Merge branch 'ggerganov:master' into master
This commit is contained in:
Marc
GitHub
commit
bf7eeb731f
28 changed files with 262 additions and 195 deletions
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@ -3,6 +3,7 @@ Checks: >
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bugprone-*,
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-bugprone-easily-swappable-parameters,
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-bugprone-implicit-widening-of-multiplication-result,
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||||
-bugprone-misplaced-widening-cast,
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||||
-bugprone-narrowing-conversions,
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||||
readability-*,
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||||
-readability-avoid-unconditional-preprocessor-if,
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||||
|
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@ -15,4 +16,8 @@ Checks: >
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|||
-clang-analyzer-security.insecureAPI.DeprecatedOrUnsafeBufferHandling,
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performance-*,
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||||
portability-*,
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misc-*,
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-misc-const-correctness,
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-misc-non-private-member-variables-in-classes,
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-misc-no-recursion,
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FormatStyle: none
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|
|
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|||
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@ -83,6 +83,7 @@ set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for
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option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF)
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option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
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option(LLAMA_METAL "llama: use Metal" ${LLAMA_METAL_DEFAULT})
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option(LLAMA_METAL_NDEBUG "llama: disable Metal debugging" OFF)
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option(LLAMA_MPI "llama: use MPI" OFF)
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option(LLAMA_K_QUANTS "llama: use k-quants" ON)
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option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF)
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@ -174,7 +175,9 @@ if (LLAMA_METAL)
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set(GGML_SOURCES_METAL ggml-metal.m ggml-metal.h)
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add_compile_definitions(GGML_USE_METAL)
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#add_compile_definitions(GGML_METAL_NDEBUG)
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if (LLAMA_METAL_NDEBUG)
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add_compile_definitions(GGML_METAL_NDEBUG)
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endif()
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# get full path to the file
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#add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/")
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@ -423,7 +426,7 @@ if (LLAMA_ALL_WARNINGS)
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)
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if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
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# g++ only
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set(cxx_flags ${cxx_flags} -Wno-format-truncation)
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set(cxx_flags ${cxx_flags} -Wno-format-truncation -Wno-array-bounds)
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endif()
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else()
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# todo : msvc
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35
Makefile
35
Makefile
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@ -42,9 +42,9 @@ endif
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default: $(BUILD_TARGETS)
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test:
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@echo "Running tests..."
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@for test_target in $(TEST_TARGETS); do \
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test: $(TEST_TARGETS)
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@failures=0; \
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for test_target in $(TEST_TARGETS); do \
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if [ "$$test_target" = "tests/test-tokenizer-0-llama" ]; then \
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./$$test_target $(CURDIR)/models/ggml-vocab-llama.gguf; \
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elif [ "$$test_target" = "tests/test-tokenizer-0-falcon" ]; then \
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@ -52,10 +52,21 @@ test:
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elif [ "$$test_target" = "tests/test-tokenizer-1" ]; then \
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continue; \
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else \
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echo "Running test $$test_target..."; \
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./$$test_target; \
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fi; \
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done
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@echo "All tests have been run."
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if [ $$? -ne 0 ]; then \
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printf 'Test $$test_target FAILED!\n\n' $$test_target; \
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failures=$$(( failures + 1 )); \
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else \
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printf 'Test %s passed.\n\n' $$test_target; \
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fi; \
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done; \
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if [ $$failures -gt 0 ]; then \
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printf '\n%s tests failed.\n' $$failures; \
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exit 1; \
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fi
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@echo 'All tests passed.'
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all: $(BUILD_TARGETS) $(TEST_TARGETS)
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@ -91,8 +102,8 @@ else
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OPT = -O3
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endif
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MK_CPPFLAGS = -I. -Icommon
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MK_CFLAGS = $(CPPFLAGS) $(OPT) -std=c11 -fPIC
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MK_CXXFLAGS = $(CPPFLAGS) $(OPT) -std=c++11 -fPIC
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MK_CFLAGS = $(OPT) -std=c11 -fPIC
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MK_CXXFLAGS = $(OPT) -std=c++11 -fPIC
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MK_LDFLAGS =
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ifdef LLAMA_DEBUG
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@ -123,7 +134,7 @@ MK_CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-m
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ifeq '' '$(findstring clang++,$(CXX))'
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# g++ only
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MK_CXXFLAGS += -Wno-format-truncation
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MK_CXXFLAGS += -Wno-format-truncation -Wno-array-bounds
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endif
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# OS specific
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@ -360,6 +371,9 @@ ifdef LLAMA_METAL
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MK_CPPFLAGS += -DGGML_USE_METAL
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MK_LDFLAGS += -framework Foundation -framework Metal -framework MetalKit
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OBJS += ggml-metal.o
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ifdef LLAMA_METAL_NDEBUG
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MK_CPPFLAGS += -DGGML_METAL_NDEBUG
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endif
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endif # LLAMA_METAL
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ifdef LLAMA_METAL
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@ -378,9 +392,8 @@ k_quants.o: k_quants.c k_quants.h
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endif # LLAMA_NO_K_QUANTS
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# combine build flags with cmdline overrides
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override CPPFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS)
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override CFLAGS := $(MK_CFLAGS) $(CFLAGS)
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override CXXFLAGS := $(MK_CXXFLAGS) $(CXXFLAGS)
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override CFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CFLAGS) $(CFLAGS)
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override CXXFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CXXFLAGS) $(CXXFLAGS)
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override LDFLAGS := $(MK_LDFLAGS) $(LDFLAGS)
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#
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@ -57,7 +57,7 @@ int32_t get_num_physical_cores() {
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siblings.insert(line);
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}
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}
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if (siblings.size() > 0) {
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if (!siblings.empty()) {
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return static_cast<int32_t>(siblings.size());
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}
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#elif defined(__APPLE__) && defined(__MACH__)
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@ -773,7 +773,7 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
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LOG("warming up the model with an empty run\n");
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const std::vector<llama_token> tmp = { llama_token_bos(lctx), llama_token_eos(lctx), };
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llama_eval(lctx, tmp.data(), tmp.size(), 0, params.n_threads);
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llama_eval(lctx, tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, params.n_threads);
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llama_reset_timings(lctx);
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}
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@ -20,6 +20,9 @@
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#define DIRECTORY_SEPARATOR '/'
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#endif // _WIN32
