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https://github.com/ggerganov/llama.cpp.git
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12 Commits
master-02f
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master-908
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332311234a | ||
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182af739c4 | ||
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4329d1acb0 |
44
Makefile
44
Makefile
@@ -142,6 +142,28 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64))
|
||||
#CXXFLAGS += -mssse3
|
||||
endif
|
||||
|
||||
ifneq ($(filter aarch64%,$(UNAME_M)),)
|
||||
# Apple M1, M2, etc.
|
||||
# Raspberry Pi 3, 4, Zero 2 (64-bit)
|
||||
CFLAGS += -mcpu=native
|
||||
CXXFLAGS += -mcpu=native
|
||||
endif
|
||||
|
||||
ifneq ($(filter armv6%,$(UNAME_M)),)
|
||||
# Raspberry Pi 1, Zero
|
||||
CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
|
||||
endif
|
||||
|
||||
ifneq ($(filter armv7%,$(UNAME_M)),)
|
||||
# Raspberry Pi 2
|
||||
CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
|
||||
endif
|
||||
|
||||
ifneq ($(filter armv8%,$(UNAME_M)),)
|
||||
# Raspberry Pi 3, 4, Zero 2 (32-bit)
|
||||
CFLAGS += -mfp16-format=ieee -mno-unaligned-access
|
||||
endif
|
||||
|
||||
ifneq ($(filter ppc64%,$(UNAME_M)),)
|
||||
POWER9_M := $(shell grep "POWER9" /proc/cpuinfo)
|
||||
ifneq (,$(findstring POWER9,$(POWER9_M)))
|
||||
@@ -270,28 +292,6 @@ ifdef LLAMA_METAL
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||||
OBJS += ggml-metal.o
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||||
endif # LLAMA_METAL
|
||||
|
||||
ifneq ($(filter aarch64%,$(UNAME_M)),)
|
||||
# Apple M1, M2, etc.
|
||||
# Raspberry Pi 3, 4, Zero 2 (64-bit)
|
||||
CFLAGS += -mcpu=native
|
||||
CXXFLAGS += -mcpu=native
|
||||
endif
|
||||
|
||||
ifneq ($(filter armv6%,$(UNAME_M)),)
|
||||
# Raspberry Pi 1, Zero
|
||||
CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
|
||||
endif
|
||||
|
||||
ifneq ($(filter armv7%,$(UNAME_M)),)
|
||||
# Raspberry Pi 2
|
||||
CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
|
||||
endif
|
||||
|
||||
ifneq ($(filter armv8%,$(UNAME_M)),)
|
||||
# Raspberry Pi 3, 4, Zero 2 (32-bit)
|
||||
CFLAGS += -mfp16-format=ieee -mno-unaligned-access
|
||||
endif
|
||||
|
||||
ifdef LLAMA_METAL
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ggml-metal.o: ggml-metal.m ggml-metal.h
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||||
$(CC) $(CFLAGS) -c $< -o $@
|
||||
|
||||
139
build.zig
139
build.zig
@@ -1,68 +1,87 @@
|
||||
// Compatible with Zig Version 0.11.0
|
||||
const std = @import("std");
|
||||
const commit_hash = @embedFile(".git/refs/heads/master");
|
||||
const Compile = std.Build.Step.Compile;
|
||||
const ConfigHeader = std.Build.Step.ConfigHeader;
|
||||
const Mode = std.builtin.Mode;
|
||||
const CrossTarget = std.zig.CrossTarget;
|
||||
|
||||
const Maker = struct {
|
||||
builder: *std.build.Builder,
|
||||
target: CrossTarget,
|
||||
optimize: Mode,
|
||||
config_header: *ConfigHeader,
|
||||
|
||||
const cflags = .{"-std=c11"};
|
||||
const cxxflags = .{"-std=c++11"};
|
||||
|
||||
fn init(builder: *std.build.Builder) Maker {
|
||||
const commit_hash = @embedFile(".git/refs/heads/master");
|
||||
const config_header = builder.addConfigHeader(
|
||||
.{ .style = .blank, .include_path = "build-info.h" },
|
||||
.{
|
||||
.BUILD_NUMBER = 0,
|
||||
.BUILD_COMMIT = commit_hash[0 .. commit_hash.len - 1], // omit newline
|
||||
},
|
||||
);
|
||||
return Maker{
|
||||
.builder = builder,
|
||||
.target = builder.standardTargetOptions(.{}),
|
||||
.optimize = builder.standardOptimizeOption(.{}),
|
||||
.config_header = config_header,
|
||||
};
|
||||
}
|
||||
|
||||
fn obj(m: *const Maker, name: []const u8, src: []const u8) *Compile {
|
||||
const o = m.builder.addObject(.{ .name = name, .target = m.target, .optimize = m.optimize });
|
||||
if (std.mem.endsWith(u8, src, ".c")) {
|
||||
o.addCSourceFiles(&.{src}, &cflags);
|
||||
o.linkLibC();
|
||||
} else {
|
||||
o.addCSourceFiles(&.{src}, &cxxflags);
|
||||
o.linkLibCpp();
|
||||
}
|
||||
o.addIncludePath(.{ .path = "." });
|
||||
o.addIncludePath(.{ .path = "./examples" });
|
||||
return o;
|
||||
}
|
||||
|
||||
fn exe(m: *const Maker, name: []const u8, src: []const u8, deps: []const *Compile) *Compile {
|
||||
const e = m.builder.addExecutable(.{ .name = name, .target = m.target, .optimize = m.optimize });
|
||||
e.addIncludePath(.{ .path = "." });
|
||||
e.addIncludePath(.{ .path = "./examples" });
|
||||
e.addCSourceFiles(&.{src}, &cxxflags);
|
||||
for (deps) |d| e.addObject(d);
|
||||
e.linkLibC();
|
||||
e.linkLibCpp();
|
||||
e.addConfigHeader(m.config_header);
|
||||
m.builder.installArtifact(e);
|
||||
|
||||
// Currently a bug is preventing correct linking for optimized builds for Windows:
|
||||
// https://github.com/ziglang/zig/issues/15958
|
||||
if (e.target.isWindows()) {
|
||||
e.want_lto = false;
|
||||
}
|
||||
return e;
|
||||
}
|
||||
};
|
||||
|
||||
// Zig Version: 0.11.0-dev.3986+e05c242cd
|
||||
pub fn build(b: *std.build.Builder) void {
|
||||
const target = b.standardTargetOptions(.{});
|
||||
const optimize = b.standardOptimizeOption(.{});
|
||||
const make = Maker.init(b);
|
||||
|
||||
const config_header = b.addConfigHeader(
|
||||
.{ .style = .blank, .include_path = "build-info.h" },
|
||||
.{
|
||||
.BUILD_NUMBER = 0,
|
||||
.BUILD_COMMIT = commit_hash[0 .. commit_hash.len - 1], // omit newline
|
||||
},
|
||||
);
|
||||
const ggml = make.obj("ggml", "ggml.c");
|
||||
const ggml_alloc = make.obj("ggml-alloc", "ggml-alloc.c");
|
||||
const llama = make.obj("llama", "llama.cpp");
|
||||
const common = make.obj("common", "examples/common.cpp");
|
||||
const grammar_parser = make.obj("grammar-parser", "examples/grammar-parser.cpp");
|
||||
|
||||
const lib = b.addStaticLibrary(.{
|
||||
.name = "llama",
|
||||
.target = target,
|
||||
.optimize = optimize,
|
||||
});
|
||||
lib.linkLibC();
|
||||
lib.linkLibCpp();
|
||||
lib.addIncludePath(".");
|
||||
lib.addIncludePath("./examples");
|
||||
lib.addConfigHeader(config_header);
|
||||
lib.addCSourceFiles(&.{"ggml.c"}, &.{"-std=c11"});
|
||||
lib.addCSourceFiles(&.{"llama.cpp"}, &.{"-std=c++11"});
|
||||
