_free_data(v);
-        }
-#endif
+        ggml_cuda_free_data(k);
+        ggml_cuda_free_data(v);
+#endif // GGML_USE_CUBLAS
     }
 };
 
@@ -1322,15 +1302,11 @@ struct llama_model {
         }
 
 #ifdef GGML_USE_CUBLAS
-        if (ggml_cublas_loaded()) {
-            for (size_t i = 0; i < tensors_by_name.size(); ++i) {
-                ggml_cuda_free_data(tensors_by_name[i].second);
-            }
-            ggml_cuda_free_scratch();
+        for (size_t i = 0; i < tensors_by_name.size(); ++i) {
+            ggml_cuda_free_data(tensors_by_name[i].second);
         }
-#endif
-
-#if defined(GGML_USE_CLBLAST)
+        ggml_cuda_free_scratch();
+#elif defined(GGML_USE_CLBLAST)
         for (size_t i = 0; i < tensors_by_name.size(); ++i) {
             ggml_cl_free_data(tensors_by_name[i].second);
         }
@@ -1442,26 +1418,23 @@ static bool llama_kv_cache_init(
     ggml_set_name(cache.v, "cache_v");
 
     (void) n_gpu_layers;
-
 #ifdef GGML_USE_CUBLAS
-    if (ggml_cublas_loaded()) {
-        size_t vram_kv_cache = 0;
+    size_t vram_kv_cache = 0;
 
-        if (n_gpu_layers > (int)n_layer + 1) {
-            ggml_cuda_assign_buffers_no_scratch(cache.v);
-            LLAMA_LOG_INFO("%s: offloading v cache to GPU\n", __func__);
-            vram_kv_cache += ggml_nbytes(cache.v);
-        }
-        if (n_gpu_layers > (int)n_layer + 2) {
-            ggml_cuda_assign_buffers_no_scratch(cache.k);
-            LLAMA_LOG_INFO("%s: offloading k cache to GPU\n", __func__);
-            vram_kv_cache += ggml_nbytes(cache.k);
-        }
-        if (vram_kv_cache > 0) {
-            LLAMA_LOG_INFO("%s: VRAM kv self = %.2f MB\n", __func__, vram_kv_cache / 1024.0 / 1024.0);
-        }
+    if (n_gpu_layers > (int)n_layer + 1) {
+        ggml_cuda_assign_buffers_no_scratch(cache.v);
+        LLAMA_LOG_INFO("%s: offloading v cache to GPU\n", __func__);
+        vram_kv_cache += ggml_nbytes(cache.v);
     }
-#endif
+    if (n_gpu_layers > (int)n_layer + 2) {
+        ggml_cuda_assign_buffers_no_scratch(cache.k);
+        LLAMA_LOG_INFO("%s: offloading k cache to GPU\n", __func__);
+        vram_kv_cache += ggml_nbytes(cache.k);
+    }
+    if (vram_kv_cache > 0) {
+        LLAMA_LOG_INFO("%s: VRAM kv self = %.2f MB\n", __func__, vram_kv_cache / 1024.0 / 1024.0);
+    }
+#endif // GGML_USE_CUBLAS
 
     return true;
 }
@@ -2548,22 +2521,18 @@ static void llm_load_tensors(
     }
 
     (void) main_gpu;
-
-    enum ggml_backend_type llama_backend_offload = GGML_BACKEND_CPU;
-    enum ggml_backend_type llama_backend_offload_split = GGML_BACKEND_CPU;
-
 #ifdef GGML_USE_CUBLAS
-    if (ggml_cublas_loaded()) {
-        LLAMA_LOG_INFO("%s: using " GGML_CUDA_NAME " for GPU acceleration\n", __func__);
-        ggml_cuda_set_main_device(main_gpu);
-
-        llama_backend_offload = GGML_BACKEND_GPU;
-        llama_backend_offload_split = GGML_BACKEND_GPU_SPLIT;
-    }
+    LLAMA_LOG_INFO("%s: using " GGML_CUDA_NAME " for GPU acceleration\n", __func__);
+    ggml_cuda_set_main_device(main_gpu);
+#define LLAMA_BACKEND_OFFLOAD       GGML_BACKEND_GPU
+#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU_SPLIT
 #elif defined(GGML_USE_CLBLAST)
-        LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration\n", __func__);
-        llama_backend_offload = GGML_BACKEND_GPU;
-        llama_backend_offload_split = GGML_BACKEND_GPU;
+    LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration\n", __func__);
+#define LLAMA_BACKEND_OFFLOAD       GGML_BACKEND_GPU
+#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU
+#else
+#define LLAMA_BACKEND_OFFLOAD       GGML_BACKEND_CPU
+#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_CPU
 #endif
 