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#define die(msg) do { fputs("error: " msg "\n", stderr); exit(1); } while (0)
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#define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", ##__VA_ARGS__); exit(1); } while (0)
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//
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// CLI argument parsing
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//
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||||
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@ -415,6 +415,7 @@ namespace grammar_parser {
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std::vector<const llama_grammar_element *> parse_state::c_rules() {
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std::vector<const llama_grammar_element *> ret;
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ret.reserve(rules.size());
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for (const auto & rule : rules) {
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ret.push_back(rule.data());
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}
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@ -5,6 +5,7 @@ import argparse
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import math
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import struct
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import sys
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from enum import IntEnum
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from pathlib import Path
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import numpy as np
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@ -34,10 +35,35 @@ GGML_QUANT_SIZES = {
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gguf.GGMLQuantizationType.Q8_K : (256, 4 + QK_K + QK_K // 8),
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}
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|
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class GGMLFormat(IntEnum):
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GGML = 0
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GGMF = 1
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GGJT = 2
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class GGMLFType(IntEnum):
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ALL_F32 = 0
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MOSTLY_F16 = 1
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MOSTLY_Q4_0 = 2
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MOSTLY_Q4_1 = 3
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MOSTLY_Q4_1_SOME_F16 = 4
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MOSTLY_Q8_0 = 7
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MOSTLY_Q5_0 = 8
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MOSTLY_Q5_1 = 9
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MOSTLY_Q2_K = 10
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MOSTLY_Q3_K_S = 11
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MOSTLY_Q3_K_M = 12
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MOSTLY_Q3_K_L = 13
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MOSTLY_Q4_K_S = 14
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MOSTLY_Q4_K_M = 15
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MOSTLY_Q5_K_S = 16
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MOSTLY_Q5_K_M = 17
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MOSTLY_Q6_K = 18
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class Hyperparameters:
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def __init__(self):
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self.n_vocab = self.n_embd = self.n_mult = self.n_head = self.n_layer = self.n_rot = self.ftype = 0
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self.n_ff = 0
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self.n_vocab = self.n_embd = self.n_mult = self.n_head = 0
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self.n_layer = self.n_rot = self.n_ff = 0
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self.ftype = GGMLFType.ALL_F32
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def set_n_ff(self, model):
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ff_tensor_idx = model.tensor_map.get(b'layers.0.feed_forward.w1.weight')
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@ -53,16 +79,21 @@ class Hyperparameters:
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self.n_head,
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self.n_layer,
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self.n_rot,
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self.ftype,
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ftype,
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) = struct.unpack('<7I', data[offset:offset + (4 * 7)])
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try:
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self.ftype = GGMLFType(ftype)
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except ValueError:
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raise ValueError(f'Invalid ftype {ftype}')
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return 4 * 7
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def __str__(self):
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return f'<Hyperparameters: n_vocab={self.n_vocab}, n_embd={self.n_embd}, n_mult={self.n_mult}, n_head={self.n_head}, n_layer={self.n_layer}, n_rot={self.n_rot}, n_ff={self.n_ff}, ftype={self.ftype}>'
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return f'<Hyperparameters: n_vocab={self.n_vocab}, n_embd={self.n_embd}, n_mult={self.n_mult}, n_head={self.n_head}, n_layer={self.n_layer}, n_rot={self.n_rot}, n_ff={self.n_ff}, ftype={self.ftype.name}>'
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class Vocab:
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def __init__(self):
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def __init__(self, load_scores = True):
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self.items = []
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self.load_scores = load_scores
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def load(self, data, offset, n_vocab):
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orig_offset = offset
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@ -70,20 +101,24 @@ class Vocab:
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itemlen = struct.unpack('<I', data[offset:offset + 4])[0]
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assert itemlen < 4096, 'Absurd vocab item length'
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offset += 4
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vocab = bytes(data[offset:offset + itemlen])
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item_text = bytes(data[offset:offset + itemlen])
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offset += itemlen
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score = struct.unpack('<f', data[offset:offset + 4])[0]
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if self.load_scores:
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item_score = struct.unpack('<f', data[offset:offset + 4])[0]
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offset += 4
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self.items.append((vocab, score))
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else:
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item_score = 0.0
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self.items.append((item_text, item_score))
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return offset - orig_offset
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|
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class Tensor:
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def __init__(self):
|
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def __init__(self, use_padding = True):
|
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self.name = None
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self.dims: tuple[int, ...] = ()
|
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self.dtype = None
|
||||
self.start_offset = 0
|
||||
self.len_bytes = np.int64(0)
|
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self.use_padding = use_padding
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|
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def load(self, data, offset):
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orig_offset = offset
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|
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@ -99,7 +134,7 @@ class Tensor:
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offset += 4 * n_dims
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self.name = bytes(data[offset:offset + name_len])
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offset += name_len
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pad = ((offset + 31) & ~31) - offset
|
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pad = ((offset + 31) & ~31) - offset if self.use_padding else 0
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offset += pad
|
||||
n_elems = np.prod(self.dims)
|
||||
n_bytes = np.int64(np.int64(n_elems) * np.int64(tysize)) // np.int64(blksize)
|
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|
|
@ -109,7 +144,7 @@ class Tensor:
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# print(n_dims, name_len, dtype, self.dims, self.name, pad)
|
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return offset - orig_offset
|
||||
|
||||
class GGMLV3Model:
|
||||
class GGMLModel:
|
||||
def __init__(self):
|
||||
self.hyperparameters = None
|
||||
self.vocab = None
|
||||
|
|
@ -117,20 +152,52 @@ class GGMLV3Model:
|
|||
self.tensors = []
|
||||
|
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def validate_header(self, data, offset):
|
||||
if bytes(data[offset:offset + 4]) != b'tjgg' or struct.unpack('<I', data[offset + 4:offset + 8])[0] != 3:
|
||||
raise ValueError('Only GGJTv3 supported')
|
||||
magic = bytes(data[offset:offset + 4])
|
||||
if magic == b'GGUF':
|
||||
raise ValueError('File is already in GGUF format.')
|
||||
if magic == b'lmgg':
|
||||
self.file_format = GGMLFormat.GGML
|
||||
self.format_version = 1
|
||||
return 4
|
||||
version = struct.unpack('<I', data[offset + 4:offset + 8])[0]
|
||||
if magic == b'fmgg':
|
||||
if version != 1:
|
||||
raise ValueError(f'Cannot handle unexpected GGMF file version {version}')
|
||||
self.file_format = GGMLFormat.GGMF
|
||||
self.format_version = version
|
||||
return 8
|
||||
if magic == b'tjgg':
|
||||
if version < 1 or version > 3:
|
||||
raise ValueError(f'Cannot handle unexpected GGJT file version {version}')
|
||||
self.file_format = GGMLFormat.GGJT
|
||||
self.format_version = version
|
||||
return 8
|
||||
raise ValueError(f"Unexpected file magic {magic!r}! This doesn't look like a GGML format file.")
|
||||
|
||||
def validate_conversion(self, ftype):
|
||||
err = ''
|
||||
if (self.file_format < GGMLFormat.GGJT or self.format_version < 2):
|
||||
if ftype not in (GGMLFType.ALL_F32, GGMLFType.MOSTLY_F16):
|
||||
err = 'Quantizations changed in GGJTv2. Can only convert unquantized GGML files older than GGJTv2.'
|
||||
elif (self.file_format == GGMLFormat.GGJT and self.format_version == 2):
|
||||
if ftype in ( GGMLFType.MOSTLY_Q4_0, GGMLFType.MOSTLY_Q4_1,
|
||||
GGMLFType.MOSTLY_Q4_1_SOME_F16, GGMLFType.MOSTLY_Q8_0):
|
||||
err = 'Q4 and Q8 quantizations changed in GGJTv3.'
|
||||
if len(err) > 0:
|
||||
raise ValueError(f'{err} Sorry, your {self.file_format.name}v{self.format_version} file of type {ftype.name} is not eligible for conversion.')