b.installArtifact(lib);
|
||||
_ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, llama, common, grammar_parser });
|
||||
_ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, llama });
|
||||
_ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, llama, common });
|
||||
_ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, llama, common });
|
||||
_ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, llama });
|
||||
|
||||
const examples = .{
|
||||
"main",
|
||||
"baby-llama",
|
||||
"embedding",
|
||||
"metal",
|
||||
"perplexity",
|
||||
"quantize",
|
||||
"quantize-stats",
|
||||
"save-load-state",
|
||||
"server",
|
||||
"simple",
|
||||
"train-text-from-scratch",
|
||||
};
|
||||
|
||||
inline for (examples) |example_name| {
|
||||
const exe = b.addExecutable(.{
|
||||
.name = example_name,
|
||||
.target = target,
|
||||
.optimize = optimize,
|
||||
});
|
||||
exe.addIncludePath(".");
|
||||
exe.addIncludePath("./examples");
|
||||
exe.addConfigHeader(config_header);
|
||||
exe.addCSourceFiles(&.{
|
||||
std.fmt.comptimePrint("examples/{s}/{s}.cpp", .{ example_name, example_name }),
|
||||
"examples/common.cpp",
|
||||
}, &.{"-std=c++11"});
|
||||
exe.linkLibrary(lib);
|
||||
b.installArtifact(exe);
|
||||
|
||||
const run_cmd = b.addRunArtifact(exe);
|
||||
run_cmd.step.dependOn(b.getInstallStep());
|
||||
if (b.args) |args| run_cmd.addArgs(args);
|
||||
|
||||
const run_step = b.step("run-" ++ example_name, "Run the app");
|
||||
run_step.dependOn(&run_cmd.step);
|
||||
const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, llama, common, grammar_parser });
|
||||
if (server.target.isWindows()) {
|
||||
server.linkSystemLibrary("ws2_32");
|
||||
}
|
||||
}
|
||||
|
||||
@@ -465,6 +465,13 @@ class GGMLQuantizedTensor(Tensor):
|
||||
def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'GGMLQuantizedTensor':
|
||||
return GGMLQuantizedTensor(permute(self.ndarray, n_head, n_kv_head), self.shape, self.data_type)
|
||||
|
||||
def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor':
|
||||
r = self.ndarray.shape[0] // 3
|
||||
return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head))
|
||||
|
||||
def part(self, n_part: int) -> 'UnquantizedTensor':
|
||||
r = self.ndarray.shape[0] // 3
|
||||
return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...])
|
||||
|
||||
GGMLCompatibleTensor = Union[UnquantizedTensor, GGMLQuantizedTensor]
|
||||
|
||||
|
||||
@@ -80,8 +80,10 @@ namespace console {
|
||||
// Set console input codepage to UTF16
|
||||
_setmode(_fileno(stdin), _O_WTEXT);
|
||||
|
||||
if (!simple_io) {
|
||||
// Turn off ICANON (ENABLE_LINE_INPUT) and ECHO (ENABLE_ECHO_INPUT)
|
||||
// Set ICANON (ENABLE_LINE_INPUT) and ECHO (ENABLE_ECHO_INPUT)
|
||||
if (simple_io) {
|
||||
dwMode |= ENABLE_LINE_INPUT | ENABLE_ECHO_INPUT;
|
||||
} else {
|
||||
dwMode &= ~(ENABLE_LINE_INPUT | ENABLE_ECHO_INPUT);
|
||||
}
|
||||
if (!SetConsoleMode(hConIn, dwMode)) {
|
||||
|
||||
@@ -87,289 +87,342 @@ unsigned char completion_js[] = {
|
||||
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0x20, 0x74, 0x68, 0x65, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74,
|
||||
0x20, 0x77, 0x69, 0x6e, 0x64, 0x6f, 0x77, 0x20, 0x61, 0x6e, 0x64, 0x20,
|
||||
0x73, 0x6f, 0x20, 0x6f, 0x6e, 0x2e, 0x0a, 0x65, 0x78, 0x70, 0x6f, 0x72,
|
||||
0x74, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6c, 0x6c, 0x61, 0x6d,
|
||||
0x61, 0x4d, 0x6f, 0x64, 0x65, 0x6c, 0x49, 0x6e, 0x66, 0x6f, 0x20, 0x3d,
|
||||
0x20, 0x61, 0x73, 0x79, 0x6e, 0x63, 0x20, 0x28, 0x29, 0x20, 0x3d, 0x3e,
|
||||
0x20, 0x7b, 0x0a, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x21, 0x67, 0x65,
|
||||
0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74,
|
||||
0x74, 0x69, 0x6e, 0x67, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20,
|
||||
0x20, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f,
|
||||
0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, 0x3d, 0x20, 0x61,
|
||||
0x77, 0x61, 0x69, 0x74, 0x20, 0x66, 0x65, 0x74, 0x63, 0x68, 0x28, 0x22,
|
||||
0x2f, 0x6d, 0x6f, 0x64, 0x65, 0x6c, 0x2e, 0x6a, 0x73, 0x6f, 0x6e, 0x22,
|
||||
0x29, 0x2e, 0x74, 0x68, 0x65, 0x6e, 0x28, 0x72, 0x20, 0x3d, 0x3e, 0x20,
|
||||
0x72, 0x2e, 0x6a, 0x73, 0x6f, 0x6e, 0x28, 0x29, 0x29, 0x3b, 0x0a, 0x20,
|
||||
0x20, 0x7d, 0x0a, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20,
|
||||
0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73,
|
||||
0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, 0x3d, 0x20, 0x61, 0x77,
|
||||
0x61, 0x69, 0x74, 0x20, 0x66, 0x65, 0x74, 0x63, 0x68, 0x28, 0x22, 0x2f,
|
||||
0x6d, 0x6f, 0x64, 0x65, 0x6c, 0x2e, 0x6a, 0x73, 0x6f, 0x6e, 0x22, 0x29,
|
||||
0x2e, 0x74, 0x68, 0x65, 0x6e, 0x28, 0x72, 0x20, 0x3d, 0x3e, 0x20, 0x72,
|
||||
0x2e, 0x6a, 0x73, 0x6f, 0x6e, 0x28, 0x29, 0x29, 0x3b, 0x0a, 0x20, 0x20,
|
||||
0x7d, 0x0a, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x67,
|
||||
0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65,
|
||||
0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x7d, 0x0a
|
||||
0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x7d, 0x0a
|
||||
};
|
||||
unsigned int completion_js_len = 4462;
|
||||
unsigned int completion_js_len = 5099;
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -282,8 +282,9 @@
|
||||
|
||||
useEffect(() => {
|
||||
// scroll to bottom (if needed)
|
||||
if (container.current && container.current.scrollHeight <= container.current.scrollTop + container.current.offsetHeight + 300) {
|
||||
container.current.scrollTo(0, container.current.scrollHeight)
|
||||
const parent = container.current.parentElement;
|
||||
if (parent && parent.scrollHeight <= parent.scrollTop + parent.offsetHeight + 300) {
|
||||
parent.scrollTo(0, parent.scrollHeight)
|
||||
}
|
||||
}, [messages])
|
||||
|
||||
|
||||
892
ggml-cuda.cu
892
ggml-cuda.cu
File diff suppressed because it is too large
Load Diff
57
ggml-metal.m
57
ggml-metal.m
@@ -7,6 +7,11 @@
|
||||
#import <Metal/Metal.h>
|
||||
#import <MetalPerformanceShaders/MetalPerformanceShaders.h>
|
||||
|
||||
#undef MIN
|
||||
#undef MAX
|
||||
#define MIN(a, b) ((a) < (b) ? (a) : (b))
|
||||
#define MAX(a, b) ((a) > (b) ? (a) : (b))
|
||||
|
||||
#ifdef GGML_METAL_NDEBUG
|
||||
#define metal_printf(...)