     // prepare memory for the weights
@@ -2590,12 +2559,12 @@ static void llm_load_tensors(
                             // 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;
+                            backend_norm = LLAMA_BACKEND_OFFLOAD;
 #else
-                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
+                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
 #endif // _WIN32
 
-                            backend_output = llama_backend_offload_split;
+                            backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
                         } else {
                             backend_norm   = GGML_BACKEND_CPU;
                             backend_output = GGML_BACKEND_CPU;
@@ -2619,8 +2588,8 @@ static void llm_load_tensors(
                     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
+                        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];
 
@@ -2656,12 +2625,12 @@ static void llm_load_tensors(
                             // 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;
+                            backend_norm = LLAMA_BACKEND_OFFLOAD;
 #else
-                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
+                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
 #endif // _WIN32
 
-                            backend_output = llama_backend_offload_split;
+                            backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
                         } else {
                             backend_norm   = GGML_BACKEND_CPU;
                             backend_output = GGML_BACKEND_CPU;
@@ -2685,8 +2654,8 @@ static void llm_load_tensors(
                     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
+                        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];
 
@@ -2726,12 +2695,12 @@ static void llm_load_tensors(
                             // 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;
+                            backend_norm = LLAMA_BACKEND_OFFLOAD;
 #else
-                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
+                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
 #endif // _WIN32
 
-                            backend_output = llama_backend_offload_split;
+                            backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
                         } else {
                             backend_norm   = GGML_BACKEND_CPU;
                             backend_output = GGML_BACKEND_CPU;
@@ -2757,8 +2726,8 @@ static void llm_load_tensors(
                     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
+                        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];
 
@@ -2803,12 +2772,12 @@ static void llm_load_tensors(
                             // 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;
+                            backend_norm = LLAMA_BACKEND_OFFLOAD;
 #else
-                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
+                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
 #endif // _WIN32
 
-                            backend_output = llama_backend_offload_split;
+                            backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
                         } else {
                             backend_norm   = GGML_BACKEND_CPU;
                             backend_output = GGML_BACKEND_CPU;
@@ -2834,8 +2803,8 @@ static void llm_load_tensors(
                     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
+                        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];
 
@@ -2880,12 +2849,12 @@ static void llm_load_tensors(
                             // 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;
+                            backend_norm = LLAMA_BACKEND_OFFLOAD;
 #else
-                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
+                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
 #endif // _WIN32
 
-                            backend_output = llama_backend_offload_split;
+                            backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
                         } else {
                             backend_norm   = GGML_BACKEND_CPU;
                             backend_output = GGML_BACKEND_CPU;
@@ -2908,8 +2877,8 @@ static void llm_load_tensors(
                     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;
-                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split;
+                        const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
+                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT;
                         auto & layer = model.layers[i];
                         layer.attn_norm     = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, backend);
                         layer.attn_norm_b   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "bias",   i), {n_embd}, backend);
@@ -2946,12 +2915,12 @@ static void llm_load_tensors(
                             // 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;
+                            backend_norm = LLAMA_BACKEND_OFFLOAD;
 #else
-                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
+                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
 #endif // _WIN32
 
-                            backend_output = llama_backend_offload_split;
+                            backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
                         } else {
                             backend_norm   = GGML_BACKEND_CPU;
                             backend_output = GGML_BACKEND_CPU;
@@ -2977,8 +2946,8 @@ static void llm_load_tensors(
                     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
+                        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];
 
@@ -3024,12 +2993,12 @@ static void llm_load_tensors(
                             // 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;
+                            backend_norm = LLAMA_BACKEND_OFFLOAD;
 #else
-                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
+                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
 #endif // _WIN32
 
-                            backend_output = llama_backend_offload_split;
+                            backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
                         } else {
                             backend_norm   = GGML_BACKEND_CPU;
                             backend_output = GGML_BACKEND_CPU;
@@ -3053,8 +3022,8 @@ static void llm_load_tensors(
                     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
+                        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];
 
@@ -4209,7 +4178,7 @@ struct llm_build_context {
                 struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass);
                 cb(Kcur, "Kcur", il);
 
-                struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 2, 1, 0, 3));
+                struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 1, 2, 0, 3));
                 cb(Q, "Q", il);
 
                 Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3));
@@ -5195,12 +5164,11 @@ static int llama_decode_internal(
 
     // 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_LLAMA    ||
+        model.arch == LLM_ARCH_BAICHUAN ||
+        model.arch == LLM_ARCH_FALCON   ||
+        model.arch == LLM_ARCH_REFACT   ||
+        model.arch == LLM_ARCH_MPT;
 
     const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3;
     if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) {

mypy.ini

@@ -3,4 +3,3 @@ strict = true
 allow_untyped_calls = true
 allow_untyped_defs = true
 allow_incomplete_defs = true
-disable_error_code = import-untyped