|
||||
|
||||
def load(self, data, offset):
|
||||
offset += self.validate_header(data, offset)
|
||||
hp = Hyperparameters()
|
||||
offset += hp.load(data, offset)
|
||||
vocab = Vocab()
|
||||
print(f'* File format: {self.file_format.name}v{self.format_version} with ftype {hp.ftype.name}')
|
||||
self.validate_conversion(hp.ftype)
|
||||
vocab = Vocab(load_scores = self.file_format > GGMLFormat.GGML)
|
||||
offset += vocab.load(data, offset, hp.n_vocab)
|
||||
tensors: list[Tensor] = []
|
||||
tensor_map = {}
|
||||
while offset < len(data):
|
||||
tensor = Tensor()
|
||||
tensor = Tensor(use_padding = self.file_format > GGMLFormat.GGMF)
|
||||
offset += tensor.load(data, offset)
|
||||
tensor_map[tensor.name] = len(tensors)
|
||||
tensors.append(tensor)
|
||||
|
|
@ -168,7 +235,10 @@ class GGMLToGGUF:
|
|||
|
||||
def save(self):
|
||||
print('* Preparing to save GGUF file')
|
||||
gguf_writer = gguf.GGUFWriter(self.cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], use_temp_file = False)
|
||||
gguf_writer = gguf.GGUFWriter(
|
||||
self.cfg.output,
|
||||
gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA],
|
||||
use_temp_file = False )
|
||||
self.add_params(gguf_writer)
|
||||
self.add_vocab(gguf_writer)
|
||||
if self.special_vocab is not None:
|
||||
|
|
@ -185,7 +255,10 @@ class GGMLToGGUF:
|
|||
def add_params(self, gguf_writer):
|
||||
hp = self.model.hyperparameters
|
||||
cfg = self.cfg
|
||||
desc = cfg.desc if cfg.desc is not None else 'converted from legacy GGJTv3 format'
|
||||
if cfg.desc is not None:
|
||||
desc = cfg.desc
|
||||
else:
|
||||
desc = f'converted from legacy {self.model.file_format.name}v{self.model.format_version} {hp.ftype.name} format'
|
||||
try:
|
||||
# Filenames aren't necessarily valid UTF8.
|
||||
name = cfg.name if cfg.name is not None else cfg.input.name
|
||||
|
|
@ -195,6 +268,7 @@ class GGMLToGGUF:
|
|||
if name is not None:
|
||||
gguf_writer.add_name(name)
|
||||
gguf_writer.add_description(desc)
|
||||
gguf_writer.add_file_type(int(hp.ftype))
|
||||
if self.params_override is not None:
|
||||
po = self.params_override
|
||||
assert po.n_embd == hp.n_embd, 'Model hyperparams mismatch'
|
||||
|
|
@ -231,7 +305,8 @@ class GGMLToGGUF:
|
|||
tokens.append(vbytes)
|
||||
scores.append(score)
|
||||
toktypes.append(ttype)
|
||||
assert len(tokens) == hp.n_vocab, f'Override vocab has a different number of items than hyperparameters - override = {len(tokens)} but n_vocab={hp.n_vocab}'
|
||||
assert len(tokens) == hp.n_vocab, \
|
||||
f'Override vocab has a different number of items than hyperparameters - override = {len(tokens)} but n_vocab={hp.n_vocab}'
|
||||
gguf_writer.add_token_list(tokens)
|
||||
gguf_writer.add_token_scores(scores)
|
||||
if len(toktypes) > 0:
|
||||
|
|
@ -283,7 +358,11 @@ class GGMLToGGUF:
|
|||
tempdims[1] = tempdims[0]
|
||||
tempdims[0] = temp
|
||||
# print(f'+ {tensor.name} | {mapped_name} {tensor.dims} :: {tempdims}')
|
||||
gguf_writer.add_tensor(mapped_name, data[tensor.start_offset:tensor.start_offset + tensor.len_bytes], raw_shape = tempdims, raw_dtype = tensor.dtype)
|
||||
gguf_writer.add_tensor(
|
||||
mapped_name,
|
||||
data[tensor.start_offset:tensor.start_offset + tensor.len_bytes],
|
||||
raw_shape = tempdims,
|
||||
raw_dtype = tensor.dtype )
|
||||
|
||||
def handle_metadata(cfg, hp):
|
||||
import convert
|
||||
|
|
@ -305,32 +384,46 @@ def handle_metadata(cfg, hp):
|
|||
params = convert.Params.loadOriginalParamsJson(fakemodel, orig_config_path)
|
||||
else:
|
||||
raise ValueError('Unable to load metadata')
|
||||
vocab = convert.load_vocab(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir, cfg.vocabtype)
|
||||
vocab = convert.load_vocab(
|
||||
cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir,
|
||||
cfg.vocabtype )
|
||||
# FIXME: Respect cfg.vocab_dir?
|
||||
svocab = gguf.SpecialVocab(cfg.model_metadata_dir)
|
||||
convert.check_vocab_size(params, vocab)
|
||||
return (params, vocab, svocab)
|
||||
|
||||
def handle_args():
|
||||
parser = argparse.ArgumentParser(description = 'Convert GGMLv3 models to GGUF')
|
||||
parser.add_argument('--input', '-i', type = Path, required = True, help = 'Input GGMLv3 filename')
|
||||
parser.add_argument('--output', '-o', type = Path, required = True, help ='Output GGUF filename')
|
||||
parser.add_argument('--name', help = 'Set model name')
|
||||
parser.add_argument('--desc', help = 'Set model description')
|
||||
parser.add_argument('--gqa', type = int, default = 1, help = 'grouped-query attention factor (use 8 for LLaMA2 70B)')
|
||||
parser.add_argument('--eps', default = '5.0e-06', help = 'RMS norm eps: Use 1e-6 for LLaMA1 and OpenLLaMA, use 1e-5 for LLaMA2')
|
||||
parser.add_argument('--context-length', '-c', type=int, default = 2048, help = 'Default max context length: LLaMA1 is typically 2048, LLaMA2 is typically 4096')
|
||||
parser.add_argument('--model-metadata-dir', '-m', type = Path, help ='Load HuggingFace/.pth vocab and metadata from the specified directory')
|
||||
parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir")
|
||||
parser.add_argument("--vocabtype", choices=["spm", "bpe"], help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)", default="spm")
|
||||
parser = argparse.ArgumentParser(description = 'Convert GGML models to GGUF')
|
||||
parser.add_argument('--input', '-i', type = Path, required = True,
|
||||
help = 'Input GGMLv3 filename')
|
||||
parser.add_argument('--output', '-o', type = Path, required = True,
|
||||
help ='Output GGUF filename')
|
||||
parser.add_argument('--name',
|
||||
help = 'Set model name')
|
||||
parser.add_argument('--desc',
|
||||
help = 'Set model description')
|
||||
parser.add_argument('--gqa', type = int, default = 1,
|
||||
help = 'grouped-query attention factor (use 8 for LLaMA2 70B)')
|
||||
parser.add_argument('--eps', default = '5.0e-06',
|
||||
help = 'RMS norm eps: Use 1e-6 for LLaMA1 and OpenLLaMA, use 1e-5 for LLaMA2')
|
||||
parser.add_argument('--context-length', '-c', type=int, default = 2048,
|
||||
help = 'Default max context length: LLaMA1 is typically 2048, LLaMA2 is typically 4096')
|
||||
parser.add_argument('--model-metadata-dir', '-m', type = Path,
|
||||
help ='Load HuggingFace/.pth vocab and metadata from the specified directory')
|
||||
parser.add_argument("--vocab-dir", type=Path,
|
||||
help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir")
|
||||
parser.add_argument("--vocabtype", choices=["spm", "bpe"], default="spm",
|
||||
help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)")
|
||||
return parser.parse_args()
|
||||
|
||||
def main():
|
||||
cfg = handle_args()
|
||||
print(f'* Using config: {cfg}')
|
||||
print('\n=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===\n')
|
||||
if cfg.model_metadata_dir is None and (cfg.gqa == 1 or cfg.eps == '5.0e-06'):
|
||||
print('- Note: If converting LLaMA2, specifying "--eps 1e-5" is required. 70B models also need "--gqa 8".')