|
||||
#else
|
||||
@@ -15,6 +20,8 @@
|
||||
|
||||
#define UNUSED(x) (void)(x)
|
||||
|
||||
#define GGML_MAX_CONCUR (2*GGML_MAX_NODES)
|
||||
|
||||
struct ggml_metal_buffer {
|
||||
const char * name;
|
||||
|
||||
@@ -36,7 +43,7 @@ struct ggml_metal_context {
|
||||
int n_buffers;
|
||||
struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS];
|
||||
|
||||
int concur_list[GGML_MAX_NODES];
|
||||
int concur_list[GGML_MAX_CONCUR];
|
||||
int concur_list_len;
|
||||
|
||||
// custom kernels
|
||||
@@ -370,15 +377,15 @@ void ggml_metal_graph_find_concurrency(
|
||||
struct ggml_metal_context * ctx,
|
||||
struct ggml_cgraph * gf) {
|
||||
int search_depth = gf->n_nodes; //we only find concurrency in this range to avoid wasting too much time
|
||||
int nodes_unused[GGML_MAX_NODES];
|
||||
int nodes_unused[GGML_MAX_CONCUR];
|
||||
|
||||
for (int i = 0; i < GGML_MAX_NODES; i++) {ctx->concur_list[i] = 0;}
|
||||
for (int i = 0; i < gf->n_nodes; i++) {nodes_unused[i] = 1;}
|
||||
for (int i = 0; i < GGML_MAX_CONCUR; i++) { ctx->concur_list[i] = 0; }
|
||||
for (int i = 0; i < gf->n_nodes; i++) { nodes_unused[i] = 1; }
|
||||
ctx->concur_list_len = 0;
|
||||
|
||||
int n_left = gf->n_nodes;
|
||||
int n_start = 0; // all nodes before n_start at nodes_unused array have been sorted and store back to ctx->concur_list
|
||||
int level_pos = 0; // at ctx->concur_list, the last layer (level) ends at level_pos
|
||||
int n_left = gf->n_nodes;
|
||||
int n_start = 0; // all nodes before n_start at nodes_unused array have been sorted and store back to ctx->concur_list
|
||||
int level_pos = 0; // at ctx->concur_list, the last layer (level) ends at level_pos
|
||||
|
||||
while (n_left > 0) {
|
||||
// number of nodes at a layer (that can be issued concurrently)
|
||||
@@ -386,28 +393,40 @@ void ggml_metal_graph_find_concurrency(
|
||||
for (int i = n_start; i < ((n_start + search_depth > gf->n_nodes) ? gf->n_nodes : n_start + search_depth); i++) {
|
||||
if (nodes_unused[i]) {
|
||||
// if the requirements for gf->nodes[i] are satisfied
|
||||
int exe_flag=1;
|
||||
int exe_flag = 1;
|
||||
|
||||
// scan all srcs
|
||||
for (int src_ind = 0; src_ind < GGML_MAX_SRC; src_ind++) {
|
||||
struct ggml_tensor * src_cur = gf->nodes[i]->src[src_ind];
|
||||
if (src_cur) {
|
||||
// if is leaf nodes it's satisfied.
|
||||
if (src_cur->op == GGML_OP_NONE && src_cur->grad == NULL) {continue;}
|
||||
// TODO: ggml_is_leaf()
|
||||
if (src_cur->op == GGML_OP_NONE && src_cur->grad == NULL) {
|
||||
continue;
|
||||
}
|
||||
|
||||
// otherwise this src should be the output from previous nodes.
|
||||
int is_found = 0;
|
||||
|
||||
// scan 2*search_depth back because we inserted barrier.
|
||||
for (int j = ((level_pos - 2*search_depth) < 0 ? 0 : (level_pos - 2*search_depth)); j < level_pos; j++) {
|
||||
if (gf->nodes[ctx->concur_list[j]] == src_cur) {is_found = 1; break;}
|
||||
//for (int j = ((level_pos - 2*search_depth) < 0 ? 0 : (level_pos - 2*search_depth)); j < level_pos; j++) {
|
||||
for (int j = MAX(0, level_pos - 2*search_depth); j < level_pos; j++) {
|
||||
if (ctx->concur_list[j] >= 0 && gf->nodes[ctx->concur_list[j]] == src_cur) {
|
||||
is_found = 1;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (is_found == 0) {
|
||||
exe_flag = 0;
|
||||
break;
|
||||
}
|
||||
if (is_found == 0) {exe_flag = 0; break;}
|
||||
}
|
||||
}
|
||||
if (exe_flag) {
|
||||
// check if nodes[i]'s data will be overwritten by a node before nodes[i].