|
||||
data = np.memmap(cfg.input, mode = 'r')
|
||||
model = GGMLV3Model()
|
||||
model = GGMLModel()
|
||||
print('* Scanning GGML input file')
|
||||
offset = model.load(data, 0)
|
||||
print(f'* GGML model hyperparameters: {model.hyperparameters}')
|
||||
|
|
@ -345,7 +438,12 @@ def main():
|
|||
print(f'* Special vocab: {special_vocab}')
|
||||
else:
|
||||
print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n')
|
||||
converter = GGMLToGGUF(model, data, cfg, params_override = params_override, vocab_override = vocab_override, special_vocab = special_vocab)
|
||||
if model.file_format == GGMLFormat.GGML:
|
||||
print('! This is a very old GGML file that does not contain vocab scores. Strongly recommend using model metadata!')
|
||||
converter = GGMLToGGUF(model, data, cfg,
|
||||
params_override = params_override,
|
||||
vocab_override = vocab_override,
|
||||
special_vocab = special_vocab )
|
||||
converter.save()
|
||||
print(f'* Successful completion. Output saved to: {cfg.output}')
|
||||
|
||||
12
convert.py
12
convert.py
|
|
@ -266,7 +266,7 @@ class Params:
|
|||
f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None
|
||||
|
||||
# hack to determine LLaMA v1 vs v2 vs CodeLlama
|
||||
if f_rope_freq_base and f_rope_freq_base == 1000000:
|
||||
if f_rope_freq_base == 1000000:
|
||||
# CodeLlama
|
||||
n_ctx = 16384
|
||||
elif config["norm_eps"] == 1e-05:
|
||||
|
|
@ -841,9 +841,9 @@ class OutputFile:
|
|||
name = "LLaMA"
|
||||
|
||||
# TODO: better logic to determine model name
|
||||
if (params.n_ctx == 4096):
|
||||
if params.n_ctx == 4096:
|
||||
name = "LLaMA v2"
|
||||
elif params.path_model:
|
||||
elif params.path_model is not None:
|
||||
name = str(params.path_model.parent).split('/')[-1]
|
||||
|
||||
self.gguf.add_name (name)
|
||||
|
|
@ -856,13 +856,13 @@ class OutputFile:
|
|||
self.gguf.add_head_count_kv (params.n_head_kv)
|
||||
self.gguf.add_layer_norm_rms_eps (params.f_norm_eps)
|
||||
|
||||
if params.f_rope_freq_base:
|
||||
if params.f_rope_freq_base is not None:
|
||||
self.gguf.add_rope_freq_base(params.f_rope_freq_base)
|
||||
|
||||
if params.f_rope_scale:
|
||||
if params.f_rope_scale is not None:
|
||||
self.gguf.add_rope_scale_linear(params.f_rope_scale)
|
||||
|
||||
if params.ftype:
|
||||
if params.ftype is not None:
|
||||
self.gguf.add_file_type(params.ftype)
|
||||
|
||||
def add_meta_vocab(self, vocab: Vocab) -> None:
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
#include "ggml.h"
|
||||
#include "llama.h"
|
||||
#include "common.h"
|
||||
|
||||
#include <unordered_map>
|
||||
#include <vector>
|
||||
|
|
@ -499,10 +500,10 @@ struct llama_file {
|
|||
errno = 0;
|
||||
std::size_t ret = std::fread(ptr, size, 1, fp);
|
||||
if (ferror(fp)) {
|
||||
throw std::runtime_error(format("read error: %s", strerror(errno)));
|
||||
die_fmt("fread failed: %s", strerror(errno));
|
||||
}
|
||||
if (ret != 1) {
|
||||
throw std::runtime_error(std::string("unexpectedly reached end of file"));
|
||||
die("unexpectedly reached end of file");
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -597,8 +598,7 @@ void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab)
|
|||
printf("Assuming llama2.c vocabulary since %s is not a gguf file\n", filename);
|
||||
llama_file file(filename, "rb");
|
||||
if (!file.fp) {
|
||||
fprintf(stderr, "error: %s: %s\n", strerror(errno), filename);
|
||||
exit(1);
|
||||
die_fmt("%s: %s", strerror(errno), filename);
|
||||
}
|
||||
const int n_vocab = config->vocab_size;
|
||||
/* uint32_t max_token_length = */ file.read_u32(); // unused
|
||||
|
|
|
|||
|
|
@ -23,7 +23,7 @@ extern "C" {
|
|||
struct MyModel* create_mymodel(int argc, char ** argv) {
|
||||
gpt_params params;
|
||||
|
||||
if (gpt_params_parse(argc, argv, params) == false) {
|
||||
if (!gpt_params_parse(argc, argv, params)) {
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -11,7 +11,7 @@
|
|||
int main(int argc, char ** argv) {
|
||||
gpt_params params;
|
||||
|
||||
if (gpt_params_parse(argc, argv, params) == false) {
|
||||
if (!gpt_params_parse(argc, argv, params)) {
|
||||
return 1;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -953,7 +953,7 @@ int main(int argc, char ** argv) {
|
|||
|
||||
gpt_params params;
|
||||
|
||||
if (gpt_params_parse(argc, argv, params) == false) {
|
||||
if (!gpt_params_parse(argc, argv, params)) {
|
||||
return 1;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -925,7 +925,7 @@ int main(int argc, char ** argv) {
|
|||
|
||||
gpt_params params;
|
||||
|
||||
if (gpt_params_parse(argc, argv, params) == false) {
|
||||
if (!gpt_params_parse(argc, argv, params)) {
|
||||
return 1;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -986,7 +986,12 @@ int main(int argc, char ** argv) {
|
|||
test t(inst, lmodel, ctx);
|
||||
|
||||
// warmup run
|
||||
if (t.n_prompt > 0) {
|
||||
test_prompt(ctx, std::min(2, t.n_batch), 0, t.n_batch, t.n_threads);
|
||||
}
|
||||
if (t.n_gen > 0) {
|
||||
test_gen(ctx, 1, 0, t.n_threads);
|
||||
}
|
||||
|
||||
for (int i = 0; i < params.reps; i++) {
|
||||
uint64_t t_start = get_time_ns();
|
||||
|
|
|
|||
|
|
@ -48,8 +48,9 @@ static bool is_interacting = false;
|
|||
|
||||
void write_logfile(
|
||||
const llama_context * ctx, const gpt_params & params, const llama_model * model,
|
||||
const std::vector<llama_token> input_tokens, const std::string output, const std::vector<llama_token> output_tokens) {
|
||||
|
||||
const std::vector<llama_token> & input_tokens, const std::string & output,
|
||||
const std::vector<llama_token> & output_tokens
|
||||
) {
|
||||
if (params.logdir.empty()) {
|
||||
return;
|
||||
}
|
||||
|
|
@ -109,7 +110,7 @@ int main(int argc, char ** argv) {
|
|||
gpt_params params;
|
||||
g_params = ¶ms;
|
||||
|
||||
if (gpt_params_parse(argc, argv, params) == false) {
|
||||
if (!gpt_params_parse(argc, argv, params)) {
|
||||
return 1;
|
||||
}
|
||||
|
||||
|
|
@ -303,7 +304,7 @@ int main(int argc, char ** argv) {
|
|||
|
||||
// debug message about similarity of saved session, if applicable
|
||||
size_t n_matching_session_tokens = 0;
|
||||
if (session_tokens.size() > 0) {
|
||||
if (!session_tokens.empty()) {
|
||||
for (llama_token id : session_tokens) {
|
||||
if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) {
|
||||
break;
|
||||
|
|
@ -401,7 +402,7 @@ int main(int argc, char ** argv) {
|
|||
|
||||
LOG_TEE("%s: interactive mode on.\n", __func__);
|
||||
|
||||
if (params.antiprompt.size()) {
|
||||
if (!params.antiprompt.empty()) {
|
||||
for (const auto & antiprompt : params.antiprompt) {
|
||||
LOG_TEE("Reverse prompt: '%s'\n", antiprompt.c_str());
|
||||
}
|
||||
|
|
@ -499,7 +500,7 @@ int main(int argc, char ** argv) {
|
|||
|
||||
while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
|
||||
// predict
|
||||
if (embd.size() > 0) {
|
||||
if (!embd.empty()) {
|
||||
// Note: n_ctx - 4 here is to match the logic for commandline prompt handling via
|
||||
// --prompt or --file which uses the same value.
|
||||
int max_embd_size = n_ctx - 4;
|
||||
|
|
@ -624,7 +625,7 @@ int main(int argc, char ** argv) {
|
|||
LOG("n_past = %d\n", n_past);
|
||||
}
|
||||
|
||||
if (embd.size() > 0 && !path_session.empty()) {
|
||||
if (!embd.empty() && !path_session.empty()) {
|
||||
session_tokens.insert(session_tokens.end(), embd.begin(), embd.end());
|
||||
n_session_consumed = session_tokens.size();
|
||||
}
|
||||
|
|
@ -695,7 +696,7 @@ int main(int argc, char ** argv) {
|
|||
// if not currently processing queued inputs;
|
||||
if ((int) embd_inp.size() <= n_consumed) {
|
||||
// check for reverse prompt
|
||||
if (params.antiprompt.size()) {
|
||||
if (!params.antiprompt.empty()) {
|
||||
std::string last_output;
|
||||
for (auto id : last_tokens) {
|
||||
last_output += llama_token_to_piece(ctx, id);
|
||||
|
|
@ -732,7 +733,7 @@ int main(int argc, char ** argv) {
|
|||
LOG("found EOS token\n");
|
||||
|
||||
if (params.interactive) {
|
||||
if (params.antiprompt.size() != 0) {
|
||||
if (!params.antiprompt.empty()) {
|
||||
// tokenize and inject first reverse prompt
|
||||
const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false);
|
||||
embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end());
|
||||
|
|
|
|||
|
|
@ -655,7 +655,7 @@ int main(int argc, char ** argv) {
|
|||
gpt_params params;
|
||||
|
||||
params.n_batch = 512;
|
||||
if (gpt_params_parse(argc, argv, params) == false) {
|
||||
if (!gpt_params_parse(argc, argv, params)) {
|
||||
return 1;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -71,7 +71,7 @@ void quantize_stats_print_usage(int /*argc*/, char ** argv) {
|
|||
}
|
||||
|
||||
// Check if a layer is included/excluded by command line
|
||||
bool layer_included(const quantize_stats_params params, const std::string & layer) {
|
||||
bool layer_included(const quantize_stats_params & params, const std::string & layer) {
|
||||
for (const auto& excluded : params.exclude_layers) {
|
||||
if (std::regex_search(layer, std::regex(excluded))) {
|
||||
return false;
|
||||
|
|
|
|||
|
|
@ -143,11 +143,10 @@ int main(int argc, char ** argv) {
|
|||
if (!try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) {
|
||||
fprintf(stderr, "%s: invalid ftype '%s'\n", __func__, argv[3]);
|
||||
return 1;
|
||||
} else {
|
||||
}
|
||||
if (ftype_str == "COPY") {
|
||||
params.only_copy = true;
|
||||
}
|
||||
}
|
||||
arg_idx++;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ int main(int argc, char ** argv) {
|
|||
params.repeat_last_n = 64;
|
||||
params.prompt = "The quick brown fox";
|
||||
|
||||
if (gpt_params_parse(argc, argv, params) == false) {
|
||||
if (!gpt_params_parse(argc, argv, params)) {
|
||||
return 1;
|
||||
}
|
||||
|
||||
|
|
@ -44,7 +44,7 @@ int main(int argc, char ** argv) {
|
|||
llama_free_model(model);
|
||||
return 1;
|
||||
}
|
||||
auto tokens = llama_tokenize(ctx, params.prompt.c_str(), true);
|
||||
auto tokens = llama_tokenize(ctx, params.prompt, true);
|
||||
auto n_prompt_tokens = tokens.size();
|
||||
if (n_prompt_tokens < 1) {
|
||||
fprintf(stderr, "%s : failed to tokenize prompt\n", __func__);
|
||||
|
|
|
|||
|
|
@ -139,7 +139,7 @@ static std::string tokens_to_output_formatted_string(const llama_context *ctx, c
|
|||
}
|
||||
|
||||
// convert a vector of completion_token_output to json
|
||||
static json probs_vector_to_json(const llama_context *ctx, const std::vector<completion_token_output> probs)
|
||||
static json probs_vector_to_json(const llama_context *ctx, const std::vector<completion_token_output> & probs)
|
||||
{
|
||||
json out = json::array();
|
||||
for (const auto &prob : probs)
|
||||
|
|
@ -271,7 +271,7 @@ struct llama_server_context
|
|||
return true;
|
||||
}
|
||||
|
||||
std::vector<llama_token> tokenize(json json_prompt, bool add_bos)
|
||||
std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const
|
||||
{
|
||||
// If `add_bos` is true, we only add BOS, when json_prompt is a string,
|
||||
// or the first element of the json_prompt array is a string.