|
||||
// if node[5] and node[3] write to the same memory region, then we can't issue node[5] before node[3]
|
||||
int64_t data_start = (int64_t) gf->nodes[i]->data;
|
||||
int64_t length = (int64_t) ggml_nbytes(gf->nodes[i]);
|
||||
int64_t length = (int64_t) ggml_nbytes(gf->nodes[i]);
|
||||
for (int j = n_start; j < i; j++) {
|
||||
if (nodes_unused[j] && gf->nodes[j]->op != GGML_OP_RESHAPE \
|
||||
&& gf->nodes[j]->op != GGML_OP_VIEW \
|
||||
@@ -416,9 +435,9 @@ void ggml_metal_graph_find_concurrency(
|
||||
if (((int64_t)gf->nodes[j]->data) >= data_start + length || \
|
||||
((int64_t)gf->nodes[j]->data) + (int64_t) ggml_nbytes(gf->nodes[j]) <= data_start) {
|
||||
continue;
|
||||
} else {
|
||||
exe_flag = 0;
|
||||
}
|
||||
|
||||
exe_flag = 0;
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -435,11 +454,13 @@ void ggml_metal_graph_find_concurrency(
|
||||
ctx->concur_list[level_pos + concurrency] = -1;
|
||||
ctx->concur_list_len++;
|
||||
// jump all sorted nodes at nodes_bak
|
||||
while (!nodes_unused[n_start]) {n_start++;}
|
||||
while (!nodes_unused[n_start]) {
|
||||
n_start++;
|
||||
}
|
||||
level_pos += concurrency + 1;
|
||||
}
|
||||
|
||||
if (ctx->concur_list_len > GGML_MAX_NODES) {
|
||||
if (ctx->concur_list_len > GGML_MAX_CONCUR) {
|
||||
fprintf(stderr, "%s: too many elements for metal ctx->concur_list!\n", __func__);
|
||||
}
|
||||
}
|
||||
@@ -453,7 +474,7 @@ void ggml_metal_graph_compute(
|
||||
// else fallback to serial dispatch
|
||||
MTLComputePassDescriptor * edesc = MTLComputePassDescriptor.computePassDescriptor;
|
||||
|
||||
const bool has_concur = ctx->concur_list_len && ctx->concur_list_len <= GGML_MAX_NODES;
|
||||
const bool has_concur = ctx->concur_list_len && ctx->concur_list_len <= GGML_MAX_CONCUR;
|
||||
|
||||
const int n_nodes = has_concur ? ctx->concur_list_len : gf->n_nodes;
|
||||
edesc.dispatchType = has_concur ? MTLDispatchTypeConcurrent : MTLDispatchTypeSerial;
|
||||
|
||||
418
ggml.c
418
ggml.c
@@ -195,8 +195,8 @@ typedef void * thread_ret_t;
|
||||
#define GGML_ALIGNED_MALLOC(size) _aligned_malloc(size, GGML_MEM_ALIGN)
|
||||
#define GGML_ALIGNED_FREE(ptr) _aligned_free(ptr)
|
||||
#else
|
||||
inline static void* ggml_aligned_malloc(size_t size) {
|
||||
void* aligned_memory = NULL;
|
||||
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);
|
||||
#else
|
||||
@@ -3811,7 +3811,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
|
||||
"CROSS_ENTROPY_LOSS_BACK",
|
||||
};
|
||||
|
||||
static_assert(GGML_OP_COUNT == 59, "GGML_OP_COUNT != 59");
|
||||
static_assert(GGML_OP_COUNT == 62, "GGML_OP_COUNT != 62");
|
||||
|
||||
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
|
||||
"none",
|
||||
@@ -3883,7 +3883,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
|
||||
"cross_entropy_loss_back(x,y)",
|
||||
};
|
||||
|
||||
static_assert(GGML_OP_COUNT == 59, "GGML_OP_COUNT != 59");
|
||||
static_assert(GGML_OP_COUNT == 62, "GGML_OP_COUNT != 62");
|
||||
|
||||
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
|
||||
|
||||
@@ -4253,7 +4253,7 @@ static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) {
|
||||
tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
|
||||
}
|
||||
|
||||
static inline bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
|
||||
bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
|
||||
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
||||
|
||||
return
|
||||
@@ -4602,7 +4602,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
|
||||
/*.ne =*/ { 1, 1, 1, 1 },
|
||||
/*.nb =*/ { 0, 0, 0, 0 },
|
||||
/*.op =*/ GGML_OP_NONE,
|
||||
/*.op_params =*/ {0},
|
||||
/*.op_params =*/ { 0 },
|
||||
/*.is_param =*/ false,
|
||||
/*.grad =*/ NULL,
|
||||
/*.src =*/ { NULL },
|
||||
@@ -4634,6 +4634,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
|
||||
}
|
||||
|
||||
static void ggml_set_op_params(struct ggml_tensor * tensor, const void * params, size_t params_size) {
|
||||
GGML_ASSERT(tensor != NULL); // silence -Warray-bounds warnings
|
||||
assert(params_size <= GGML_MAX_OP_PARAMS);
|
||||
memcpy(tensor->op_params, params, params_size);
|
||||
}
|
||||
@@ -6439,7 +6440,7 @@ struct ggml_tensor * ggml_permute(
|
||||
result->src[0] = a;
|
||||
|
||||
int32_t params[] = { axis0, axis1, axis2, axis3 };
|
||||
ggml_set_op_params(result, ¶ms, sizeof(params));
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
return result;
|
||||
}
|
||||
@@ -6565,7 +6566,7 @@ static struct ggml_tensor * ggml_diag_mask_inf_impl(
|
||||
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
||||
|
||||
int32_t params[] = { n_past, inplace ? 1 : 0 };
|
||||
ggml_set_op_params(result, ¶ms, sizeof(params));
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_DIAG_MASK_INF;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
@@ -6605,7 +6606,7 @@ static struct ggml_tensor * ggml_diag_mask_zero_impl(
|
||||
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
||||
|
||||
int32_t params[] = { n_past, inplace ? 1 : 0 };
|
||||
ggml_set_op_params(result, ¶ms, sizeof(params));
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_DIAG_MASK_ZERO;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
@@ -6721,9 +6722,9 @@ static struct ggml_tensor * ggml_rope_impl(
|
||||
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
||||
|
||||
int32_t params[6] = { n_past, n_dims, mode, n_ctx };
|
||||
memcpy(params + 4, &freq_base, sizeof(float));
|
||||
memcpy(params + 4, &freq_base, sizeof(float));
|
||||
memcpy(params + 5, &freq_scale, sizeof(float));
|
||||
ggml_set_op_params(result, ¶ms, sizeof(params));
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_ROPE;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
@@ -6797,7 +6798,7 @@ struct ggml_tensor * ggml_rope_back(
|
||||
struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
|
||||
|
||||
int32_t params[] = { n_past, n_dims, mode, n_ctx };
|
||||
ggml_set_op_params(result, ¶ms, sizeof(params));
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_ROPE_BACK;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
@@ -6828,7 +6829,7 @@ struct ggml_tensor * ggml_alibi(
|
||||
|
||||
int32_t op_params[3] = { n_past, n_head };
|
||||
memcpy(op_params + 2, &bias_max, sizeof(float));
|
||||
ggml_set_op_params(result, &op_params, sizeof(op_params));
|
||||
ggml_set_op_params(result, op_params, sizeof(op_params));
|
||||
|
||||
result->op = GGML_OP_ALIBI;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
@@ -6855,7 +6856,7 @@ struct ggml_tensor * ggml_clamp(
|
||||
struct ggml_tensor * result = ggml_view_tensor(ctx, a);
|
||||
|
||||
float params[] = { min, max };
|
||||
ggml_set_op_params(result, ¶ms, sizeof(params));