|
||||
|
|
@ -611,7 +611,7 @@ struct llama_server_context
|
|||
|
||||
completion_token_output doCompletion()
|
||||
{
|
||||
const completion_token_output token_with_probs = nextToken();
|
||||
auto token_with_probs = nextToken();
|
||||
|
||||
const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(ctx, token_with_probs.tok);
|
||||
generated_text += token_text;
|
||||
|
|
@ -1255,7 +1255,7 @@ void beam_search_callback(void * callback_data, llama_beams_state beams_state) {
|
|||
struct token_translator {
|
||||
llama_context * ctx;
|
||||
std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); }
|
||||
std::string operator()(completion_token_output cto) const { return (*this)(cto.tok); }
|
||||
std::string operator()(const completion_token_output & cto) const { return (*this)(cto.tok); }
|
||||
};
|
||||
|
||||
void append_to_generated_text_from_generated_token_probs(llama_server_context & llama) {
|
||||
|
|
|
|||
|
|
@ -169,10 +169,6 @@ struct my_llama_hparams {
|
|||
|
||||
float rope_freq_base = 10000.0f;
|
||||
float rope_freq_scale = 1.0f;
|
||||
|
||||
bool operator!=(const my_llama_hparams& other) const {
|
||||
return memcmp(this, &other, sizeof(my_llama_hparams));
|
||||
}
|
||||
};
|
||||
|
||||
struct my_llama_layer {
|
||||
|
|
@ -929,28 +925,6 @@ void get_example_targets_batch(struct llama_context * lctx, const int * train_sa
|
|||
}
|
||||
}
|
||||
|
||||
|
||||
#ifdef __GNUC__
|
||||
#ifdef __MINGW32__
|
||||
__attribute__((format(gnu_printf, 1, 2)))
|
||||
#else
|
||||
__attribute__((format(printf, 1, 2)))
|
||||
#endif
|
||||
#endif
|
||||
static std::string format(const char * fmt, ...) {
|
||||
va_list ap, ap2;
|
||||
va_start(ap, fmt);
|
||||
va_copy(ap2, ap);
|
||||
int size = vsnprintf(NULL, 0, fmt, ap);
|
||||
GGML_ASSERT(size >= 0 && size < INT_MAX);
|
||||
std::vector<char> buf(size + 1);
|
||||
int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
|
||||
GGML_ASSERT(size2 == size);
|
||||
va_end(ap2);
|
||||
va_end(ap);
|
||||
return std::string(buf.data(), size);
|
||||
}
|
||||
|
||||
int tokenize_file(struct llama_context * lctx, const char * filename, std::vector<llama_token>& out) {
|
||||
FILE * fp = std::fopen(filename, "rb");
|
||||
if (fp == NULL) {
|
||||
|
|
@ -983,10 +957,10 @@ int tokenize_file(struct llama_context * lctx, const char * filename, std::vecto
|
|||
out.resize(size+1);
|
||||
|
||||
if (std::fread(buf.data(), size, 1, fp) != 1) {
|
||||
throw std::runtime_error(std::string("unexpectedly reached end of file"));
|
||||
die("unexpectedly reached end of file");
|
||||
}
|
||||
if (ferror(fp)) {
|
||||
throw std::runtime_error(format("read error: %s", strerror(errno)));
|
||||
die_fmt("fread failed: %s", strerror(errno));
|
||||
}
|
||||
|
||||
buf[size] = '\0';
|
||||
|
|
@ -1047,11 +1021,11 @@ void shuffle_ints(int * begin, int * end) {
|
|||
if (kid >= 0) { \
|
||||
enum gguf_type ktype = gguf_get_kv_type(ctx, kid); \
|
||||
if (ktype != (type)) { \
|
||||
throw std::runtime_error(format("key %s has wrong type: %s", skey.c_str(), gguf_type_name(ktype))); \
|
||||
die_fmt("key %s has wrong type: %s", skey.c_str(), gguf_type_name(ktype)); \
|
||||
} \
|
||||
(dst) = func(ctx, kid); \
|
||||
} else if (req) { \
|
||||
throw std::runtime_error(format("key not found in model: %s", skey.c_str())); \
|
||||
die_fmt("key not found in model: %s", skey.c_str()); \
|
||||
} \
|
||||
}
|
||||
|
||||
|
|
@ -1136,7 +1110,7 @@ void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_g
|
|||
read_tensor_by_name(opt->lbfgs.lms, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S);
|
||||
read_tensor_by_name(opt->lbfgs.lmy, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y);
|
||||
} else {
|
||||
throw std::runtime_error("unknown optimizer type\n");
|
||||
die("unknown optimizer type");
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -1315,20 +1289,20 @@ void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vocab_mod
|
|||
|
||||
const int token_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_LIST));
|
||||
if (token_idx == -1) {
|
||||
throw std::runtime_error("cannot find tokenizer vocab in model file\n");
|
||||
die("cannot find tokenizer vocab in model file");
|
||||
}
|
||||
const uint32_t n_vocab = gguf_get_arr_n(vctx, token_idx);
|
||||
|
||||
const int score_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_SCORES));
|
||||
if (score_idx == -1) {
|
||||
throw std::runtime_error("cannot find tokenizer scores in model file\n");
|
||||
die("cannot find tokenizer scores in model file");
|
||||
}
|
||||
|
||||
const float * scores = (const float * ) gguf_get_arr_data(vctx, score_idx);
|
||||
|
||||
const int toktype_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE));
|
||||
if (toktype_idx == -1) {
|
||||
throw std::runtime_error("cannot find token type list in GGUF file\n");
|
||||
die("cannot find token type list in GGUF file");
|
||||
}
|
||||
|
||||
const int * toktypes = (const int * ) gguf_get_arr_data(vctx, toktype_idx);
|
||||
|
|
@ -1356,7 +1330,7 @@ void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vocab_mod
|
|||
// read and copy bpe merges
|
||||
const int merges_keyidx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_MERGES));
|
||||
if (merges_keyidx == -1) {
|
||||
throw std::runtime_error("cannot find tokenizer merges in model file\n");
|
||||
die("cannot find tokenizer merges in model file");
|
||||
}
|
||||
|
||||
const int n_merges = gguf_get_arr_n(vctx, merges_keyidx);
|
||||
|
|
@ -1988,7 +1962,7 @@ void opt_callback(void * vdata, float * sched) {
|
|||
float min_sched = params->adam_min_alpha / params->adam_alpha;
|
||||
*sched = min_sched + *sched * (1.0f - min_sched);
|
||||
|
||||
int impr_plot = std::isnan(opt->loss_after) ? 0 : -(int)(1 + (opt->loss_before - opt->loss_after) * 10.0f + 0.5f);
|
||||
int impr_plot = std::isnan(opt->loss_after) ? 0 : -std::lround(1 + (opt->loss_before - opt->loss_after) * 10.0f);
|
||||
printf("%s: iter=%*d, sched=%f loss0=%f loss=%f | improvement: %*d>\n", __func__, 6, opt->iter, *sched, opt->loss_before, opt->loss_after, impr_plot, (int)0);
|
||||
|
||||
if (data->shuffle_countdown < n_batch) {
|
||||
|
|
|
|||
|
|
@ -138,7 +138,7 @@ static bool ggml_allocr_is_own(struct ggml_allocr * alloc, const struct ggml_ten
|
|||
|
||||
void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
|
||||
#ifdef GGML_ALLOCATOR_DEBUG
|
||||