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_CLAMP;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
@@ -6890,10 +6891,10 @@ GGML_API struct ggml_tensor * ggml_conv_1d(
|
||||
ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0),
|
||||
a->ne[2], 1, 1,
|
||||
};
|
||||
struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
|
||||
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
|
||||
|
||||
int32_t params[] = { s0, p0, d0 };
|
||||
ggml_set_op_params(result, ¶ms, sizeof(params));
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_CONV_1D;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
@@ -6905,10 +6906,10 @@ GGML_API struct ggml_tensor * ggml_conv_1d(
|
||||
|
||||
// ggml_conv_2d
|
||||
|
||||
struct ggml_tensor* ggml_conv_2d(
|
||||
struct ggml_context* ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
struct ggml_tensor * ggml_conv_2d(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
int s0,
|
||||
int s1,
|
||||
int p0,
|
||||
@@ -6929,10 +6930,10 @@ struct ggml_tensor* ggml_conv_2d(
|
||||
ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1),
|
||||
a->ne[3], b->ne[3],
|
||||
};
|
||||
struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
|
||||
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
|
||||
|
||||
int32_t params[] = { s0, s1, p0, p1, d0, d1 };
|
||||
ggml_set_op_params(result, ¶ms, sizeof(params));
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_CONV_2D;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
@@ -6945,7 +6946,7 @@ struct ggml_tensor* ggml_conv_2d(
|
||||
|
||||
// ggml_conv_1d_ph
|
||||
|
||||
struct ggml_tensor* ggml_conv_1d_ph(
|
||||
struct ggml_tensor * ggml_conv_1d_ph(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
@@ -6963,7 +6964,7 @@ static int64_t ggml_calc_pool_output_size(int64_t ins, int ks, int s, int p) {
|
||||
|
||||
// ggml_pool_1d
|
||||
|
||||
struct ggml_tensor* ggml_pool_1d(
|
||||
struct ggml_tensor * ggml_pool_1d(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
enum ggml_op_pool op,
|
||||
@@ -6982,10 +6983,10 @@ struct ggml_tensor* ggml_pool_1d(
|
||||
ggml_calc_pool_output_size(a->ne[0], k0, s0, p0),
|
||||
a->ne[1],
|
||||
};
|
||||
struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
|
||||
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
|
||||
|
||||
int32_t params[] = { op, k0, s0, p0 };
|
||||
ggml_set_op_params(result, ¶ms, sizeof(params));
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_POOL_1D;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
@@ -6996,7 +6997,7 @@ struct ggml_tensor* ggml_pool_1d(
|
||||
|
||||
// ggml_pool_2d
|
||||
|
||||
struct ggml_tensor* ggml_pool_2d(
|
||||
struct ggml_tensor * ggml_pool_2d(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
enum ggml_op_pool op,
|
||||
@@ -7019,10 +7020,10 @@ struct ggml_tensor* ggml_pool_2d(
|
||||
ggml_calc_pool_output_size(a->ne[1], k1, s1, p1),
|
||||
a->ne[2],
|
||||
};
|
||||
struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne);
|
||||
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne);
|
||||
|
||||
int32_t params[] = { op, k0, k1, s0, s1, p0, p1 };
|
||||
ggml_set_op_params(result, ¶ms, sizeof(params));
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_POOL_2D;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
@@ -7190,7 +7191,7 @@ struct ggml_tensor * ggml_win_part(
|
||||
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
|
||||
|
||||
int32_t params[] = { npx, npy, w };
|
||||
ggml_set_op_params(result, ¶ms, sizeof(params));
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_WIN_PART;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
@@ -7220,7 +7221,7 @@ struct ggml_tensor * ggml_win_unpart(
|
||||
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne);
|
||||
|
||||
int32_t params[] = { w };
|
||||
ggml_set_op_params(result, ¶ms, sizeof(params));
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_WIN_UNPART;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
@@ -7349,7 +7350,7 @@ struct ggml_tensor * ggml_map_binary_inplace_f32(
|
||||
return ggml_map_binary_impl_f32(ctx, a, b, fun, true);
|
||||
}
|
||||
|
||||
// ggml_map_custom1
|
||||
// ggml_map_custom1_f32
|
||||
|
||||
static struct ggml_tensor * ggml_map_custom1_impl_f32(
|
||||
struct ggml_context * ctx,
|
||||
@@ -7366,7 +7367,7 @@ static struct ggml_tensor * ggml_map_custom1_impl_f32(
|
||||
|
||||
ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
|
||||
|
||||
result->op = GGML_OP_MAP_CUSTOM1;
|
||||
result->op = GGML_OP_MAP_CUSTOM1_F32;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
result->src[0] = a;
|
||||
|
||||
@@ -7387,7 +7388,7 @@ struct ggml_tensor * ggml_map_custom1_inplace_f32(
|
||||
return ggml_map_custom1_impl_f32(ctx, a, fun, true);
|
||||
}
|
||||
|
||||
// ggml_map_custom2
|
||||
// ggml_map_custom2_f32
|
||||
|
||||
static struct ggml_tensor * ggml_map_custom2_impl_f32(
|
||||
struct ggml_context * ctx,
|
||||
@@ -7405,7 +7406,7 @@ static struct ggml_tensor * ggml_map_custom2_impl_f32(
|
||||
|
||||
ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
|
||||
|
||||
result->op = GGML_OP_MAP_CUSTOM2;
|
||||
result->op = GGML_OP_MAP_CUSTOM2_F32;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
result->src[0] = a;
|
||||
result->src[1] = b;
|
||||
@@ -7429,7 +7430,7 @@ struct ggml_tensor * ggml_map_custom2_inplace_f32(
|
||||
return ggml_map_custom2_impl_f32(ctx, a, b, fun, true);
|
||||
}
|
||||
|
||||
// ggml_map_custom3
|
||||
// ggml_map_custom3_f32
|
||||
|
||||
static struct ggml_tensor * ggml_map_custom3_impl_f32(
|
||||
struct ggml_context * ctx,
|
||||
@@ -7448,7 +7449,7 @@ static struct ggml_tensor * ggml_map_custom3_impl_f32(
|
||||
|
||||
ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
|
||||
|
||||
result->op = GGML_OP_MAP_CUSTOM3;
|
||||
result->op = GGML_OP_MAP_CUSTOM3_F32;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
result->src[0] = a;
|
||||
result->src[1] = b;
|
||||
@@ -7475,6 +7476,190 @@ struct ggml_tensor * ggml_map_custom3_inplace_f32(
|
||||
return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, true);
|
||||
}
|
||||
|
||||
// ggml_map_custom1
|
||||
struct ggml_map_custom1_op_params {
|
||||
ggml_custom1_op_t fun;
|
||||
int n_tasks;
|
||||
void * userdata;
|
||||
};
|
||||
|
||||
static struct ggml_tensor * ggml_map_custom1_impl(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
const ggml_custom1_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata,
|
||||
bool inplace) {
|
||||
GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0);
|
||||
|
||||
bool is_node = false;
|
||||
|
||||
if (!inplace && a->grad) {
|
||||
is_node = true;
|
||||
}
|
||||
|
||||
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
||||
|
||||
struct ggml_map_custom1_op_params params = {
|
||||
/*.fun =*/ fun,
|
||||
/*.n_tasks =*/ n_tasks,
|
||||
/*.userdata =*/ userdata
|
||||
};
|
||||
ggml_set_op_params(result, (const void *) ¶ms, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_MAP_CUSTOM1;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
result->src[0] = a;
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_map_custom1(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
const ggml_custom1_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata) {
|
||||
return ggml_map_custom1_impl(ctx, a, fun, n_tasks, userdata, false);
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_map_custom1_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
const ggml_custom1_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata) {
|
||||
return ggml_map_custom1_impl(ctx, a, fun, n_tasks, userdata, true);
|
||||
}
|
||||
|
||||
// ggml_map_custom2
|
||||
|
||||
struct ggml_map_custom2_op_params {
|
||||
ggml_custom2_op_t fun;
|
||||
int n_tasks;
|
||||
void * userdata;
|
||||
};
|
||||
|
||||
static struct ggml_tensor * ggml_map_custom2_impl(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
const ggml_custom2_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata,
|
||||
bool inplace) {
|
||||
GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0);
|
||||
|
||||
bool is_node = false;
|
||||
|
||||
if (!inplace && (a->grad || b->grad)) {
|
||||
is_node = true;
|
||||
}
|
||||
|
||||
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
||||
|
||||
struct ggml_map_custom2_op_params params = {
|
||||
/*.fun =*/ fun,
|
||||
/*.n_tasks =*/ n_tasks,
|
||||
/*.userdata =*/ userdata
|
||||
};
|
||||
ggml_set_op_params(result, (const void *) ¶ms, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_MAP_CUSTOM2;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
result->src[0] = a;
|
||||
result->src[1] = b;
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_map_custom2(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
const ggml_custom2_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata) {
|
||||
return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, false);
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_map_custom2_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
const ggml_custom2_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata) {
|
||||
return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, true);
|
||||
}
|
||||
|
||||
// ggml_map_custom3
|
||||
|
||||
struct ggml_map_custom3_op_params {
|
||||
ggml_custom3_op_t fun;
|
||||
int n_tasks;
|
||||
void * userdata;
|
||||
};
|
||||
|
||||
static struct ggml_tensor * ggml_map_custom3_impl(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
struct ggml_tensor * c,
|
||||
const ggml_custom3_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata,
|
||||
bool inplace) {
|
||||
GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0);
|
||||
|
||||
bool is_node = false;
|
||||
|
||||
if (!inplace && (a->grad || b->grad || c->grad)) {
|
||||
is_node = true;
|
||||
}
|
||||
|
||||
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
||||
|
||||
struct ggml_map_custom3_op_params params = {
|
||||
/*.fun =*/ fun,
|
||||
/*.n_tasks =*/ n_tasks,
|
||||
/*.userdata =*/ userdata
|
||||
};
|
||||
ggml_set_op_params(result, (const void *) ¶ms, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_MAP_CUSTOM3;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
result->src[0] = a;
|
||||
result->src[1] = b;
|
||||
result->src[2] = c;
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_map_custom3(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
struct ggml_tensor * c,
|
||||
const ggml_custom3_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata) {
|
||||
return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, false);
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_map_custom3_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
struct ggml_tensor * c,
|
||||
const ggml_custom3_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata) {
|
||||
return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, true);
|
||||
}
|
||||
|
||||
|
||||
|
||||
// ggml_cross_entropy_loss
|
||||
|
||||
struct ggml_tensor * ggml_cross_entropy_loss(
|
||||
@@ -9283,8 +9468,8 @@ static void ggml_compute_forward_sum_rows_f32(
|
||||
for (int64_t i3 = 0; i3 < ne03; i3++) {
|
||||
for (int64_t i2 = 0; i2 < ne02; i2++) {
|
||||
for (int64_t i1 = 0; i1 < ne01; i1++) {
|
||||
float* src_row = (float *) ((char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03);
|
||||
float* dst_row = (float *) ((char *) dst->data + i1*nb1 + i2*nb2 + i3*nb3);
|
||||
float * src_row = (float *) ((char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03);
|
||||
float * dst_row = (float *) ((char *) dst->data + i1*nb1 + i2*nb2 + i3*nb3);
|
||||
float row_sum = 0;
|
||||
ggml_vec_sum_f32(ne00, &row_sum, src_row);
|
||||
dst_row[0] = row_sum;
|
||||
@@ -12894,7 +13079,7 @@ static void ggml_compute_forward_pool_1d(
|
||||
const struct ggml_tensor * src0,
|
||||
struct ggml_tensor * dst) {
|
||||
|
||||
const int32_t* opts = (const int32_t*)dst->op_params;
|
||||
const int32_t * opts = (const int32_t *)dst->op_params;
|
||||
enum ggml_op_pool op = opts[0];
|
||||
const int k0 = opts[1];
|
||||
const int s0 = opts[2];
|
||||
@@ -14227,24 +14412,6 @@ static void ggml_compute_forward_map_custom1_f32(
|
||||
fun(dst, a);
|
||||
}
|
||||
|
||||
|
||||
static void ggml_compute_forward_map_custom1(
|
||||
const struct ggml_compute_params * params,
|
||||
const struct ggml_tensor * a,
|
||||
struct ggml_tensor * dst,
|
||||
const ggml_custom1_op_f32_t fun) {
|
||||
switch (a->type) {
|
||||
case GGML_TYPE_F32:
|
||||
{
|
||||
ggml_compute_forward_map_custom1_f32(params, a, dst, fun);
|
||||
} break;
|
||||
default:
|
||||
{
|
||||
GGML_ASSERT(false);
|
||||
} break;
|
||||
}
|
||||
}
|
||||
|
||||
// ggml_compute_forward_map_custom2
|
||||
|
||||
static void ggml_compute_forward_map_custom2_f32(
|
||||
@@ -14263,24 +14430,6 @@ static void ggml_compute_forward_map_custom2_f32(
|
||||
}
|
||||
|
||||
|
||||
static void ggml_compute_forward_map_custom2(
|
||||
const struct ggml_compute_params * params,
|
||||
const struct ggml_tensor * a,
|
||||
const struct ggml_tensor * b,
|
||||
struct ggml_tensor * dst,
|
||||
const ggml_custom2_op_f32_t fun) {
|
||||
switch (a->type) {
|
||||
case GGML_TYPE_F32:
|
||||
{
|
||||
ggml_compute_forward_map_custom2_f32(params, a, b, dst, fun);
|
||||
} break;
|
||||
default:
|
||||
{
|
||||
GGML_ASSERT(false);
|
||||
} break;
|
||||
}
|
||||
}
|
||||
|
||||
// ggml_compute_forward_map_custom3
|
||||
|
||||
static void ggml_compute_forward_map_custom3_f32(
|
||||
@@ -14299,24 +14448,52 @@ static void ggml_compute_forward_map_custom3_f32(
|
||||
fun(dst, a, b, c);
|
||||
}
|
||||
|
||||
// ggml_compute_forward_map_custom1
|
||||
|
||||
static void ggml_compute_forward_map_custom1(
|
||||
const struct ggml_compute_params * params,
|
||||
const struct ggml_tensor * a,
|
||||
struct ggml_tensor * dst) {
|
||||
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
||||
return;
|
||||
}
|
||||
|
||||
struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) dst->op_params;
|
||||
|
||||
p->fun(dst, a, params->ith, params->nth, p->userdata);
|
||||
}
|
||||
|
||||
// ggml_compute_forward_map_custom2
|
||||
|
||||
static void ggml_compute_forward_map_custom2(
|
||||
const struct ggml_compute_params * params,
|
||||
const struct ggml_tensor * a,
|
||||
const struct ggml_tensor * b,
|
||||
struct ggml_tensor * dst) {
|
||||
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
||||
return;
|
||||
}
|
||||
|
||||
struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) dst->op_params;
|
||||
|
||||
p->fun(dst, a, b, params->ith, params->nth, p->userdata);
|
||||
}
|
||||
|
||||
// ggml_compute_forward_map_custom3
|
||||
|
||||
static void ggml_compute_forward_map_custom3(
|
||||
const struct ggml_compute_params * params,
|
||||
const struct ggml_tensor * a,
|
||||
const struct ggml_tensor * b,
|
||||
const struct ggml_tensor * c,
|
||||
struct ggml_tensor * dst,
|
||||
const ggml_custom3_op_f32_t fun) {
|
||||
switch (a->type) {
|
||||
case GGML_TYPE_F32:
|
||||
{
|
||||
ggml_compute_forward_map_custom3_f32(params, a, b, c, dst, fun);
|
||||
} break;
|
||||
default:
|
||||
{
|
||||
GGML_ASSERT(false);
|
||||
} break;
|
||||
struct ggml_tensor * dst) {
|
||||
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
||||
return;
|
||||
}
|
||||
|
||||
struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) dst->op_params;
|
||||
|
||||
p->fun(dst, a, b, c, params->ith, params->nth, p->userdata);
|
||||
}
|
||||
|
||||
// ggml_compute_forward_cross_entropy_loss
|
||||
@@ -14838,25 +15015,40 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
|
||||
ggml_compute_forward_map_binary(params, tensor->src[0], tensor->src[1], tensor, fun);
|
||||
}
|
||||
break;
|
||||
case GGML_OP_MAP_CUSTOM1:
|
||||
case GGML_OP_MAP_CUSTOM1_F32:
|
||||
{
|
||||
ggml_custom1_op_f32_t fun;
|
||||
memcpy(&fun, tensor->op_params, sizeof(fun));
|
||||
ggml_compute_forward_map_custom1(params, tensor->src[0], tensor, fun);
|
||||
ggml_compute_forward_map_custom1_f32(params, tensor->src[0], tensor, fun);
|
||||
}
|
||||
break;
|
||||
case GGML_OP_MAP_CUSTOM2_F32:
|
||||
{
|
||||
ggml_custom2_op_f32_t fun;
|
||||
memcpy(&fun, tensor->op_params, sizeof(fun));
|
||||
ggml_compute_forward_map_custom2_f32(params, tensor->src[0], tensor->src[1], tensor, fun);
|
||||
}
|
||||
break;
|
||||
case GGML_OP_MAP_CUSTOM3_F32:
|
||||
{
|
||||
ggml_custom3_op_f32_t fun;
|
||||
memcpy(&fun, tensor->op_params, sizeof(fun));
|
||||
ggml_compute_forward_map_custom3_f32(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor, fun);
|
||||
}
|
||||
break;
|
||||
case GGML_OP_MAP_CUSTOM1:
|
||||
{
|
||||
ggml_compute_forward_map_custom1(params, tensor->src[0], tensor);
|
||||
}
|
||||
break;
|
||||
case GGML_OP_MAP_CUSTOM2:
|
||||
{
|
||||
ggml_custom2_op_f32_t fun;
|
||||
memcpy(&fun, tensor->op_params, sizeof(fun));
|
||||
ggml_compute_forward_map_custom2(params, tensor->src[0], tensor->src[1], tensor, fun);
|
||||
ggml_compute_forward_map_custom2(params, tensor->src[0], tensor->src[1], tensor);
|
||||
}
|
||||
break;
|
||||
case GGML_OP_MAP_CUSTOM3:
|
||||
{
|
||||
ggml_custom3_op_f32_t fun;
|
||||
memcpy(&fun, tensor->op_params, sizeof(fun));
|
||||
ggml_compute_forward_map_custom3(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor, fun);
|
||||
ggml_compute_forward_map_custom3(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
|
||||
}
|
||||
break;
|
||||
case GGML_OP_CROSS_ENTROPY_LOSS:
|
||||
@@ -15664,6 +15856,9 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
||||
} break;
|
||||
case GGML_OP_MAP_UNARY:
|
||||
case GGML_OP_MAP_BINARY:
|
||||
case GGML_OP_MAP_CUSTOM1_F32:
|
||||
case GGML_OP_MAP_CUSTOM2_F32:
|
||||
case GGML_OP_MAP_CUSTOM3_F32:
|
||||
case GGML_OP_MAP_CUSTOM1:
|
||||
case GGML_OP_MAP_CUSTOM2:
|
||||
case GGML_OP_MAP_CUSTOM3:
|
||||
@@ -16449,12 +16644,39 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
|
||||
case GGML_OP_WIN_UNPART:
|
||||
case GGML_OP_MAP_UNARY:
|
||||
case GGML_OP_MAP_BINARY:
|
||||
case GGML_OP_MAP_CUSTOM1:
|
||||
case GGML_OP_MAP_CUSTOM2:
|
||||
case GGML_OP_MAP_CUSTOM3:
|
||||
case GGML_OP_MAP_CUSTOM1_F32:
|
||||
case GGML_OP_MAP_CUSTOM2_F32:
|
||||
case GGML_OP_MAP_CUSTOM3_F32:
|
||||
{
|
||||
n_tasks = 1;
|
||||
} break;
|
||||
case GGML_OP_MAP_CUSTOM1:
|
||||
{
|
||||
struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) node->op_params;
|
||||
if (p->n_tasks == GGML_N_TASKS_MAX) {
|
||||
n_tasks = n_threads;
|
||||
} else {
|
||||
n_tasks = MIN(p->n_tasks, n_threads);
|
||||
}
|
||||
} break;
|
||||
case GGML_OP_MAP_CUSTOM2:
|
||||
{
|
||||
struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) node->op_params;
|
||||
if (p->n_tasks == GGML_N_TASKS_MAX) {
|
||||
n_tasks = n_threads;
|
||||
} else {
|
||||
n_tasks = MIN(p->n_tasks, n_threads);
|
||||
}
|
||||
} break;
|
||||
case GGML_OP_MAP_CUSTOM3:
|
||||
{
|
||||
struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) node->op_params;
|
||||
if (p->n_tasks == GGML_N_TASKS_MAX) {
|
||||
n_tasks = n_threads;
|
||||
} else {
|
||||
n_tasks = MIN(p->n_tasks, n_threads);
|
||||
}
|
||||
} break;
|
||||
case GGML_OP_CROSS_ENTROPY_LOSS:
|
||||
{
|
||||
n_tasks = n_threads;
|
||||
|
||||
145
ggml.h
145
ggml.h
@@ -183,6 +183,15 @@
|
||||
# define GGML_API
|
||||
#endif
|
||||
|
||||
// TODO: support for clang
|
||||
#ifdef __GNUC__
|
||||
# define GGML_DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
|
||||
#elif defined(_MSC_VER)
|
||||
# define GGML_DEPRECATED(func, hint) __declspec(deprecated(hint)) func
|
||||
#else
|
||||
# define GGML_DEPRECATED(func, hint) func
|
||||
#endif
|
||||
|
||||
#include <stdint.h>
|
||||
#include <stddef.h>
|
||||
#include <stdbool.h>
|
||||
@@ -374,6 +383,10 @@ extern "C" {
|
||||
GGML_OP_MAP_UNARY,
|
||||
GGML_OP_MAP_BINARY,
|
||||
|
||||
GGML_OP_MAP_CUSTOM1_F32,
|
||||
GGML_OP_MAP_CUSTOM2_F32,
|
||||
GGML_OP_MAP_CUSTOM3_F32,
|
||||
|
||||
GGML_OP_MAP_CUSTOM1,
|
||||
GGML_OP_MAP_CUSTOM2,
|
||||
GGML_OP_MAP_CUSTOM3,
|
||||
@@ -570,6 +583,8 @@ extern "C" {
|
||||
GGML_API bool ggml_is_contiguous(const struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_is_permuted (const struct ggml_tensor * tensor);
|
||||
|
||||
GGML_API bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1);
|
||||
|
||||
// use this to compute the memory overhead of a tensor
|
||||
GGML_API size_t ggml_tensor_overhead(void);
|
||||
|
||||
@@ -1240,7 +1255,7 @@ extern "C" {
|
||||
|
||||
// conv_1d with padding = half
|
||||
// alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
|
||||
GGML_API struct ggml_tensor* ggml_conv_1d_ph(
|
||||
GGML_API struct ggml_tensor * ggml_conv_1d_ph(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
@@ -1253,7 +1268,7 @@ extern "C" {
|
||||
GGML_OP_POOL_COUNT,
|
||||
};
|
||||
|
||||
GGML_API struct ggml_tensor* ggml_pool_1d(
|
||||
GGML_API struct ggml_tensor * ggml_pool_1d(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
enum ggml_op_pool op,
|
||||
@@ -1261,7 +1276,7 @@ extern "C" {
|
||||
int s0, // stride
|
||||
int p0); // padding
|
||||
|
||||
GGML_API struct ggml_tensor* ggml_pool_2d(
|
||||
GGML_API struct ggml_tensor * ggml_pool_2d(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
enum ggml_op_pool op,
|
||||
@@ -1315,15 +1330,6 @@ extern "C" {
|
||||
int h0,
|
||||
int w);
|
||||
|
||||
// custom operators
|
||||
|
||||
typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
|
||||
typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
|
||||
|
||||
typedef void (*ggml_custom1_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *);
|
||||
typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
|
||||
typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_unary(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
@@ -1334,63 +1340,138 @@ extern "C" {
|
||||
struct ggml_tensor * a,
|
||||
enum ggml_unary_op op);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_unary_f32(
|
||||
// custom operators
|
||||
|
||||
typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
|
||||
typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
|
||||
|
||||
typedef void (*ggml_custom1_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *);
|
||||
typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
|
||||
typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
|
||||
|
||||
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
ggml_unary_op_f32_t fun);
|
||||
ggml_unary_op_f32_t fun),
|
||||
"use ggml_map_custom1 instead");
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
|
||||
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
ggml_unary_op_f32_t fun);
|
||||
ggml_unary_op_f32_t fun),
|
||||
"use ggml_map_custom1_inplace instead");
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_binary_f32(
|
||||
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
ggml_binary_op_f32_t fun);
|
||||
ggml_binary_op_f32_t fun),
|
||||
"use ggml_map_custom2 instead");
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
|
||||
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
ggml_binary_op_f32_t fun);
|
||||
ggml_binary_op_f32_t fun),
|
||||
"use ggml_map_custom2_inplace instead");
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom1_f32(
|
||||
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
ggml_custom1_op_f32_t fun);
|
||||
ggml_custom1_op_f32_t fun),
|
||||
"use ggml_map_custom1 instead");
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
|
||||
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
ggml_custom1_op_f32_t fun);
|
||||
ggml_custom1_op_f32_t fun),
|
||||
"use ggml_map_custom1_inplace instead");
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom2_f32(
|
||||
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
ggml_custom2_op_f32_t fun);
|
||||
ggml_custom2_op_f32_t fun),
|
||||
"use ggml_map_custom2 instead");
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
|
||||
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
ggml_custom2_op_f32_t fun);
|
||||
ggml_custom2_op_f32_t fun),
|
||||
"use ggml_map_custom2_inplace instead");
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom3_f32(
|
||||
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
struct ggml_tensor * c,
|
||||
ggml_custom3_op_f32_t fun);
|
||||
ggml_custom3_op_f32_t fun),
|
||||
"use ggml_map_custom3 instead");
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
|
||||
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
struct ggml_tensor * c,
|
||||
ggml_custom3_op_f32_t fun);
|
||||
ggml_custom3_op_f32_t fun),
|
||||
"use ggml_map_custom3_inplace instead");
|
||||
|
||||
// custom operators v2
|
||||
|
||||
typedef void (*ggml_custom1_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, int ith, int nth, void * userdata);
|
||||
typedef void (*ggml_custom2_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, int ith, int nth, void * userdata);
|
||||
typedef void (*ggml_custom3_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, const struct ggml_tensor * c, int ith, int nth, void * userdata);
|
||||
|
||||
#define GGML_N_TASKS_MAX -1
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom1(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
ggml_custom1_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom1_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
ggml_custom1_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom2(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
ggml_custom2_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom2_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
ggml_custom2_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom3(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
struct ggml_tensor * c,
|
||||
ggml_custom3_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom3_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
struct ggml_tensor * c,
|
||||
ggml_custom3_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata);
|
||||
|
||||
// loss function
|
||||
|
||||
|
||||
@@ -219,7 +219,7 @@ struct llama_mmap {
|
||||
// prefetch/readahead impairs performance on NUMA systems
|
||||
if (numa) { prefetch = 0; }
|
||||
#ifdef __linux__
|
||||
if (prefetch) { flags |= MAP_POPULATE; }
|
||||
if (prefetch >= file->size) { flags |= MAP_POPULATE; }
|
||||
#endif
|
||||
addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
|
||||
if (addr == MAP_FAILED) {
|
||||
|
||||
@@ -747,12 +747,12 @@ struct llama_model_loader {
|
||||
|
||||
void load_all_data(llama_progress_callback progress_callback, void * progress_callback_user_data, llama_mlock * lmlock) {
|
||||
size_t data_size = 0;
|
||||
size_t prefetch_size = 0;
|
||||
size_t prefetch_size = file_loader->file.size;
|
||||
size_t lock_size = 0;
|
||||
for (const llama_load_tensor & lt : tensors_map.tensors) {
|
||||
data_size += lt.size;
|
||||
if (lt.ggml_tensor->backend == GGML_BACKEND_CPU) {
|
||||
prefetch_size += lt.size;
|
||||
if (lt.ggml_tensor->backend != GGML_BACKEND_CPU) {
|
||||
prefetch_size -= lt.size;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user