GGML_ASSERT(ggml_is_view(tensor) == false); // views generally get data pointer from one of their sources
|
||||
GGML_ASSERT(!ggml_is_view(tensor)); // views generally get data pointer from one of their sources
|
||||
GGML_ASSERT(tensor->data == NULL); // avoid allocating tensor which already has memory allocated
|
||||
#endif
|
||||
size_t size = ggml_allocr_get_alloc_size(alloc, tensor);
|
||||
|
|
@ -165,9 +165,9 @@ void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor)
|
|||
if (best_fit_block == -1) {
|
||||
// the last block is our last resort
|
||||
struct free_block * block = &alloc->free_blocks[alloc->n_free_blocks - 1];
|
||||
max_avail = MAX(max_avail, block->size);
|
||||
if (block->size >= size) {
|
||||
best_fit_block = alloc->n_free_blocks - 1;
|
||||
max_avail = MAX(max_avail, block->size);
|
||||
} else {
|
||||
fprintf(stderr, "%s: not enough space in the buffer (needed %zu, largest block available %zu)\n",
|
||||
__func__, size, max_avail);
|
||||
|
|
|
|||
|
|
@ -307,7 +307,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
|
|||
|
||||
void * ggml_metal_host_malloc(size_t n) {
|
||||
void * data = NULL;
|
||||
const int result = posix_memalign((void **) &data, getpagesize(), n);
|
||||
const int result = posix_memalign((void **) &data, sysconf(_SC_PAGESIZE), n);
|
||||
if (result != 0) {
|
||||
metal_printf("%s: error: posix_memalign failed\n", __func__);
|
||||
return NULL;
|
||||
|
|
@ -381,7 +381,7 @@ bool ggml_metal_add_buffer(
|
|||
}
|
||||
}
|
||||
|
||||
const size_t size_page = getpagesize();
|
||||
const size_t size_page = sysconf(_SC_PAGESIZE);
|
||||
|
||||
size_t size_aligned = size;
|
||||
if ((size_aligned % size_page) != 0) {
|
||||
|
|
@ -1114,7 +1114,7 @@ void ggml_metal_graph_compute(
|
|||
[encoder setBytes:&freq_base length:sizeof(float) atIndex:21];
|
||||
[encoder setBytes:&freq_scale length:sizeof(float) atIndex:22];
|
||||
|
||||
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
|
||||
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, 1, 1)];
|
||||
} break;
|
||||
case GGML_OP_DUP:
|
||||
case GGML_OP_CPY:
|
||||
|
|
|
|||
|
|
@ -220,14 +220,10 @@ kernel void kernel_norm(
|
|||
}
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
}
|
||||
//// broadcast
|
||||
//if (tpitg == 0) {
|
||||
// sum[0] /= ne00;
|
||||
//}
|
||||
//threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
const float mean = sum[0];
|
||||
const float mean = sum[0] / ne00;
|
||||
|
||||
// recenter and VARIANCE
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
device float * y = dst + tgpig*ne00;
|
||||
sum[tpitg] = 0.0f;
|
||||
for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
|
||||
|
|
@ -235,12 +231,6 @@ kernel void kernel_norm(
|
|||
sum[tpitg] += y[i00] * y[i00];
|
||||
}
|
||||
|
||||
//// VARIANCE
|
||||
//// parallel sum
|
||||
//sum[tpitg] = 0.0f;
|
||||
//for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
|
||||
// sum[tpitg] += y[i00] * y[i00];
|
||||
//}
|
||||
// reduce
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
for (uint i = ntg/2; i > 0; i /= 2) {
|
||||
|
|
@ -249,12 +239,7 @@ kernel void kernel_norm(
|
|||
}
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
}
|
||||
//// broadcast
|
||||
//if (tpitg == 0) {
|
||||
// sum[0] /= ne00;
|
||||
//}
|
||||
//threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
const float variance = sum[0];
|
||||
const float variance = sum[0] / ne00;
|
||||
|
||||
const float scale = 1.0f/sqrt(variance + eps);
|
||||
for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
|
||||
|
|
@ -262,7 +247,6 @@ kernel void kernel_norm(
|
|||
}
|
||||
}
|
||||
|
||||
|
||||
kernel void kernel_rms_norm(
|
||||
device const void * src0,
|
||||
device float * dst,
|
||||
|
|
@ -630,7 +614,6 @@ kernel void kernel_mul_mat_f16_f32(
|
|||
}
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
kernel void kernel_alibi_f32(
|
||||
|
|
@ -699,25 +682,27 @@ kernel void kernel_rope(
|
|||
constant int & mode,
|
||||
constant float & freq_base,
|
||||
constant float & freq_scale,
|
||||
uint3 tpig[[thread_position_in_grid]]) {
|
||||
const int64_t i3 = tpig[2];
|
||||
const int64_t i2 = tpig[1];
|
||||
const int64_t i1 = tpig[0];
|
||||
uint tiitg[[thread_index_in_threadgroup]],
|
||||
uint3 tptg[[threads_per_threadgroup]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]]) {
|
||||
const int64_t i3 = tgpig[2];
|
||||
const int64_t i2 = tgpig[1];
|
||||
const int64_t i1 = tgpig[0];
|
||||
|
||||
const bool is_neox = mode & 2;
|
||||
const float theta_scale = pow(freq_base, -2.0f/n_dims);
|
||||
|
||||
const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2);
|
||||
|
||||
float theta = freq_scale * (float)p;
|
||||
const float theta_0 = freq_scale * (float)p;
|
||||
const float inv_ndims = -1.f/n_dims;
|
||||
|
||||
if (!is_neox) {
|
||||
for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
|
||||
for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) {
|
||||
|
||||
const float theta = theta_0 * pow(freq_base, inv_ndims*i0);
|
||||
const float cos_theta = cos(theta);
|
||||
const float sin_theta = sin(theta);
|
||||
|
||||
theta *= theta_scale;
|
||||
|
||||
device const float * const src = (device float *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
|
||||
device float * dst_data = (device float *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
|
||||
|
||||
|
|
@ -729,12 +714,12 @@ kernel void kernel_rope(
|
|||
}
|
||||
} else {
|
||||
for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
|
||||
for (int64_t ic = 0; ic < n_dims; ic += 2) {
|
||||
for (int64_t ic = 2*tiitg; ic < n_dims; ic += 2*tptg.x) {
|
||||
|
||||
const float theta = theta_0 * pow(freq_base, inv_ndims*ic - ib);
|
||||
const float cos_theta = cos(theta);
|
||||
const float sin_theta = sin(theta);
|
||||
|
||||
theta *= theta_scale;
|
||||
|
||||
const int64_t i0 = ib*n_dims + ic/2;
|
||||
|
||||
device const float * const src = (device float *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
|
||||
|
|
|
|||
12
ggml.c
12
ggml.c
|
|
@ -195,7 +195,7 @@ typedef void * thread_ret_t;
|
|||
inline static void * ggml_aligned_malloc(size_t size) {
|
||||
void * aligned_memory = NULL;
|
||||
#ifdef GGML_USE_METAL
|
||||
int result = posix_memalign(&aligned_memory, getpagesize(), size);
|
||||
int result = posix_memalign(&aligned_memory, sysconf(_SC_PAGESIZE), size);
|
||||
#else
|
||||
int result = posix_memalign(&aligned_memory, GGML_MEM_ALIGN, size);
|
||||
#endif
|
||||
|
|
@ -4815,7 +4815,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
|
|||
|
||||
size_t obj_alloc_size = 0;
|
||||
|
||||
if (view_src == NULL && ctx->no_alloc == false) {
|
||||
if (view_src == NULL && !ctx->no_alloc) {
|
||||
if (ctx->scratch.data != NULL) {
|
||||
// allocate tensor data in the scratch buffer
|
||||
if (ctx->scratch.offs + data_size > ctx->scratch.size) {
|
||||
|
|
@ -5516,7 +5516,7 @@ static struct ggml_tensor * ggml_mul_impl(
|
|||
}
|
||||
|
||||
if (inplace) {
|
||||
GGML_ASSERT(is_node == false);
|
||||
GGML_ASSERT(!is_node);
|
||||
}
|
||||
|
||||
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
||||
|
|
@ -5559,7 +5559,7 @@ static struct ggml_tensor * ggml_div_impl(
|
|||
}
|
||||
|
||||
if (inplace) {
|
||||
GGML_ASSERT(is_node == false);
|
||||
GGML_ASSERT(!is_node);
|
||||
}
|
||||
|
||||
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
||||
|
|
@ -20006,7 +20006,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
|
|||
|
||||
struct ggml_tensor * data = NULL;
|
||||
|
||||
if (params.no_alloc == false) {
|
||||
if (!params.no_alloc) {
|
||||
data = ggml_new_tensor_1d(ctx_data, GGML_TYPE_I8, ctx->size);
|
||||
|
||||
ok = ok && data != NULL;
|
||||
|
|
@ -20047,7 +20047,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
|
|||
}
|
||||
|
||||
// point the data member to the appropriate location in the binary blob using the tensor infos
|
||||
if (params.no_alloc == false) {
|
||||
if (!params.no_alloc) {
|
||||
//cur->data = (char *) data->data + ctx->infos[i].offset - ctx->offset; // offset from start of file
|
||||
cur->data = (char *) data->data + ctx->infos[i].offset; // offset from data
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1089,6 +1089,7 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict
|
|||
if (!max_abs_scale) {
|
||||
memset(&y[i], 0, sizeof(block_q6_K));
|
||||
y[i].d = ggml_fp32_to_fp16(0.f);
|
||||
x += QK_K;
|
||||
continue;
|
||||
}
|
||||
|
||||
|
|
|
|||
35
llama.cpp
35
llama.cpp
|
|
@ -606,16 +606,16 @@ struct llama_mmap {
|
|||
|
||||
if (prefetch > 0) {
|
||||
// Advise the kernel to preload the mapped memory
|
||||
if (madvise(addr, std::min(file->size, prefetch), MADV_WILLNEED)) {
|
||||
fprintf(stderr, "warning: madvise(.., MADV_WILLNEED) failed: %s\n",
|
||||
if (posix_madvise(addr, std::min(file->size, prefetch), POSIX_MADV_WILLNEED)) {
|
||||
fprintf(stderr, "warning: posix_madvise(.., POSIX_MADV_WILLNEED) failed: %s\n",
|
||||
strerror(errno));
|
||||
}
|
||||
}
|
||||
if (numa) {
|
||||
// advise the kernel not to use readahead
|
||||
// (because the next page might not belong on the same node)
|
||||
if (madvise(addr, file->size, MADV_RANDOM)) {
|
||||
fprintf(stderr, "warning: madvise(.., MADV_RANDOM) failed: %s\n",
|
||||
if (posix_madvise(addr, file->size, POSIX_MADV_RANDOM)) {
|
||||
fprintf(stderr, "warning: posix_madvise(.., POSIX_MADV_RANDOM) failed: %s\n",
|
||||
strerror(errno));
|
||||
}
|
||||
}
|
||||
|
|
@ -3052,33 +3052,10 @@ static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) {
|
|||
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_CONTROL;
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}
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static bool llama_is_user_defined_token(const llama_vocab & vocab, llama_token id) {
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return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_USER_DEFINED;
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}
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static bool llama_is_unused_token(const llama_vocab & vocab, llama_token id) {
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||||
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_UNUSED;
|
||||
}
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||||
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static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) {
|
||||
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_BYTE;
|
||||
}
|
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static bool llama_is_bos_token(const llama_vocab & vocab, llama_token id) {
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||||
GGML_ASSERT(llama_is_control_token(vocab, id));
|
||||
return id == vocab.special_bos_id;
|
||||
}
|
||||
|
||||
static bool llama_is_eos_token(const llama_vocab & vocab, llama_token id ) {
|
||||
GGML_ASSERT(llama_is_control_token(vocab, id));
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||||
return id == vocab.special_eos_id;
|
||||
}
|
||||
|
||||
static bool llama_is_pad_token(const llama_vocab & vocab, llama_token id ) {
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||||
GGML_ASSERT(id < 0 || llama_is_control_token(vocab, id));
|
||||
return id == vocab.special_pad_id;
|
||||
}
|
||||
|
||||
static uint8_t llama_token_to_byte(const llama_vocab & vocab, llama_token id) {
|
||||
GGML_ASSERT(llama_is_byte_token(vocab, id));
|
||||
const auto& token_data = vocab.id_to_token.at(id);
|
||||
|
|
@ -4800,9 +4777,11 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
std::vector<std::thread> workers;
|
||||
std::mutex mutex;
|
||||
|
||||
#ifdef GGML_USE_K_QUANTS
|
||||
auto use_more_bits = [] (int i_layer, int num_layers) -> bool {
|
||||
return i_layer < num_layers/8 || i_layer >= 7*num_layers/8 || (i_layer - num_layers/8)%3 == 2;
|
||||
};
|
||||
#endif
|
||||
|
||||
int idx = 0;
|
||||
|
||||
|
|
@ -5947,7 +5926,7 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
|
|||
rng_ss.str(std::string(&rng_buf[0], rng_size));
|
||||
rng_ss >> ctx->rng;
|
||||
|
||||
GGML_ASSERT(rng_ss.fail() == false);
|
||||
GGML_ASSERT(!rng_ss.fail());
|
||||
}
|
||||
|
||||
// set logits
|
||||
|
|
|
|||
|
|
@ -76,7 +76,7 @@ void * align_with_offset(void * ptr, int offset) {
|
|||
return (char *) std::align(MAX_ALIGNMENT, MAX_ALIGNMENT, ptr, dummy_size) + offset;
|
||||
}
|
||||
|
||||
void benchmark_function(size_t size, size_t q_size, int64_t iterations, std::function<size_t(void)> function) {
|
||||
void benchmark_function(size_t size, size_t q_size, int64_t iterations, const std::function<size_t(void)> & function) {
|
||||
int64_t min_time_us = INT64_MAX;
|
||||
int64_t total_time_us = 0;
|
||||
int64_t min_time_cycles = INT64_MAX;
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue