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14 Commits

Author SHA1 Message Date
Georgi Gerganov 8cb1ad5a2c metal : add set_rows with src0 f16 (#25434) 2026-07-08 16:51:31 +03:00
Georgi Gerganov 400d4bf0e2 metal: add col2im_1d op (f32/f16/bf16) (#25176) 2026-07-08 16:50:17 +03:00
YiChen Lv 5dca9f3470 metal : per-op source split + parallel compile (#24021)
* preliminary extract common header

* op source split

* split metallib into 8 libs && load in parallel

* derive kernel->library routing from functionNames

* x-macro lib list + underscore filenames, dedup QK_NL, MRC fixes

* op source split 8 to 20

* improve robustness of source fallback

* clean up

* change bool -> atomic_bool

* only prepend headers that source actually includes

* no semaphore, use GCD global queue

* dedup library compile path, fix NSError lifetime, rename gla

* relocate upstream concat/rope_back/repeat kernel changes into split files

* move ggml-common.h from common.h into dequantize.h to shrink binary size

---------

Co-authored-by: lvyichen <lvyichen@stepfun.com>
2026-07-08 16:42:39 +03:00
Xuan-Son Nguyen c264f65ff9 cli : move to HTTP-based implementation (#24948)
* cli: move to HTTP-based implementation

* wip

* working

* remote server ok

* cli support router mode

Co-authored-by: Piotr Wilkin <ilintar@gmail.com>

* case: router with only one model

* Apply suggestions from code review

Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>

* remove outdated comment

* use destructor instead

* add ftype

* cli-view --> cli-ui

* pimpl

* no more json in header

* nits fixes

* also show model aliases

---------

Co-authored-by: Piotr Wilkin <ilintar@gmail.com>
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
2026-07-08 14:52:43 +02:00
Oliver Simons 07e012afdc Make hip quality check run on all changes (#25403)
Improvement of the CI to run on all hip-related changes as a follow-up to
https://github.com/ggml-org/llama.cpp/pull/25373
so breakage is more likely to be caught in future
2026-07-08 14:38:51 +02:00
fairydreaming ed8c26150e cuda : add support for f16->f16 GGML_OP_SET_ROWS (#25367) 2026-07-08 19:24:20 +08:00
Aman Gupta 90e0f5cfcb llama: refactor fused ops (#24646) 2026-07-08 18:18:09 +08:00
Pascal bbebeec4a8 server-stream: follow-up on SSE Replay Buffer (#23226) (#25047)
* server-stream : pimpl

* server-stream: prefix free functions with server_stream_

address review from ggerganov: scope the public stream functions under the
server_stream_ prefix, matching server_stream_session_manager_start/stop.

* server-stream: guard session and manager state with the mutex

address review from ggerganov: make done, completed_ts and the GC running flag plain members under their
mutex and set the condvar predicates under the lock. keep cancelled atomic for
the lock-free should_stop poll.

* server-stream: trim comments to the non-obvious

address review from ggerganov: drop comments that restate the code, keep the
concurrency, lifetime and ordering rationale. de-stale a few comments left by the
pimpl: g_stream_sessions is now internal and the /v1/streams listing is gone.

* server-stream: update dev docs for the pimpl and prefix

reflect server_stream_session_manager_start/stop and the server_stream_ prefix,
note the manager is now a file-static singleton hidden in the .cpp

* server-stream: move stream traces to debug level

keep the bring-up traces for diagnostics but off the default log: skip
drain, draining, drain ended, DELETE evict, attach_pipe, and the router
stream resume proxy.

* server-stream: align router stream resume proxy trace with upstream

the child-side bring-up traces are already SRV_TRC on master, move the
router stream resume proxy trace to the same level.

* server-stream: move stream_read_status enum to the cpp

it is only used by the hidden session and consumer types, so it belongs
with them behind the pimpl boundary, not on the public header surface.

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2026-07-08 12:02:50 +03:00
Aman Gupta 230ea9d214 llama-batch: add n_keep_tail in split_equal for recurrent models (#25278) 2026-07-08 15:55:19 +08:00
rankaiyx f296fdfbed common: auto-create prompts-log-dir at argument parsing, so all tools using the flag benefit (#25322) 2026-07-08 09:45:28 +02:00
Aleksander Grygier f1161b15f2 ui: Context usage gauge and panel (#25340)
* feat: WIP

* feat: Retire ChatScreenProcessingInfo component, context, and keepStatsVisible settings

* feat: Always-on gauge with active-model /props, conversation stats and live-reactive reading/output/avg

* feat: Add /tokenize endpoint, TokenizeService, FNV-1a and JSON Schema utilities

* feat: Surface enabled-tools token count in context hover card

* refactor(tools): make toolsStore the sole owner of the OpenAI wire format

Previously mcpStore.getToolDefinitionsForLLM() owned the MCP->OpenAI
shape conversion (plus normalizeSchemaProperties). That created two
sources of truth for what gets sent to the LLM, with the
duplication-prone risk of the deduplicated enabled list (which feeds
the token-count cache) drifting from the bytes actually shipped on
chat.

Now:
- mcpStore: pure protocol state + routing. Drop getToolDefinitionsForLLM
  and the inline OpenAIToolDefinition conversion + normalizeSchemaProperties.
  Doc comment adjusted to declare wire-format ownership as belonging
  to toolsStore. Connection lifecycle, health checks, executeTool,
  and the connections/toolsIndex remain.
- toolsStore: owns the wire shape (added earlier this series). mcpEntries()
  inlines the MCP tool conversion; uses normalizeJsonSchema (the JSON
  Schema util extracted in the prior commit) so missing 'type' fields
  are inferred from defaults. mcpTools getter iterates mcpEntries() so
  the Settings UI and the deduplicated enabled list see the same
  definitions. getEnabledToolsForLLM iterates mcpEntries() instead of
  calling mcpStore, so the JSON sent to the LLM is identical to what
  toolsStore.refreshEnabledToolsTokenCount tokenizes.
- agentic: the chat-completion tools field's type was annotated as
  ReturnType<typeof mcpStore.getToolDefinitionsForLLM>, claiming the
  shape was owned by mcpStore. Switch to ReturnType<typeof
  toolsStore.getEnabledToolsForLLM>, the actual source.

Assisted-by: Claude

* feat: UI WIP

* feat: UI WIP

* feat: UI WIP

* feat: Adjust reasoning submenu layout and spacing

* feat: Adjust context usage gauge thresholds and styling

* feat: Split context usage gauge stats into current and cumulative breakdowns

* chore: Format

* refactor: Cleanup

* refactor: Cleanup

* feat: improve token gauge accuracy and display

* refactor: remove MCP recommendation gating and simplify server visibility

* feat: add token audit logging to ChatStore for debugging

* refactor: Simplify context token reading to use server promptTokens directly

* feat: Replace last-known token tracking with live server-derived stats for accurate streaming gauges

* feat: UI Improvements

* feat: Move prompt processing stats to the preceding user message

* feat: Fix context token double-counting and refine gauge layout

* refactor: remove always-show-agentic-turns setting and simplify agentic turn display

* feat: track and display cache tokens in context gauge

* feat: add diagnostic logging for chat completion requests

* refactor: improve token audit console output with fresh/cached breakdown

* fix: invalidate enabled tools token count cache on tool changes

* test: add unit tests for tools store token count invalidation

* refactor: Remove tools token counting infrastructure

* refactor: Update ChatFormContextGauge to use simplified token tracking

* refactor: Update ChatStore to remove tools token counting

* chore: Formatting

* feat: Improve UI text

* feat: simplify context usage derivation and refine gauge labels

* refactor: cleanup logs

* cleaning

* fix: UI

* refactor: Enums

* refactor: Extract context gauge logic into hook and split UI into sub-components

* refactor: Cleanup comments

---------

Co-authored-by: Pascal <admin@serveurperso.com>
2026-07-08 09:22:35 +02:00
Georgi Gerganov da46e59cbf llama-eval : fix crash when answer is None in HTML dump (#25435)
dict.get("key", default) returns None (not default) when the key
exists but its value is explicitly None. This caused an AttributeError
in _escape_html() when a task errored before grading and answer was
set to None.

Assisted-by: pi:llama.cpp/Qwen3.6-27B
2026-07-08 10:00:03 +03:00
fairydreaming 0512ef1e5a metal : add set_rows with src0 f16 (#25434)
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2026-07-08 09:49:07 +03:00
hourhl 4a7ee3126d fix: OOB reads in UGM tokenizer (precompiled_charsmap handling) (#18750)
* fix: OOB reads in UGM tokenizer (precompiled_charsmap handling)

- Validate minimum size (4 bytes) before reading xcda_blob_size
- Use strnlen with bounds check instead of unsafe strlen

Both issues allow heap-buffer-overflow from malicious T5/UGM GGUF files.

* Replace unsafe strnlen() with a bounds-checked loop that scans for \0 within the remaining array size.

* move bounds checks to load

* typo merge fix

---------

Co-authored-by: hourhl <hourhl8200@gmail.com>
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
2026-07-08 08:02:09 +03:00
121 changed files with 14462 additions and 12748 deletions
+4
View File
@@ -9,6 +9,8 @@ on:
'.github/workflows/hip-quality-check.yml',
'**/*.cu',
'**/*.cuh',
'ggml/src/ggml-hip/CMakeLists.txt',
'ggml/src/ggml-cuda/vendors/hip.h',
'scripts/hip/gcn-cdna-vgpr-check.py'
]
@@ -18,6 +20,8 @@ on:
'.github/workflows/hip-quality-check.yml',
'**/*.cu',
'**/*.cuh',
'ggml/src/ggml-hip/CMakeLists.txt',
'ggml/src/ggml-cuda/vendors/hip.h',
'scripts/hip/gcn-cdna-vgpr-check.py'
]
+16 -4
View File
@@ -27,6 +27,7 @@
#include <cinttypes>
#include <climits>
#include <cstdarg>
#include <filesystem>
#include <fstream>
#include <list>
#include <regex>
@@ -718,9 +719,8 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
// model is required (except for server)
// TODO @ngxson : maybe show a list of available models in CLI in this case
if (params.model.path.empty()
&& !params.usage
&& !params.completion) {
bool can_skip_model = params.usage || params.completion || !params.server_base.empty();
if (!can_skip_model && params.model.path.empty()) {
throw std::invalid_argument("error: --model is required\n");
}
}
@@ -1240,6 +1240,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.completion = true;
}
));
add_opt(common_arg(
{"--server-base"}, "URL",
string_format("connect to this server instead of starting a new one, example: 'http://localhost:8080' (default: none)"),
[](common_params & params, const std::string & value) {
params.server_base = value;
}
).set_examples({LLAMA_EXAMPLE_CLI}));
add_opt(common_arg(
{"--verbose-prompt"},
string_format("print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false"),
@@ -3451,9 +3458,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_env("LLAMA_ARG_LOG_FILE"));
add_opt(common_arg(
{"--log-prompts-dir"}, "PATH",
"Log prompts to directory (only used for debugging, default: disabled)",
"Log prompts to directory (auto-created if not present; only used for debugging, default: disabled)",
[](common_params & params, const std::string & value) {
params.path_prompts_log_dir = value;
std::error_code ec;
std::filesystem::create_directories(value, ec);
if (ec) {
fprintf(stderr, "warning: failed to create prompts-log-dir '%s': %s\n", value.c_str(), ec.message().c_str());
}
}
).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
add_opt(common_arg(
+3
View File
@@ -644,6 +644,9 @@ struct common_params {
std::map<std::string, std::string> default_template_kwargs;
// CLI params
std::string server_base; // if set, connect to this server instead of starting a new one
// UI configs
bool ui = true;
bool ui_mcp_proxy = false;
+70
View File
@@ -2,6 +2,16 @@
#include <cpp-httplib/httplib.h>
#ifdef _WIN32
#include <winsock2.h>
#include <windows.h>
#else
#include <sys/socket.h>
#include <netinet/in.h>
#include <arpa/inet.h>
#include <unistd.h>
#endif
struct common_http_url {
std::string scheme;
std::string user;
@@ -119,3 +129,63 @@ static std::pair<httplib::Client, common_http_url> common_http_client(const std:
static std::string common_http_show_masked_url(const common_http_url & parts) {
return parts.scheme + "://" + (parts.user.empty() ? "" : "****:****@") + common_http_format_host(parts.host) + parts.path;
}
static int common_http_get_free_port() {
#ifdef _WIN32
WSADATA wsaData;
if (WSAStartup(MAKEWORD(2, 2), &wsaData) != 0) {
return -1;
}
typedef SOCKET native_socket_t;
#define INVALID_SOCKET_VAL INVALID_SOCKET
#define CLOSE_SOCKET(s) closesocket(s)
#else
typedef int native_socket_t;
#define INVALID_SOCKET_VAL -1
#define CLOSE_SOCKET(s) close(s)
#endif
native_socket_t sock = socket(AF_INET, SOCK_STREAM, 0);
if (sock == INVALID_SOCKET_VAL) {
#ifdef _WIN32
WSACleanup();
#endif
return -1;
}
struct sockaddr_in serv_addr;
std::memset(&serv_addr, 0, sizeof(serv_addr));
serv_addr.sin_family = AF_INET;
serv_addr.sin_addr.s_addr = htonl(INADDR_ANY);
serv_addr.sin_port = htons(0);
if (bind(sock, (struct sockaddr*)&serv_addr, sizeof(serv_addr)) != 0) {
CLOSE_SOCKET(sock);
#ifdef _WIN32
WSACleanup();
#endif
return -1;
}
#ifdef _WIN32
int namelen = sizeof(serv_addr);
#else
socklen_t namelen = sizeof(serv_addr);
#endif
if (getsockname(sock, (struct sockaddr*)&serv_addr, &namelen) != 0) {
CLOSE_SOCKET(sock);
#ifdef _WIN32
WSACleanup();
#endif
return -1;
}
int port = ntohs(serv_addr.sin_port);
CLOSE_SOCKET(sock);
#ifdef _WIN32
WSACleanup();
#endif
return port;
}
+2 -2
View File
@@ -362,7 +362,7 @@ class EvalState:
case = cases.get(task_id, {})
status = case.get("status", "pending")
expected = case.get("expected", "")
answer = case.get("answer", "") if status == "ok" else ""
answer = case.get("answer") or "" if status == "ok" else ""
is_correct = case.get("correct", False) if status == "ok" else False
response = case.get("response", "") or ""
prompt = case.get("prompt", "") or ""
@@ -647,7 +647,7 @@ class EvalState:
question, prompt, expected = self.get_case(i)
case = cases.get(task_id, {})
status = case.get("status", "pending")
answer = case.get("answer", "N/A") if status == "ok" else "N/A"
answer = case.get("answer") or "N/A" if status == "ok" else "N/A"
tokens = case.get("tokens")
tokens_str = str(tokens) if tokens is not None else "N/A"
tps_gen = case.get("tps_gen")
-3
View File
@@ -340,9 +340,6 @@ set(GGML_PUBLIC_HEADERS
include/gguf.h)
set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}")
#if (GGML_METAL)
# set_target_properties(ggml PROPERTIES RESOURCE "${CMAKE_CURRENT_SOURCE_DIR}/src/ggml-metal.metal")
#endif()
install(TARGETS ggml LIBRARY PUBLIC_HEADER)
install(TARGETS ggml-base LIBRARY)
+10 -4
View File
@@ -4709,10 +4709,16 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
} break;
case GGML_OP_SET_ROWS:
{
return (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16 || op->type == GGML_TYPE_BF16 ||
op->type == GGML_TYPE_Q4_0 || op->type == GGML_TYPE_Q4_1 || op->type == GGML_TYPE_Q5_0 ||
op->type == GGML_TYPE_Q5_1 || op->type == GGML_TYPE_Q8_0 || op->type == GGML_TYPE_IQ4_NL) &&
op->src[0]->type == GGML_TYPE_F32 &&
return (
(
(op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16 || op->type == GGML_TYPE_BF16 ||
op->type == GGML_TYPE_Q4_0 || op->type == GGML_TYPE_Q4_1 || op->type == GGML_TYPE_Q5_0 ||
op->type == GGML_TYPE_Q5_1 || op->type == GGML_TYPE_Q8_0 || op->type == GGML_TYPE_IQ4_NL) &&
op->src[0]->type == GGML_TYPE_F32
) || (
op->type == GGML_TYPE_F16 && op->src[0]->type == GGML_TYPE_F16
)
) &&
(op->src[1]->type == GGML_TYPE_I64 || op->src[1]->type == GGML_TYPE_I32);
} break;
case GGML_OP_SET:
+64 -4
View File
@@ -322,17 +322,77 @@ static void set_rows_cuda(ggml_backend_cuda_context & ctx, const ggml_tensor * s
}
}
template<>
void set_rows_cuda<half, int32_t>(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const half * src0_d = (const half *)src0->data;
const int32_t * src1_d = (const int32_t *)src1->data;
GGML_TENSOR_BINARY_OP_LOCALS
cudaStream_t stream = ctx.stream();
if (dst->type == GGML_TYPE_F16) {
set_rows_cuda(
src0_d, src1_d, (half*)dst->data,
ne00, ne01, ne02, ne03,
ne10, ne11, ne12, ne13,
nb01, nb02, nb03,
nb10, nb11, nb12,
nb1, nb2, nb3,
stream
);
} else {
GGML_ABORT("unsupported type %s", ggml_type_name(dst->type));
}
}
template<>
void set_rows_cuda<half, int64_t>(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const half * src0_d = (const half *)src0->data;
const int64_t * src1_d = (const int64_t *)src1->data;
GGML_TENSOR_BINARY_OP_LOCALS
cudaStream_t stream = ctx.stream();
if (dst->type == GGML_TYPE_F16) {
set_rows_cuda(
src0_d, src1_d, (half*)dst->data,
ne00, ne01, ne02, ne03,
ne10, ne11, ne12, ne13,
nb01, nb02, nb03,
nb10, nb11, nb12,
nb1, nb2, nb3,
stream
);
} else {
GGML_ABORT("unsupported type %s", ggml_type_name(dst->type));
}
}
void ggml_cuda_op_set_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src0->type == GGML_TYPE_F32 || (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16));
GGML_ASSERT(src1->type == GGML_TYPE_I64 || src1->type == GGML_TYPE_I32);
if (src1->type == GGML_TYPE_I64) {
set_rows_cuda<float, int64_t>(ctx, src0, src1, dst);
if (src0->type == GGML_TYPE_F32) {
if (src1->type == GGML_TYPE_I64) {
set_rows_cuda<float, int64_t>(ctx, src0, src1, dst);
} else {
set_rows_cuda<float, int32_t>(ctx, src0, src1, dst);
}
} else if (src0->type == GGML_TYPE_F16) {
if (src1->type == GGML_TYPE_I64) {
set_rows_cuda<half, int64_t>(ctx, src0, src1, dst);
} else {
set_rows_cuda<half, int32_t>(ctx, src0, src1, dst);
}
} else {
set_rows_cuda<float, int32_t>(ctx, src0, src1, dst);
GGML_ABORT("unsupported type %s", ggml_type_name(src0->type));
}
}
+119 -51
View File
@@ -24,62 +24,119 @@ if (GGML_METAL_NDEBUG)
endif()
set(METALLIB_COMMON "${CMAKE_CURRENT_SOURCE_DIR}/../ggml-common.h")
set(METALLIB_KERNELS_COMMON "${CMAKE_CURRENT_SOURCE_DIR}/kernels/common.h")
set(METALLIB_KERNELS_DEQUANTIZE "${CMAKE_CURRENT_SOURCE_DIR}/kernels/dequantize.h")
set(METALLIB_KERNELS_QUANTIZE "${CMAKE_CURRENT_SOURCE_DIR}/kernels/quantize.h")
set(METALLIB_KERNEL_SOURCES
kernels/fa.metal
kernels/mul_mv.metal
kernels/mul_mm.metal
kernels/quantize.metal
kernels/softmax.metal
kernels/norm.metal
kernels/unary.metal
kernels/binbcast.metal
kernels/reduce.metal
kernels/tri.metal
kernels/ssm.metal
kernels/wkv.metal
kernels/gated_delta_net.metal
kernels/solve_tri.metal
kernels/rope.metal
kernels/conv.metal
kernels/upscale.metal
kernels/argsort.metal
kernels/pool.metal
kernels/misc.metal
)
if (GGML_METAL_EMBED_LIBRARY)
enable_language(ASM)
add_compile_definitions(GGML_METAL_EMBED_LIBRARY)
set(METALLIB_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal")
set(METALLIB_IMPL "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal-impl.h")
set(METALLIB_IMPL "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal-impl.h")
file(MAKE_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/autogenerated")
# merge ggml-common.h and ggml-metal.metal into a single file
set(METALLIB_EMBED_ASM "${CMAKE_CURRENT_BINARY_DIR}/autogenerated/ggml-metal-embed.s")
set(METALLIB_SOURCE_EMBED "${CMAKE_CURRENT_BINARY_DIR}/autogenerated/ggml-metal-embed.metal")
set(METALLIB_SOURCE_EMBED_TMP "${CMAKE_CURRENT_BINARY_DIR}/autogenerated/ggml-metal-embed.metal.tmp")
set(METALLIB_EMBED_ASM_FILES "")
foreach(src ${METALLIB_KERNEL_SOURCES})
get_filename_component(kind ${src} NAME_WE)
# symbol names must be valid C identifiers ('-' is not allowed)
string(REPLACE "-" "_" kind_sym ${kind})
add_custom_command(
OUTPUT "${METALLIB_EMBED_ASM}"
COMMAND echo "Embedding Metal library"
COMMAND sed -e "/__embed_ggml-common.h__/r ${METALLIB_COMMON}" -e "/__embed_ggml-common.h__/d" < "${METALLIB_SOURCE}" > "${METALLIB_SOURCE_EMBED_TMP}"
COMMAND sed -e "/\#include \"ggml-metal-impl.h\"/r ${METALLIB_IMPL}" -e "/\#include \"ggml-metal-impl.h\"/d" < "${METALLIB_SOURCE_EMBED_TMP}" > "${METALLIB_SOURCE_EMBED}"
COMMAND echo ".section __DATA,__ggml_metallib" > "${METALLIB_EMBED_ASM}"
COMMAND echo ".globl _ggml_metallib_start" >> "${METALLIB_EMBED_ASM}"
COMMAND echo "_ggml_metallib_start:" >> "${METALLIB_EMBED_ASM}"
COMMAND echo .incbin "\"${METALLIB_SOURCE_EMBED}\"" >> "${METALLIB_EMBED_ASM}"
COMMAND echo ".globl _ggml_metallib_end" >> "${METALLIB_EMBED_ASM}"
COMMAND echo "_ggml_metallib_end:" >> "${METALLIB_EMBED_ASM}"
DEPENDS ../ggml-common.h ggml-metal.metal ggml-metal-impl.h
COMMENT "Generate assembly for embedded Metal library"
VERBATIM
)
set(SRC "${CMAKE_CURRENT_SOURCE_DIR}/kernels/${kind}.metal")
set(EMBED "${CMAKE_CURRENT_BINARY_DIR}/autogenerated/ggml-metal-embed-${kind}.metal")
set(ASM "${CMAKE_CURRENT_BINARY_DIR}/autogenerated/ggml-metal-embed-${kind}.s")
target_sources(ggml-metal PRIVATE "${METALLIB_EMBED_ASM}")
# only prepend headers that this source actually includes
set(HEADERS_FOR_SRC ${METALLIB_KERNELS_COMMON})
file(STRINGS ${SRC} _has_dequantize REGEX "#include \"dequantize\\.h\"")
file(STRINGS ${SRC} _has_quantize REGEX "#include \"quantize\\.h\"")
if(_has_dequantize)
list(APPEND HEADERS_FOR_SRC ${METALLIB_KERNELS_DEQUANTIZE})
endif()
if(_has_quantize)
list(APPEND HEADERS_FOR_SRC ${METALLIB_KERNELS_QUANTIZE})
endif()
add_custom_command(
OUTPUT "${ASM}"
# Step 1: concatenate shared headers + this kernel source
COMMAND cat ${HEADERS_FOR_SRC} ${SRC} > "${EMBED}.tmp1"
# Step 2: remove internal #include and #pragma once
COMMAND sed -e "/\#include \"common.h\"/d" -e "/\#include \"dequantize.h\"/d" -e "/\#include \"quantize.h\"/d" -e "/\#pragma once/d" < "${EMBED}.tmp1" > "${EMBED}.tmp2"
# Step 3: inline ggml-common.h (replacing __embed_ggml-common.h__ sentinel)
COMMAND sed -e "/__embed_ggml-common.h__/r ${METALLIB_COMMON}" -e "/__embed_ggml-common.h__/d" < "${EMBED}.tmp2" > "${EMBED}.tmp3"
# Step 4: inline ggml-metal-impl.h
COMMAND sed -e "/\#include \"ggml-metal-impl.h\"/r ${METALLIB_IMPL}" -e "/\#include \"ggml-metal-impl.h\"/d" < "${EMBED}.tmp3" > "${EMBED}"
# Step 5: emit an asm chunk with kind-specific start/end symbols
# note: '-' is illegal in C symbols, so we use kind_sym; the macOS
# section name is limited to 16 chars so we keep it shared
# across kinds (__ggml_metallib) and only vary the global symbols.
COMMAND echo ".section __DATA,__ggml_metallib" > "${ASM}"
COMMAND echo ".globl _ggml_metallib_${kind_sym}_start" >> "${ASM}"
COMMAND echo "_ggml_metallib_${kind_sym}_start:" >> "${ASM}"
COMMAND echo .incbin "\"${EMBED}\"" >> "${ASM}"
COMMAND echo ".globl _ggml_metallib_${kind_sym}_end" >> "${ASM}"
COMMAND echo "_ggml_metallib_${kind_sym}_end:" >> "${ASM}"
DEPENDS ../ggml-common.h ggml-metal-impl.h
kernels/common.h kernels/dequantize.h kernels/quantize.h
kernels/${kind}.metal
COMMENT "Generate embedded Metal library for ${kind}"
VERBATIM
)
list(APPEND METALLIB_EMBED_ASM_FILES "${ASM}")
endforeach()
target_sources(ggml-metal PRIVATE ${METALLIB_EMBED_ASM_FILES})
else()
# copy metal files to bin directory
# copy header files to bin directory
configure_file(../ggml-common.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h COPYONLY)
configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY)
configure_file(ggml-metal-impl.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal-impl.h COPYONLY)
file(MAKE_DIRECTORY "${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/kernels")
configure_file(kernels/common.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/kernels/common.h COPYONLY)
configure_file(kernels/dequantize.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/kernels/dequantize.h COPYONLY)
configure_file(kernels/quantize.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/kernels/quantize.h COPYONLY)
foreach(src ${METALLIB_KERNEL_SOURCES})
configure_file(${src} ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${src} COPYONLY)
endforeach()
if (GGML_METAL_SHADER_DEBUG)
# custom command to do the following:
# xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
# xcrun -sdk macosx metallib ggml-metal.air -o default.metallib
#
# note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works
# disabling fast math is needed in order to pass tests/test-backend-ops
# note: disabling fast math is needed in order to pass tests/test-backend-ops
# note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1
# note: unfortunately, we have to call it default.metallib instead of ggml.metallib
# ref: https://github.com/ggml-org/whisper.cpp/issues/1720
# note: adding -g causes segmentation fault during compile
#set(XC_FLAGS -fno-fast-math -fno-inline -g)
set(XC_FLAGS -fno-fast-math -fno-inline)
else()
set(XC_FLAGS -O3)
endif()
# Append macOS metal versioning flags
if (GGML_METAL_MACOSX_VERSION_MIN)
message(STATUS "Adding -mmacosx-version-min=${GGML_METAL_MACOSX_VERSION_MIN} flag to metal compilation")
list (APPEND XC_FLAGS -mmacosx-version-min=${GGML_METAL_MACOSX_VERSION_MIN})
@@ -90,35 +147,46 @@ else()
list (APPEND XC_FLAGS -std=${GGML_METAL_STD})
endif()
# Compile each kernel source to .air, then link into default.metallib
set(AIR_FILES "")
foreach(src ${METALLIB_KERNEL_SOURCES})
get_filename_component(name ${src} NAME_WE)
set(AIR "${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${name}.air")
list(APPEND AIR_FILES ${AIR})
add_custom_command(
OUTPUT ${AIR}
COMMAND xcrun -sdk macosx metal ${XC_FLAGS} -I ${CMAKE_RUNTIME_OUTPUT_DIRECTORY} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${src} -o ${AIR}
DEPENDS ${src} kernels/common.h kernels/dequantize.h kernels/quantize.h ${METALLIB_COMMON} ggml-metal-impl.h
COMMENT "Compiling ${src}"
VERBATIM
)
endforeach()
add_custom_command(
OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
COMMAND xcrun -sdk macosx metal ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o - |
xcrun -sdk macosx metallib - -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
COMMAND xcrun -sdk macosx metallib ${AIR_FILES} -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h
COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal
DEPENDS ggml-metal.metal ${METALLIB_COMMON}
COMMENT "Compiling Metal kernels"
)
COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal-impl.h
COMMAND rm -rf ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/kernels
DEPENDS ${AIR_FILES}
COMMENT "Linking Metal kernels into default.metallib"
)
# FIXME: only add to the ggml-metal target?
add_custom_target(
ggml-metal-lib ALL
DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
)
)
endif() # GGML_METAL_EMBED_LIBRARY
if (NOT GGML_METAL_EMBED_LIBRARY)
install(
FILES src/ggml-metal/ggml-metal.metal
PERMISSIONS
OWNER_READ
OWNER_WRITE
GROUP_READ
WORLD_READ
DESTINATION ${CMAKE_INSTALL_BINDIR})
DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/kernels/
DESTINATION ${CMAKE_INSTALL_BINDIR}/kernels
FILES_MATCHING PATTERN "*.metal" PATTERN "*.h"
)
install(
FILES ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
DESTINATION ${CMAKE_INSTALL_BINDIR}
)
install(
FILES ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
DESTINATION ${CMAKE_INSTALL_BINDIR}
)
endif()
+6 -2
View File
@@ -160,11 +160,15 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_get_rows(ggml_me
return res;
}
ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_set_rows(ggml_metal_library_t lib, ggml_type tidx, ggml_type tdst) {
ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_set_rows(ggml_metal_library_t lib, const ggml_tensor * op) {
char base[256];
char name[256];
snprintf(base, 256, "kernel_set_rows_%s_%s", ggml_type_name(tdst), ggml_type_name(tidx));
const auto tsrc = op->src[0]->type;
const auto tidx = op->src[1]->type;
const auto tdst = op->type;
snprintf(base, 256, "kernel_set_rows_%s_%s_%s", ggml_type_name(tsrc), ggml_type_name(tidx), ggml_type_name(tdst));
snprintf(name, 256, "%s", base);
ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
+1 -1
View File
@@ -112,7 +112,7 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_cpy
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_pool_1d (ggml_metal_library_t lib, const struct ggml_tensor * op, enum ggml_op_pool op_pool);
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_pool_2d (ggml_metal_library_t lib, const struct ggml_tensor * op, enum ggml_op_pool op_pool);
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_get_rows (ggml_metal_library_t lib, enum ggml_type tsrc);
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_set_rows (ggml_metal_library_t lib, enum ggml_type tidx, enum ggml_type tdst);
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_set_rows (ggml_metal_library_t lib, const struct ggml_tensor * op);
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_diag (ggml_metal_library_t lib, const struct ggml_tensor * op);
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_repeat (ggml_metal_library_t lib, enum ggml_type tsrc);
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_concat (ggml_metal_library_t lib, enum ggml_type tsrc);
+424 -129
View File
@@ -94,8 +94,63 @@ int ggml_metal_pipeline_max_theads_per_threadgroup(struct ggml_metal_pipeline_wi
return pipeline.pipeline->obj.maxTotalThreadsPerThreadgroup;
}
//
// MTLLibrary collection (one library per op-source, compiled separately)
//
// Single source of truth for the per-kind metal libraries. The order here
// defines the enum values and every per-kind table below, so adding a library
// is a one-line change here (plus adding its source to CMakeLists.txt).
// X(suffix, name): name is both the kernels/<name>.metal basename and the
// ggml_metallib_<name>_{start,end} embed-symbol stem.
#define GGML_METAL_LIBS \
X(FA, fa) \
X(MUL_MV, mul_mv) \
X(MUL_MM, mul_mm) \
X(QUANTIZE, quantize) \
X(SOFTMAX, softmax) \
X(NORM, norm) \
X(UNARY, unary) \
X(BINBCAST, binbcast) \
X(REDUCE, reduce) \
X(TRI, tri) \
X(SSM, ssm) \
X(WKV, wkv) \
X(GATED_DELTA_NET, gated_delta_net)\
X(SOLVE_TRI, solve_tri) \
X(ROPE, rope) \
X(CONV, conv) \
X(UPSCALE, upscale) \
X(ARGSORT, argsort) \
X(POOL, pool) \
X(MISC, misc)
enum ggml_metal_lib_kind {
#define X(e, s) GGML_METAL_LIB_##e,
GGML_METAL_LIBS
#undef X
GGML_METAL_LIB_COUNT,
};
static const char * const k_lib_names[GGML_METAL_LIB_COUNT] = {
#define X(e, s) [GGML_METAL_LIB_##e] = #s,
GGML_METAL_LIBS
#undef X
};
struct ggml_metal_library {
id<MTLLibrary> obj;
// Per-kind compiled libraries. When single_library is true, the whole library
// (e.g. a pre-compiled default.metallib or a from-source build) lives at
// objs[0] and the remaining slots are nil.
id<MTLLibrary> objs[GGML_METAL_LIB_COUNT];
bool single_library; // true: combined library at objs[0]; false: per-kind libs in objs[*]
// Routing table: kernel function name -> objs[] index, populated from each
// compiled library's -[MTLLibrary functionNames]. The actual compiled
// libraries are the single source of truth for which library owns a kernel,
// so adding kernels later requires no manual routing maintenance.
// nil in single_library mode (everything resolves to objs[0]).
NSMutableDictionary<NSString *, NSNumber *> * fn_to_lib;
ggml_metal_device_t dev;
ggml_metal_pipelines_t pipelines; // cache of compiled pipelines
@@ -103,160 +158,376 @@ struct ggml_metal_library {
NSLock * lock;
};
ggml_metal_library_t ggml_metal_library_init(ggml_metal_device_t dev) {
id<MTLLibrary> library = nil;
id<MTLDevice> device = ggml_metal_device_get_obj(dev);
// Build the fn_to_lib routing table by querying each compiled library's public
// function names. Call once after all per-kind libraries have been compiled.
static void ggml_metal_library_build_index(ggml_metal_library_t lib) {
@autoreleasepool {
NSMutableDictionary<NSString *, NSNumber *> * index = [[NSMutableDictionary alloc] init];
for (int kind = 0; kind < GGML_METAL_LIB_COUNT; ++kind) {
for (NSString * fname in [lib->objs[kind] functionNames]) {
index[fname] = @(kind);
}
}
lib->fn_to_lib = index;
}
}
// load library
//
// - first check if the library is embedded
// - then check if the library is in the bundle
// - if not found, load the source and compile it
// - if that fails, return NULL
//
// TODO: move to a function
{
const int64_t t_start = ggml_time_us();
// Parse a `#include "name"` line. Returns the quoted name in *include_name on
// success. Whitespace-tolerant; ignores `#include <...>` (system headers).
static bool ggml_metal_library_parse_quoted_include(NSString * line, NSString ** include_name) {
NSScanner * scanner = [NSScanner scannerWithString:line];
scanner.charactersToBeSkipped = [NSCharacterSet whitespaceCharacterSet];
NSError * error = nil;
NSString * src = nil;
if (![scanner scanString:@"#" intoString:NULL] ||
![scanner scanString:@"include" intoString:NULL] ||
![scanner scanString:@"\"" intoString:NULL]) {
return false;
}
#if GGML_METAL_EMBED_LIBRARY
GGML_LOG_INFO("%s: using embedded metal library\n", __func__);
NSString * name = nil;
if (![scanner scanUpToString:@"\"" intoString:&name]) {
return false;
}
extern const char ggml_metallib_start[];
extern const char ggml_metallib_end[];
if (include_name) {
*include_name = name;
}
return true;
}
src = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding];
#else
// Recursively inline `#include "name"` directives. System includes (<...>),
// `#if/#else/#endif`, and other preprocessor lines are passed through to the
// Metal compiler unchanged. `#pragma once` is dropped since `seen` already
// guards against double-inclusion.
static bool ggml_metal_library_flatten_file(NSMutableString * dst, NSString * path,
NSArray<NSString *> * search_paths,
NSMutableSet<NSString *> * seen, NSError ** error) {
NSString * key = [path stringByStandardizingPath];
if ([seen containsObject:key]) {
return true;
}
[seen addObject:key];
#ifdef SWIFT_PACKAGE
NSBundle * bundle = SWIFTPM_MODULE_BUNDLE;
#else
NSBundle * bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
#endif
NSString * src = [NSString stringWithContentsOfFile:path encoding:NSUTF8StringEncoding error:error];
if (!src) {
return false;
}
NSString * path_lib = [bundle pathForResource:@"default" ofType:@"metallib"];
if (path_lib == nil) {
// Try to find the resource in the directory where the current binary located.
NSString * bin_cur = [[NSProcessInfo processInfo] arguments][0];
NSString * bin_dir = [bin_cur stringByDeletingLastPathComponent];
NSFileManager * fm = [NSFileManager defaultManager];
for (NSString * line in [src componentsSeparatedByString:@"\n"]) {
NSString * trimmed = [line stringByTrimmingCharactersInSet:[NSCharacterSet whitespaceCharacterSet]];
if ([trimmed isEqualToString:@"#pragma once"]) {
continue;
}
NSString * path_lib_default = [NSString pathWithComponents:@[bin_dir, @"default.metallib"]];
if ([[NSFileManager defaultManager] isReadableFileAtPath:path_lib_default]) {
GGML_LOG_INFO("%s: found '%s'\n", __func__, [path_lib_default UTF8String]);
NSDictionary * atts = [[NSFileManager defaultManager] attributesOfItemAtPath:path_lib_default error:&error];
if (atts && atts[NSFileType] == NSFileTypeSymbolicLink) {
// Optionally, if this is a symlink, try to resolve it.
path_lib_default = [[NSFileManager defaultManager] destinationOfSymbolicLinkAtPath:path_lib_default error:&error];
if (path_lib_default && [path_lib_default length] > 0 && ![[path_lib_default substringToIndex:1] isEqualToString:@"/"]) {
// It is a relative path, adding the binary directory as directory prefix.
path_lib_default = [NSString pathWithComponents:@[bin_dir, path_lib_default]];
}
if (!path_lib_default || ![[NSFileManager defaultManager] isReadableFileAtPath:path_lib_default]) {
// Link to the resource could not be resolved.
path_lib_default = nil;
} else {
GGML_LOG_INFO("%s: symlink resolved '%s'\n", __func__, [path_lib_default UTF8String]);
}
NSString * include_name = nil;
if (ggml_metal_library_parse_quoted_include(line, &include_name)) {
NSString * resolved = nil;
for (NSString * dir in search_paths) {
NSString * candidate = [dir stringByAppendingPathComponent:include_name];
if ([fm isReadableFileAtPath:candidate]) {
resolved = candidate;
break;
}
} else {
// The resource couldn't be found in the binary's directory.
path_lib_default = nil;
}
path_lib = path_lib_default;
if (!resolved) {
if (error) {
NSString * msg = [NSString stringWithFormat:@"could not resolve include \"%@\" from '%@'", include_name, path];
*error = [NSError errorWithDomain:@"ggml-metal-source-flatten" code:1
userInfo:@{NSLocalizedDescriptionKey: msg}];
}
return false;
}
if (!ggml_metal_library_flatten_file(dst, resolved, search_paths, seen, error)) {
return false;
}
continue;
}
if (path_lib != nil) {
// pre-compiled library found
NSURL * libURL = [NSURL fileURLWithPath:path_lib];
GGML_LOG_INFO("%s: loading '%s'\n", __func__, [path_lib UTF8String]);
[dst appendString:line];
[dst appendString:@"\n"];
}
library = [device newLibraryWithURL:libURL error:&error];
if (error) {
GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
return nil;
}
} else {
GGML_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
return true;
}
NSString * path_source;
NSString * path_resource = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
static NSString * ggml_metal_library_flatten_source(NSString * path_source, NSError ** error) {
// Search paths cover both runtime layout (build/bin/kernels + build/bin)
// and source-tree layout (ggml/src/ggml-metal/kernels + ggml/src/ggml-metal + ggml/src).
NSString * path_kernels = [path_source stringByDeletingLastPathComponent];
NSString * path_base = [path_kernels stringByDeletingLastPathComponent];
NSArray<NSString *> * search_paths = @[
path_kernels,
path_base,
[path_base stringByDeletingLastPathComponent],
];
GGML_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, path_resource ? [path_resource UTF8String] : "nil");
NSMutableString * src = [[NSMutableString alloc] init];
NSMutableSet<NSString *> * seen = [NSMutableSet set];
if (path_resource) {
path_source = [path_resource stringByAppendingPathComponent:@"ggml-metal.metal"];
} else {
path_source = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
if (!ggml_metal_library_flatten_file(src, path_source, search_paths, seen, error)) {
[src release];
return nil;
}
return src;
}
// Compile all per-kind libraries in parallel. `source_for_kind` returns the MSL
// source for a kind (the helper takes ownership and releases it), or nil with
// *err set on failure. On success the objs[] slots are populated and the routing
// index is built; on any failure every error is logged and false is returned
// (the caller is responsible for freeing `res`).
static bool ggml_metal_library_compile_all(
ggml_metal_library_t res,
id<MTLDevice> device,
NSDictionary * prep,
NSString * (^source_for_kind)(int kind, NSError ** err),
const char * origin) {
const int64_t t_start = ggml_time_us();
int64_t * t_per_lib = calloc(GGML_METAL_LIB_COUNT, sizeof(int64_t));
NSError ** err_per_lib = calloc(GGML_METAL_LIB_COUNT, sizeof(NSError *));
__block atomic_bool any_failure = false;
dispatch_group_t group = dispatch_group_create();
dispatch_queue_t queue = dispatch_get_global_queue(QOS_CLASS_USER_INITIATED, 0);
for (int kind = 0; kind < GGML_METAL_LIB_COUNT; ++kind) {
dispatch_group_async(group, queue, ^{
const int64_t t0 = ggml_time_us();
NSError * error = nil;
NSString * src = source_for_kind(kind, &error);
if (!src) {
err_per_lib[kind] = [error retain];
atomic_store(&any_failure, true);
return;
}
if (path_source == nil) {
GGML_LOG_WARN("%s: error: could not use bundle path to find ggml-metal.metal, falling back to trying cwd\n", __func__);
path_source = @"ggml-metal.metal";
}
id<MTLLibrary> lib = nil;
GGML_LOG_INFO("%s: loading '%s'\n", __func__, [path_source UTF8String]);
src = [NSString stringWithContentsOfFile:path_source encoding:NSUTF8StringEncoding error:&error];
if (error) {
GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
return nil;
}
}
#endif
if (!library) {
@autoreleasepool {
// dictionary of preprocessor macros
NSMutableDictionary * prep = [NSMutableDictionary dictionary];
if (ggml_metal_device_get_props(dev)->has_bfloat) {
[prep setObject:@"1" forKey:@"GGML_METAL_HAS_BF16"];
}
if (ggml_metal_device_get_props(dev)->has_tensor) {
[prep setObject:@"1" forKey:@"GGML_METAL_HAS_TENSOR"];
}
#if GGML_METAL_EMBED_LIBRARY
[prep setObject:@"1" forKey:@"GGML_METAL_EMBED_LIBRARY"];
#endif
MTLCompileOptions * options = [MTLCompileOptions new];
options.preprocessorMacros = prep;
//[options setFastMathEnabled:false];
lib = [device newLibraryWithSource:src options:options error:&error];
library = [device newLibraryWithSource:src options:options error:&error];
if (error) {
GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
return nil;
}
#if !__has_feature(objc_arc)
[options release];
#endif
// retain the error before the autorelease pool drains it
if (!lib) {
err_per_lib[kind] = [error retain];
}
}
[src release];
t_per_lib[kind] = ggml_time_us() - t0;
if (!lib) {
atomic_store(&any_failure, true);
return;
}
res->objs[kind] = lib;
});
}
dispatch_group_wait(group, DISPATCH_TIME_FOREVER);
dispatch_release(group);
const bool ok = !atomic_load(&any_failure);
if (ok) {
const int64_t t_total = ggml_time_us() - t_start;
int64_t t_max = 0;
for (int kind = 0; kind < GGML_METAL_LIB_COUNT; ++kind) {
GGML_LOG_DEBUG("%s: compiled '%s' library in %.3f sec\n",
__func__, k_lib_names[kind], t_per_lib[kind] / 1e6);
if (t_per_lib[kind] > t_max) t_max = t_per_lib[kind];
}
GGML_LOG_INFO("%s: loaded %d libraries from %s in %.3f sec (max single = %.3f sec)\n",
__func__, GGML_METAL_LIB_COUNT, origin, t_total / 1e6, t_max / 1e6);
ggml_metal_library_build_index(res);
} else {
for (int kind = 0; kind < GGML_METAL_LIB_COUNT; ++kind) {
if (err_per_lib[kind]) {
GGML_LOG_ERROR("%s: failed to build '%s' library: %s\n", __func__,
k_lib_names[kind], [[err_per_lib[kind] description] UTF8String]);
[err_per_lib[kind] release];
}
}
#if GGML_METAL_EMBED_LIBRARY
[src release];
#endif // GGML_METAL_EMBED_LIBRARY
GGML_LOG_INFO("%s: loaded in %.3f sec\n", __func__, (ggml_time_us() - t_start) / 1e6);
}
ggml_metal_library_t res = calloc(1, sizeof(struct ggml_metal_library));
free(err_per_lib);
free(t_per_lib);
res->obj = library;
return ok;
}
ggml_metal_library_t ggml_metal_library_init(ggml_metal_device_t dev) {
id<MTLDevice> device = ggml_metal_device_get_obj(dev);
ggml_metal_library_t res = calloc(1, sizeof(struct ggml_metal_library));
res->dev = dev;
res->pipelines = ggml_metal_pipelines_init();
res->lock = [NSLock new];
// shared MTLCompileOptions preprocessor macros (matches the build-time defines)
NSMutableDictionary * prep = [NSMutableDictionary dictionary];
if (ggml_metal_device_get_props(dev)->has_bfloat) {
[prep setObject:@"1" forKey:@"GGML_METAL_HAS_BF16"];
}
if (ggml_metal_device_get_props(dev)->has_tensor) {
[prep setObject:@"1" forKey:@"GGML_METAL_HAS_TENSOR"];
}
#if GGML_METAL_EMBED_LIBRARY
[prep setObject:@"1" forKey:@"GGML_METAL_EMBED_LIBRARY"];
#endif
#if GGML_METAL_EMBED_LIBRARY
GGML_LOG_INFO("%s: using embedded metal library\n", __func__);
// start/end symbols emitted by CMake (see CMakeLists.txt), one pair per kind
#define X(e, s) extern const char ggml_metallib_##s##_start[]; extern const char ggml_metallib_##s##_end[];
GGML_METAL_LIBS
#undef X
static const char * const lib_start[GGML_METAL_LIB_COUNT] = {
#define X(e, s) [GGML_METAL_LIB_##e] = ggml_metallib_##s##_start,
GGML_METAL_LIBS
#undef X
};
static const char * const lib_end[GGML_METAL_LIB_COUNT] = {
#define X(e, s) [GGML_METAL_LIB_##e] = ggml_metallib_##s##_end,
GGML_METAL_LIBS
#undef X
};
const bool ok = ggml_metal_library_compile_all(res, device, prep,
^NSString * (int kind, NSError ** err) {
(void) err;
return [[NSString alloc] initWithBytes:lib_start[kind]
length:(lib_end[kind] - lib_start[kind])
encoding:NSUTF8StringEncoding];
}, "embedded data");
if (!ok) {
ggml_metal_library_free(res);
return NULL;
}
return res;
#else
#ifdef SWIFT_PACKAGE
NSBundle * bundle = SWIFTPM_MODULE_BUNDLE;
#else
NSBundle * bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
#endif
const int64_t t_start = ggml_time_us();
NSError * error = nil;
NSString * path_lib = [bundle pathForResource:@"default" ofType:@"metallib"];
if (path_lib == nil) {
// Try to find the resource in the directory where the current binary located.
NSString * bin_cur = [[NSProcessInfo processInfo] arguments][0];
NSString * bin_dir = [bin_cur stringByDeletingLastPathComponent];
NSString * path_lib_default = [NSString pathWithComponents:@[bin_dir, @"default.metallib"]];
if ([[NSFileManager defaultManager] isReadableFileAtPath:path_lib_default]) {
GGML_LOG_INFO("%s: found '%s'\n", __func__, [path_lib_default UTF8String]);
NSDictionary * atts = [[NSFileManager defaultManager] attributesOfItemAtPath:path_lib_default error:&error];
if (atts && atts[NSFileType] == NSFileTypeSymbolicLink) {
// Optionally, if this is a symlink, try to resolve it.
path_lib_default = [[NSFileManager defaultManager] destinationOfSymbolicLinkAtPath:path_lib_default error:&error];
if (path_lib_default && [path_lib_default length] > 0 && ![[path_lib_default substringToIndex:1] isEqualToString:@"/"]) {
// It is a relative path, adding the binary directory as directory prefix.
path_lib_default = [NSString pathWithComponents:@[bin_dir, path_lib_default]];
}
if (!path_lib_default || ![[NSFileManager defaultManager] isReadableFileAtPath:path_lib_default]) {
// Link to the resource could not be resolved.
path_lib_default = nil;
} else {
GGML_LOG_INFO("%s: symlink resolved '%s'\n", __func__, [path_lib_default UTF8String]);
}
}
} else {
// The resource couldn't be found in the binary's directory.
path_lib_default = nil;
}
path_lib = path_lib_default;
}
if (path_lib != nil) {
// pre-compiled library found: a single combined default.metallib
NSURL * libURL = [NSURL fileURLWithPath:path_lib];
GGML_LOG_INFO("%s: loading '%s'\n", __func__, [path_lib UTF8String]);
res->objs[0] = [device newLibraryWithURL:libURL error:&error];
res->single_library = true;
if (!res->objs[0]) {
GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
ggml_metal_library_free(res);
return NULL;
}
GGML_LOG_INFO("%s: loaded in %.3f sec\n", __func__, (ggml_time_us() - t_start) / 1e6);
return res;
}
// no pre-compiled metallib: fall back to compiling each kernel source separately
GGML_LOG_INFO("%s: default.metallib not found, loading kernel sources\n", __func__);
NSString * path_resource = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
if (path_resource) {
GGML_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, [path_resource UTF8String]);
}
// resolve each kind's source path up front (file lookup/logging stays on the calling thread)
NSString ** path_per_kind = calloc(GGML_METAL_LIB_COUNT, sizeof(NSString *));
for (int kind = 0; kind < GGML_METAL_LIB_COUNT; ++kind) {
NSString * rel = [NSString stringWithFormat:@"kernels/%s.metal", k_lib_names[kind]];
NSString * path_source = nil;
if (path_resource) {
path_source = [path_resource stringByAppendingPathComponent:rel];
} else {
NSString * stem = [NSString stringWithFormat:@"kernels/%s", k_lib_names[kind]];
path_source = [bundle pathForResource:stem ofType:@"metal"];
}
if (path_source == nil || ![[NSFileManager defaultManager] isReadableFileAtPath:path_source]) {
GGML_LOG_WARN("%s: could not locate %s in bundle, falling back to cwd\n", __func__, [rel UTF8String]);
path_source = rel;
}
GGML_LOG_DEBUG("%s: loading '%s'\n", __func__, [path_source UTF8String]);
path_per_kind[kind] = [path_source retain];
}
const bool ok = ggml_metal_library_compile_all(res, device, prep,
^NSString * (int kind, NSError ** err) {
return ggml_metal_library_flatten_source(path_per_kind[kind], err);
}, "source");
for (int kind = 0; kind < GGML_METAL_LIB_COUNT; ++kind) {
[path_per_kind[kind] release];
}
free(path_per_kind);
if (!ok) {
ggml_metal_library_free(res);
return NULL;
}
return res;
#endif
}
ggml_metal_library_t ggml_metal_library_init_from_source(ggml_metal_device_t dev, const char * source, bool verbose) {
@@ -318,10 +589,11 @@ ggml_metal_library_t ggml_metal_library_init_from_source(ggml_metal_device_t dev
return NULL;
}
res->obj = library;
res->dev = dev;
res->pipelines = ggml_metal_pipelines_init();
res->lock = [NSLock new];
res->objs[0] = library;
res->single_library = true;
res->dev = dev;
res->pipelines = ggml_metal_pipelines_init();
res->lock = [NSLock new];
return res;
}
@@ -331,8 +603,14 @@ void ggml_metal_library_free(ggml_metal_library_t lib) {
return;
}
if (lib->obj) {
[lib->obj release];
for (int kind = 0; kind < GGML_METAL_LIB_COUNT; ++kind) {
if (lib->objs[kind]) {
[lib->objs[kind] release];
}
}
if (lib->fn_to_lib) {
[lib->fn_to_lib release];
}
ggml_metal_pipelines_free(lib->pipelines);
@@ -393,11 +671,28 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_compile_pipeline(ggml_
GGML_LOG_DEBUG("%s: compiling pipeline: base = '%s', name = '%s'\n", __func__, base, name);
// route to the library that actually defines this kernel; fn_to_lib is
// built from -[MTLLibrary functionNames] so it's always in sync
int lib_idx = 0;
if (!lib->single_library) {
NSNumber * idx = lib->fn_to_lib[base_func];
if (!idx) {
[lib->lock unlock];
GGML_LOG_ERROR("%s: kernel not found in any metal library: base = '%s', name = '%s'\n", __func__, base, name);
return res;
}
lib_idx = [idx intValue];
}
id<MTLLibrary> mtl_lib = lib->objs[lib_idx];
id<MTLFunction> mtl_function;
if (!cv) {
mtl_function = [lib->obj newFunctionWithName:base_func];
mtl_function = [mtl_lib newFunctionWithName:base_func];
} else {
mtl_function = [lib->obj newFunctionWithName:base_func constantValues:cv->obj error:&error];
mtl_function = [mtl_lib newFunctionWithName:base_func constantValues:cv->obj error:&error];
}
if (!mtl_function) {
[lib->lock unlock];
@@ -1334,7 +1629,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
return op->src[0]->type != GGML_TYPE_NVFP4;
case GGML_OP_SET_ROWS:
{
if (op->src[0]->type != GGML_TYPE_F32) {
if (op->src[0]->type != GGML_TYPE_F32 && op->src[0]->type != GGML_TYPE_F16) {
return false;
}
+1 -1
View File
@@ -1202,7 +1202,7 @@ int ggml_metal_op_set_rows(ggml_metal_op_t ctx, int idx) {
GGML_TENSOR_LOCALS( int32_t, ne, op, ne);
GGML_TENSOR_LOCALS(uint64_t, nb, op, nb);
auto pipeline = ggml_metal_library_get_pipeline_set_rows(lib, op->src[1]->type, op->type);
auto pipeline = ggml_metal_library_get_pipeline_set_rows(lib, op);
const int32_t nk0 = ne0/ggml_blck_size(op->type);
File diff suppressed because it is too large Load Diff
+232
View File
@@ -0,0 +1,232 @@
#include "common.h"
// bitonic sort implementation following the CUDA kernels as reference
typedef void (argsort_t)(
constant ggml_metal_kargs_argsort & args,
device const char * src0,
device int32_t * dst,
threadgroup int32_t * shmem_i32 [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort3 ntg[[threads_per_threadgroup]]);
template<ggml_sort_order order>
kernel void kernel_argsort_f32_i32(
constant ggml_metal_kargs_argsort & args,
device const char * src0,
device int32_t * dst,
threadgroup int32_t * shmem_i32 [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
// bitonic sort
const int col = tpitg[0];
const int ib = tgpig[0] / args.ne01;
const int i00 = ib*ntg.x;
const int i01 = tgpig[0] % args.ne01;
const int i02 = tgpig[1];
const int i03 = tgpig[2];
device const float * src0_row = (device const float *) (src0 + args.nb01*i01 + args.nb02*i02 + args.nb03*i03);
// initialize indices
shmem_i32[col] = i00 + col;
threadgroup_barrier(mem_flags::mem_threadgroup);
for (int k = 2; k <= ntg.x; k *= 2) {
for (int j = k / 2; j > 0; j /= 2) {
int ixj = col ^ j;
if (ixj > col) {
if ((col & k) == 0) {
if (shmem_i32[col] >= args.ne00 ||
(shmem_i32[ixj] < args.ne00 && (order == GGML_SORT_ORDER_ASC ?
src0_row[shmem_i32[col]] > src0_row[shmem_i32[ixj]] :
src0_row[shmem_i32[col]] < src0_row[shmem_i32[ixj]]))
) {
SWAP(shmem_i32[col], shmem_i32[ixj]);
}
} else {
if (shmem_i32[ixj] >= args.ne00 ||
(shmem_i32[col] < args.ne00 && (order == GGML_SORT_ORDER_ASC ?
src0_row[shmem_i32[col]] < src0_row[shmem_i32[ixj]] :
src0_row[shmem_i32[col]] > src0_row[shmem_i32[ixj]]))
) {
SWAP(shmem_i32[col], shmem_i32[ixj]);
}
}
}
threadgroup_barrier(mem_flags::mem_threadgroup);
}
}
const int64_t i0 = ib*args.top_k;
// copy the result to dst without the padding
if (i0 + col < args.ne0 && col < args.top_k) {
dst += i0 + args.ne0*i01 + args.ne0*args.ne1*i02 + args.ne0*args.ne1*args.ne2*i03;
dst[col] = shmem_i32[col];
}
}
template [[host_name("kernel_argsort_f32_i32_asc")]] kernel argsort_t kernel_argsort_f32_i32<GGML_SORT_ORDER_ASC>;
template [[host_name("kernel_argsort_f32_i32_desc")]] kernel argsort_t kernel_argsort_f32_i32<GGML_SORT_ORDER_DESC>;
typedef void (argsort_merge_t)(
constant ggml_metal_kargs_argsort_merge & args,
device const char * src0,
device const int32_t * tmp,
device int32_t * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort3 ntg[[threads_per_threadgroup]]);
template<ggml_sort_order order>
kernel void kernel_argsort_merge_f32_i32(
constant ggml_metal_kargs_argsort_merge & args,
device const char * src0,
device const int32_t * tmp,
device int32_t * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
const int im = tgpig[0] / args.ne01;
const int i01 = tgpig[0] % args.ne01;
const int i02 = tgpig[1];
const int i03 = tgpig[2];
const int start = im * (2 * args.len);
const int len0 = MIN(args.len, MAX(0, args.ne0 - (int)(start)));
const int len1 = MIN(args.len, MAX(0, args.ne0 - (int)(start + args.len)));
const int total = len0 + len1;
device const int32_t * tmp0 = tmp + start
+ i01*args.ne0
+ i02*args.ne0*args.ne01
+ i03*args.ne0*args.ne01*args.ne02;
device const int32_t * tmp1 = tmp0 + args.len;
dst += start
+ i01*args.top_k
+ i02*args.top_k*args.ne01
+ i03*args.top_k*args.ne01*args.ne02;
device const float * src0_row = (device const float *)(src0
+ args.nb01*i01
+ args.nb02*i02
+ args.nb03*i03);
if (total == 0) {
return;
}
const int chunk = (total + ntg.x - 1) / ntg.x;
const int k0 = tpitg.x * chunk;
const int k1 = MIN(MIN(k0 + chunk, total), args.top_k);
if (k0 >= args.top_k) {
return;
}
if (k0 >= total) {
return;
}
int low = k0 > len1 ? k0 - len1 : 0;
int high = MIN(k0, len0);
// binary-search partition (i, j) such that i + j = k
while (low < high) {
const int mid = (low + high) >> 1;
const int32_t idx0 = tmp0[mid];
const int32_t idx1 = tmp1[k0 - mid - 1];
const float val0 = src0_row[idx0];
const float val1 = src0_row[idx1];
bool take_left;
if (order == GGML_SORT_ORDER_ASC) {
take_left = (val0 <= val1);
} else {
take_left = (val0 >= val1);
}
if (take_left) {
low = mid + 1;
} else {
high = mid;
}
}
int i = low;
int j = k0 - i;
// keep the merge fronts into registers
int32_t idx0 = 0;
float val0 = 0.0f;
if (i < len0) {
idx0 = tmp0[i];
val0 = src0_row[idx0];
}
int32_t idx1 = 0;
float val1 = 0.0f;
if (j < len1) {
idx1 = tmp1[j];
val1 = src0_row[idx1];
}
for (int k = k0; k < k1; ++k) {
int32_t out_idx;
if (i >= len0) {
while (k < k1) {
dst[k++] = tmp1[j++];
}
break;
} else if (j >= len1) {
while (k < k1) {
dst[k++] = tmp0[i++];
}
break;
} else {
bool take_left;
if (order == GGML_SORT_ORDER_ASC) {
take_left = (val0 <= val1);
} else {
take_left = (val0 >= val1);
}
if (take_left) {
out_idx = idx0;
++i;
if (i < len0) {
idx0 = tmp0[i];
val0 = src0_row[idx0];
}
} else {
out_idx = idx1;
++j;
if (j < len1) {
idx1 = tmp1[j];
val1 = src0_row[idx1];
}
}
}
dst[k] = out_idx;
}
}
template [[host_name("kernel_argsort_merge_f32_i32_asc")]] kernel argsort_merge_t kernel_argsort_merge_f32_i32<GGML_SORT_ORDER_ASC>;
template [[host_name("kernel_argsort_merge_f32_i32_desc")]] kernel argsort_merge_t kernel_argsort_merge_f32_i32<GGML_SORT_ORDER_DESC>;
+226
View File
@@ -0,0 +1,226 @@
#include "common.h"
// OP: 0 - add, 1 - sub, 2 - mul, 3 - div
constant short FC_bin_op [[function_constant(FC_BIN + 0)]];
constant short FC_bin_f [[function_constant(FC_BIN + 1)]];
constant bool FC_bin_rb [[function_constant(FC_BIN + 2)]];
constant bool FC_bin_cb [[function_constant(FC_BIN + 3)]];
template <typename T0, typename T1, typename T>
kernel void kernel_bin_fuse_impl(
constant ggml_metal_kargs_bin & args,
device const char * src0,
device const char * src1,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
#define FC_OP FC_bin_op
#define FC_F FC_bin_f
#define FC_RB FC_bin_rb
#define FC_CB FC_bin_cb
if (FC_RB) {
// row broadcast
const uint i0 = tgpig.y*args.ne00 + tgpig.x;
const uint i1 = FC_CB ? tgpig.x%args.ne10 : tgpig.x;
device const T0 * src0_row = (device const T0 *) (src0);
device T * dst_row = (device T *) (dst);
if (FC_F == 1) {
device const T1 * src1_row = (device const T1 *) (src1 + args.o1[0]);
if (FC_OP == 0) {
dst_row[i0] = src0_row[i0] + src1_row[i1];
}
if (FC_OP == 1) {
dst_row[i0] = src0_row[i0] - src1_row[i1];
}
if (FC_OP == 2) {
dst_row[i0] = src0_row[i0] * src1_row[i1];
}
if (FC_OP == 3) {
dst_row[i0] = src0_row[i0] / src1_row[i1];
}
} else {
T0 res = src0_row[i0];
if (FC_OP == 0) {
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
res += ((device const T1 *) (src1 + args.o1[j]))[i1];
}
}
if (FC_OP == 1) {
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
res -= ((device const T1 *) (src1 + args.o1[j]))[i1];
}
}
if (FC_OP == 2) {
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
res *= ((device const T1 *) (src1 + args.o1[j]))[i1];
}
}
if (FC_OP == 3) {
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
res /= ((device const T1 *) (src1 + args.o1[j]))[i1];
}
}
dst_row[i0] = res;
}
} else {
const int i03 = tgpig.z;
const int i02 = tgpig.y;
const int i01 = tgpig.x;
if (i01 >= args.ne01) {
return;
}
const int i13 = i03%args.ne13;
const int i12 = i02%args.ne12;
const int i11 = i01%args.ne11;
device const T0 * src0_ptr = (device const T0 *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs);
device T * dst_ptr = (device T *) (dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1 + args.offs);
if (FC_F == 1) {
device const T1 * src1_ptr = (device const T1 *) (src1 + args.o1[0] + i13*args.nb13 + i12*args.nb12 + i11*args.nb11);
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
const int i10 = FC_CB ? i0%args.ne10 : i0;
if (FC_OP == 0) {
dst_ptr[i0] = src0_ptr[i0] + src1_ptr[i10];
}
if (FC_OP == 1) {
dst_ptr[i0] = src0_ptr[i0] - src1_ptr[i10];
}
if (FC_OP == 2) {
dst_ptr[i0] = src0_ptr[i0] * src1_ptr[i10];
}
if (FC_OP == 3) {
dst_ptr[i0] = src0_ptr[i0] / src1_ptr[i10];
}
}
} else {
device const T1 * src1_ptr[8];
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
src1_ptr[j] = (device const T1 *) (src1 + args.o1[j] + i13*args.nb13 + i12*args.nb12 + i11*args.nb11);
}
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
const int i10 = FC_CB ? i0%args.ne10 : i0;
T res = src0_ptr[i0];
if (FC_OP == 0) {
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
res += src1_ptr[j][i10];
}
}
if (FC_OP == 1) {
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
res -= src1_ptr[j][i10];
}
}
if (FC_OP == 2) {
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
res *= src1_ptr[j][i10];
}
}
if (FC_OP == 3) {
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
res /= src1_ptr[j][i10];
}
}
dst_ptr[i0] = res;
}
}
}
#undef FC_OP
#undef FC_F
#undef FC_RB
#undef FC_CB
}
typedef decltype(kernel_bin_fuse_impl<float, float, float>) kernel_bin_fuse_t;
template [[host_name("kernel_bin_fuse_f32_f32_f32")]] kernel kernel_bin_fuse_t kernel_bin_fuse_impl<float, float, float>;
template [[host_name("kernel_bin_fuse_f32_f32_f32_4")]] kernel kernel_bin_fuse_t kernel_bin_fuse_impl<float4, float4, float4>;
kernel void kernel_add_id(
constant ggml_metal_kargs_add_id & args,
device const char * src0,
device const char * src1,
device const char * src2,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
const int i1 = tgpig.x;
const int i2 = tgpig.y;
const int i11 = *((device const int32_t *) (src2 + i1*sizeof(int32_t) + i2*args.nb21));
const size_t nb1 = args.ne0 * sizeof(float);
const size_t nb2 = args.ne1 * nb1;
device float * dst_row = (device float *)((device char *)dst + i1*nb1 + i2*nb2);
device const float * src0_row = (device const float *)((device char *)src0 + i1*args.nb01 + i2*args.nb02);
device const float * src1_row = (device const float *)((device char *)src1 + i11*args.nb11);
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
dst_row[i0] = src0_row[i0] + src1_row[i0];
}
}
template<typename T>
kernel void kernel_repeat(
constant ggml_metal_kargs_repeat & args,
device const char * src0,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
const int i3 = tgpig.z;
const int i2 = tgpig.y;
const int i1 = tgpig.x;
const int i03 = i3%args.ne03;
const int i02 = i2%args.ne02;
const int i01 = i1%args.ne01;
device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01;
device char * dst_ptr = dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1;
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
const int i00 = i0%args.ne00;
*((device T *)(dst_ptr + i0*args.nb0)) = *((device T *)(src0_ptr + i00*args.nb00));
}
}
typedef decltype(kernel_repeat<float>) kernel_repeat_t;
template [[host_name("kernel_repeat_f32")]] kernel kernel_repeat_t kernel_repeat<float>;
template [[host_name("kernel_repeat_f16")]] kernel kernel_repeat_t kernel_repeat<half>;
#if defined(GGML_METAL_HAS_BF16)
template [[host_name("kernel_repeat_bf16")]] kernel kernel_repeat_t kernel_repeat<bfloat>;
#endif
template [[host_name("kernel_repeat_i32")]] kernel kernel_repeat_t kernel_repeat<int>;
template [[host_name("kernel_repeat_i16")]] kernel kernel_repeat_t kernel_repeat<short>;
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#pragma once
#include "ggml-metal-impl.h"
#include <metal_stdlib>
#ifdef GGML_METAL_HAS_TENSOR
#include <metal_tensor>
#include <MetalPerformancePrimitives/MetalPerformancePrimitives.h>
#endif
using namespace metal;
#define MAX(x, y) ((x) > (y) ? (x) : (y))
#define MIN(x, y) ((x) < (y) ? (x) : (y))
#define SWAP(x, y) { auto tmp = (x); (x) = (y); (y) = tmp; }
#define PAD2(x, n) (((x) + (n) - 1) & ~((n) - 1))
#define FOR_UNROLL(x) _Pragma("clang loop unroll(full)") for (x)
#define N_SIMDWIDTH 32 // assuming SIMD group size is 32
// ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf
//
// cmd:
// .../usr/bin/metal -dM -E -c ggml/src/ggml-metal/kernels/<src>.metal
// .../usr/bin/metal -dM -E -c -target air64-apple-ios14.0 ggml/src/ggml-metal/kernels/<src>.metal
//
#if __METAL_VERSION__ < 310 && defined(GGML_METAL_HAS_BF16)
#undef GGML_METAL_HAS_BF16
#endif
#if defined(GGML_METAL_HAS_BF16)
typedef matrix<bfloat, 4, 4> bfloat4x4;
typedef matrix<bfloat, 2, 4> bfloat2x4;
#endif
constexpr constant static float kvalues_iq4nl_f[16] = {
-127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f, 1.f, 13.f, 25.f, 38.f, 53.f, 69.f, 89.f, 113.f
};
constexpr constant static float kvalues_mxfp4_f[16] = {
0, .5f, 1.f, 1.5f, 2.f, 3.f, 4.f, 6.f, -0, -.5f, -1.f, -1.5f, -2.f, -3.f, -4.f, -6.f
};
static inline int best_index_int8(int n, constant float * val, float x) {
if (x <= val[0]) return 0;
if (x >= val[n-1]) return n-1;
int ml = 0, mu = n-1;
while (mu-ml > 1) {
int mav = (ml+mu)/2;
if (x < val[mav]) mu = mav; else ml = mav;
}
return x - val[mu-1] < val[mu] - x ? mu-1 : mu;
}
static inline float e8m0_to_fp32(uint8_t x) {
uint32_t bits;
if (x == 0) {
bits = 0x00400000;
} else {
bits = (uint32_t) x << 23;
}
return as_type<float>(bits);
}
static inline float dot(float x, float y) {
return x*y;
}
static inline float sum(float x) {
return x;
}
static inline float sum(float4 x) {
return x[0] + x[1] + x[2] + x[3];
}
enum ggml_sort_order {
GGML_SORT_ORDER_ASC,
GGML_SORT_ORDER_DESC,
};
constant float GELU_COEF_A = 0.044715f;
constant float GELU_QUICK_COEF = -1.702f;
constant float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
constant float SQRT_2_INV = 0.70710678118654752440084436210484f;
// based on Abramowitz and Stegun formula 7.1.26 or similar Hastings' approximation
// ref: https://www.johndcook.com/blog/python_erf/
constant float p_erf = 0.3275911f;
constant float a1_erf = 0.254829592f;
constant float a2_erf = -0.284496736f;
constant float a3_erf = 1.421413741f;
constant float a4_erf = -1.453152027f;
constant float a5_erf = 1.061405429f;
template<typename T>
inline T erf_approx(T x) {
T sign_x = sign(x);
x = fabs(x);
T t = 1.0f / (1.0f + p_erf * x);
T y = 1.0f - (((((a5_erf * t + a4_erf) * t) + a3_erf) * t + a2_erf) * t + a1_erf) * t * exp(-x * x);
return sign_x * y;
}
template<typename T> T elu_approx(T x);
template<> inline float elu_approx<float>(float x) {
return (x > 0.f) ? x : (exp(x) - 1);
}
template<> inline float4 elu_approx<float4>(float4 x) {
float4 res;
res[0] = (x[0] > 0.0f) ? x[0] : (exp(x[0]) - 1.0f);
res[1] = (x[1] > 0.0f) ? x[1] : (exp(x[1]) - 1.0f);
res[2] = (x[2] > 0.0f) ? x[2] : (exp(x[2]) - 1.0f);
res[3] = (x[3] > 0.0f) ? x[3] : (exp(x[3]) - 1.0f);
return res;
}
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#include "common.h"
typedef void (im2col_t)(
constant ggml_metal_kargs_im2col & args,
device const float * x,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tgpg[[threadgroups_per_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]);
template <typename T>
kernel void kernel_im2col(
constant ggml_metal_kargs_im2col & args,
device const float * x,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tgpg[[threadgroups_per_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
// const int64_t IC = tgpg[0];
const int64_t OH = tgpg[1];
const int64_t OW = tgpg[2];
const int64_t KH = ntg[1];
const int64_t KW = ntg[2];
int64_t in = tpitg[0];
const int64_t ikh = tpitg[1];
const int64_t ikw = tpitg[2];
const int64_t iic = tgpig[0];
const int64_t ioh = tgpig[1];
const int64_t iow = tgpig[2];
const int64_t iiw = iow*args.s0 + ikw*args.d0 - args.p0;
const int64_t iih = ioh*args.s1 + ikh*args.d1 - args.p1;
int64_t offset_dst = (in*OH*OW + ioh*OW + iow)*args.CHW + (iic*(KH*KW) + ikh*KW + ikw);
device T * pdst = (device T *) (dst);
if (iih < 0 || iih >= args.IH || iiw < 0 || iiw >= args.IW) {
while (in < args.N) {
pdst[offset_dst] = 0.0f;
offset_dst += ntg[0]*args.CHW*OH*OW;
in += ntg[0];
}
} else {
int64_t offset_src = in*args.ofs0 + iic*args.ofs1 + iih*args.IW + iiw;
while (in < args.N) {
pdst[offset_dst] = x[offset_src];
offset_dst += ntg[0]*args.CHW*OH*OW;
offset_src += ntg[0]*args.ofs0;
in += ntg[0];
}
}
}
template [[host_name("kernel_im2col_f32")]] kernel im2col_t kernel_im2col<float>;
template [[host_name("kernel_im2col_f16")]] kernel im2col_t kernel_im2col<half>;
// TODO: optimize
typedef void (im2col_ext_t)(
constant ggml_metal_kargs_im2col & args,
device const float * x,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tgpg[[threadgroups_per_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]);
template <typename T>
kernel void kernel_im2col_ext(
constant ggml_metal_kargs_im2col & args,
device const float * x,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tgpg[[threadgroups_per_grid]], // tgpg[0] = D x IC x KH x KW, CHW = IC x KH x KW
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) { // [M, 1, 1]
const int64_t KHW = (int64_t)args.KHW;
const int64_t d = tgpig[0] / args.CHW;
const int64_t chw = tgpig[0] % args.CHW;
const int64_t tgpig_0 = chw / KHW; // 0 ~ (IC - 1)
const int64_t HW = tgpig[0] % KHW;
const int64_t tpitg_0 = (d * ntg[0]) + tpitg[0];
if (tpitg_0 >= args.N) {
return;
}
const int64_t tpitg_1 = HW / args.KW;
const int64_t tpitg_2 = HW % args.KW;
const int64_t iiw = tgpig[2] * args.s0 + tpitg_2 * args.d0 - args.p0;
const int64_t iih = tgpig[1] * args.s1 + tpitg_1 * args.d1 - args.p1;
const int64_t offset_dst =
(tpitg_0 * tgpg[1] * tgpg[2] + tgpig[1] * tgpg[2] + tgpig[2]) * args.CHW +
(tgpig_0 * KHW + tpitg_1 * args.KW + tpitg_2);
device T * pdst = (device T *) (dst);
if (iih < 0 || iih >= args.IH || iiw < 0 || iiw >= args.IW) {
pdst[offset_dst] = 0.0f;
} else {
const int64_t offset_src = tpitg_0 * args.ofs0 + tgpig_0 * args.ofs1;
pdst[offset_dst] = x[offset_src + iih * args.IW + iiw];
}
}
template [[host_name("kernel_im2col_ext_f32")]] kernel im2col_ext_t kernel_im2col_ext<float>;
template [[host_name("kernel_im2col_ext_f16")]] kernel im2col_ext_t kernel_im2col_ext<half>;
template <typename T>
kernel void kernel_col2im_1d(
constant ggml_metal_kargs_col2im_1d & args,
device const T * col,
device T * dst,
uint tgpig [[threadgroup_position_in_grid]],
uint tpitg [[thread_position_in_threadgroup]],
uint ntg [[threads_per_threadgroup]]) {
const int idx = tgpig * ntg + tpitg;
if (idx >= args.T_out * args.OC) {
return;
}
const int t_out = idx % args.T_out;
const int oc = idx / args.T_out;
const int t_abs = t_out + args.p0; // absolute position in uncropped signal
int t_in_min = (t_abs - args.K + args.s0) / args.s0; // ceil((t_abs - K + 1) / s0)
if (t_in_min < 0) {
t_in_min = 0;
}
int t_in_max = t_abs / args.s0;
if (t_in_max >= args.T_in) {
t_in_max = args.T_in - 1;
}
float sum = 0.0f;
for (int t_in = t_in_min; t_in <= t_in_max; t_in++) {
const int k = t_abs - t_in * args.s0;
sum += float(col[(oc * args.K + k) + t_in * args.K_OC]);
}
dst[t_out + oc * args.T_out] = T(sum);
}
template [[host_name("kernel_col2im_1d_f32")]] kernel void kernel_col2im_1d<float>(constant ggml_metal_kargs_col2im_1d &, device const float *, device float *, uint, uint, uint);
template [[host_name("kernel_col2im_1d_f16")]] kernel void kernel_col2im_1d<half>(constant ggml_metal_kargs_col2im_1d &, device const half *, device half *, uint, uint, uint);
#if defined(GGML_METAL_HAS_BF16)
template [[host_name("kernel_col2im_1d_bf16")]] kernel void kernel_col2im_1d<bfloat>(constant ggml_metal_kargs_col2im_1d &, device const bfloat *, device bfloat *, uint, uint, uint);
#endif
template <typename TK>
kernel void kernel_conv_2d(
constant ggml_metal_kargs_conv_2d & args,
device const char * weights,
device const char * src,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tgpg[[threadgroups_per_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const uint threads_per_tg = ntg.x * ntg.y * ntg.z;
const uint tg_index = (tgpig.z * tgpg.y + tgpig.y) * tgpg.x + tgpig.x;
const uint local_thread = tpitg.z * (ntg.x * ntg.y) + tpitg.y * ntg.x + tpitg.x;
const uint thread_index = tg_index * threads_per_tg + local_thread;
const uint64_t total_threads = (uint64_t) threads_per_tg * tgpg.x * tgpg.y * tgpg.z;
const uint64_t total_outputs = (uint64_t) args.N * args.OC * args.OH * args.OW;
for (uint64_t index = thread_index; index < total_outputs; index += total_threads) {
uint64_t tmp = index;
const int32_t ow = tmp % args.OW; tmp /= args.OW;
const int32_t oh = tmp % args.OH; tmp /= args.OH;
const int32_t oc = tmp % args.OC; tmp /= args.OC;
const int32_t n = tmp;
float acc = 0.0f;
const int32_t base_x = ow*args.s0 - args.p0;
const int32_t base_y = oh*args.s1 - args.p1;
int32_t ky_start = 0;
if (base_y < 0) {
ky_start = (-base_y + args.d1 - 1)/args.d1;
}
int32_t ky_end = args.KH;
const int32_t y_max = args.IH - 1 - base_y;
if (y_max < 0) {
ky_end = ky_start;
} else if (base_y + (args.KH - 1)*args.d1 >= args.IH) {
ky_end = min(ky_end, y_max/args.d1 + 1);
}
int32_t kx_start = 0;
if (base_x < 0) {
kx_start = (-base_x + args.d0 - 1)/args.d0;
}
int32_t kx_end = args.KW;
const int32_t x_max = args.IW - 1 - base_x;
if (x_max < 0) {
kx_end = kx_start;
} else if (base_x + (args.KW - 1)*args.d0 >= args.IW) {
kx_end = min(kx_end, x_max/args.d0 + 1);
}
if (ky_start < ky_end && kx_start < kx_end) {
const uint64_t src_base_n = (uint64_t) n * args.nb13;
const uint64_t w_base_oc = (uint64_t) oc * args.nb03;
for (int32_t ic = 0; ic < args.IC; ++ic) {
const uint64_t src_base_nc = src_base_n + (uint64_t) ic * args.nb12;
const uint64_t w_base_ocic = w_base_oc + (uint64_t) ic * args.nb02;
for (int32_t ky = ky_start; ky < ky_end; ++ky) {
const int32_t iy = base_y + ky*args.d1;
const uint64_t src_base_row = src_base_nc + (uint64_t) iy * args.nb11;
const uint64_t w_base_row = w_base_ocic + (uint64_t) ky * args.nb01;
for (int32_t kx = kx_start; kx < kx_end; ++kx) {
const int32_t ix = base_x + kx*args.d0;
const uint64_t src_offs = src_base_row + (uint64_t) ix * args.nb10;
const uint64_t w_offs = w_base_row + (uint64_t) kx * args.nb00;
const float x = *(device const float *)(src + src_offs);
const float w = (float) (*(device const TK *)(weights + w_offs));
acc += x * w;
}
}
}
}
const uint64_t dst_offs =
(uint64_t) n * args.nb3 +
(uint64_t) oc * args.nb2 +
(uint64_t) oh * args.nb1 +
(uint64_t) ow * args.nb0;
*(device float *)(dst + dst_offs) = acc;
}
}
template [[host_name("kernel_conv_2d_f32_f32")]]
kernel void kernel_conv_2d<float>(
constant ggml_metal_kargs_conv_2d & args,
device const char * weights,
device const char * src,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tgpg[[threadgroups_per_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]);
template [[host_name("kernel_conv_2d_f16_f32")]]
kernel void kernel_conv_2d<half>(
constant ggml_metal_kargs_conv_2d & args,
device const char * weights,
device const char * src,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tgpg[[threadgroups_per_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]);
typedef void (conv_transpose_1d_t)(
constant ggml_metal_kargs_conv_transpose_1d & args,
device const float * src0,
device const float * src1,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tgpg[[threadgroups_per_grid]]);
template <typename T>
kernel void kernel_conv_transpose_1d(
constant ggml_metal_kargs_conv_transpose_1d & args,
device const T * src0,
device const float * src1,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tgpg[[threadgroups_per_grid]]) {
// For output position j on the time axis, only input positions
// i such that i*s0 <= j < i*s0 + K
// contribute -- i.e. i in [ceil((j - K + 1)/s0), floor(j/s0)]
// intersected with [0, IL-1]. That's at most ceil(K/s0) values
// (typically 2 for stride==K/2 transposed convs).
const int32_t j = tgpig[0];
const int32_t s0 = args.s0;
const int32_t K = args.K;
const int32_t IL = args.IL;
int32_t i_min;
{
int32_t a = j - K + 1;
i_min = a <= 0 ? 0 : (a + s0 - 1) / s0; // ceil(a/s0) for a>0
}
int32_t i_max = j / s0;
if (i_max > IL - 1) i_max = IL - 1;
float v = 0.0f;
if (i_min <= i_max) {
for (int64_t c = 0; c < args.IC; c++) {
const int32_t kernel_offset = c * tgpg[1] * K + K * tgpig[1];
const int32_t input_offset = c * IL;
for (int32_t i = i_min; i <= i_max; i++) {
v += float(src0[kernel_offset + j - i * s0]) * src1[input_offset + i];
}
}
}
device float * dst_ptr = (device float *) (dst + tgpig[0] * args.nb0 + tgpig[1] * args.nb1);
dst_ptr[0] = v;
}
template [[host_name("kernel_conv_transpose_1d_f32_f32")]]
kernel void kernel_conv_transpose_1d<float>(
constant ggml_metal_kargs_conv_transpose_1d & args,
device const float * src0,
device const float * src1,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tgpg[[threadgroups_per_grid]]);
template [[host_name("kernel_conv_transpose_1d_f16_f32")]]
kernel void kernel_conv_transpose_1d<half>(
constant ggml_metal_kargs_conv_transpose_1d & args,
device const half * src0,
device const float * src1,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tgpg[[threadgroups_per_grid]]);
typedef void (conv_transpose_2d_t)(
constant ggml_metal_kargs_conv_transpose_2d & args,
device const float * src0,
device const float * src1,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tgpg[[threadgroups_per_grid]]);
template <typename T>
kernel void kernel_conv_transpose_2d(
constant ggml_metal_kargs_conv_transpose_2d & args,
device const T * src0,
device const float * src1,
device char * dst,
threadgroup float * shared_sum [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t out_x = tgpig[0];
const int64_t out_y = tgpig[1];
const int64_t out_c = tgpig[2];
const int64_t kw = tpitg[0];
const int64_t kh = tpitg[1];
float v = 0.0f;
for (int64_t in_c = 0; in_c < args.IC; in_c++) {
int64_t in_y = out_y - kh;
if (in_y < 0 || in_y % args.s0) continue;
in_y /= args.s0;
if (in_y >= args.IH) continue;
int64_t in_x = out_x - kw;
if (in_x < 0 || in_x % args.s0) continue;
in_x /= args.s0;
if (in_x >= args.IW) continue;
const int64_t input_idx = (args.IW * args.IH) * in_c + (args.IW) * in_y + in_x;
const int64_t kernel_idx = (args.KH * args.KW * args.OC) * in_c + (args.KH * args.KW) * out_c + (args.KW) * kh + kw;
v += (float)src0[kernel_idx] * src1[input_idx];
}
const uint tid = tpitg.y * ntg.x + tpitg.x;
shared_sum[tid] = v;
threadgroup_barrier(mem_flags::mem_threadgroup);
if (tid == 0) {
float total = 0.0f;
const uint num_threads = ntg.x * ntg.y;
for (uint i = 0; i < num_threads; i++) {
total += shared_sum[i];
}
device float * dst_ptr = (device float *) (dst + out_x*args.nb0 + out_y * args.nb1 + out_c*args.nb2);
dst_ptr[0] = total;
}
}
template [[host_name("kernel_conv_transpose_2d_f32_f32")]]
kernel void kernel_conv_transpose_2d<float>(
constant ggml_metal_kargs_conv_transpose_2d & args,
device const float * src0,
device const float * src1,
device char * dst,
threadgroup float * shared_sum [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]);
template [[host_name("kernel_conv_transpose_2d_f16_f32")]]
kernel void kernel_conv_transpose_2d<half>(
constant ggml_metal_kargs_conv_transpose_2d & args,
device const half * src0,
device const float * src1,
device char * dst,
threadgroup float * shared_sum [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]);
template <typename T>
kernel void kernel_conv_3d(
constant ggml_metal_kargs_conv_3d & args,
device const char * src0, // Weights [IC * OC, KD, KH, KW]
device const char * src1, // Inputs [IC * N, ID, IH, IW]
device char * dst, // Outputs [OC * N, OD, OH, OW]
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]]) {
// 1. Un-flatten the spatial dimension from Grid X
int64_t spatial_idx = tgpig.x * 32 + tpitg.x;
if (spatial_idx >= args.OW * args.OH * args.OD) {
return; // Thread falls outside the spatial volume
}
int64_t od = spatial_idx / (args.OW * args.OH);
int64_t oh = (spatial_idx / args.OW) % args.OH;
int64_t ow = spatial_idx % args.OW;
// 2. Map Y to Channels, Z to Batch
int64_t oc = tgpig.y;
int64_t batch_idx = tgpig.z;
// 3. Calculate anchor coordinates in the Input volume
int64_t i_w_base = ow * args.s0 - args.p0;
int64_t i_h_base = oh * args.s1 - args.p1;
int64_t i_d_base = od * args.s2 - args.p2;
float sum = 0.0f;
// 4. Gather Loop (Iterate over Input Channels -> Depth -> Height -> Width)
for (int64_t ic = 0; ic < args.IC; ++ic) {
// ggml packs batch and channel together in the 4th dimension
int64_t src_cn_idx = batch_idx * args.IC + ic;
int64_t w_cn_idx = oc * args.IC + ic;
for (int64_t kz = 0; kz < args.KD; ++kz) {
int64_t id = i_d_base + kz * args.d2;
if (id < 0 || id >= args.ID) continue; // Boundary check (Padding)
for (int64_t ky = 0; ky < args.KH; ++ky) {
int64_t ih = i_h_base + ky * args.d1;
if (ih < 0 || ih >= args.IH) continue;
for (int64_t kx = 0; kx < args.KW; ++kx) {
int64_t iw = i_w_base + kx * args.d0;
if (iw < 0 || iw >= args.IW) continue;
// Convert multi-dimensional coordinates to flat byte offsets
int64_t w_idx = kx*args.nb00 + ky*args.nb01 + kz*args.nb02 + w_cn_idx*args.nb03;
int64_t i_idx = iw*args.nb10 + ih*args.nb11 + id*args.nb12 + src_cn_idx*args.nb13;
// Dereference memory and cast weights to f32 if they were f16
float w_val = (float)*(device const T*)((device const char*)src0 + w_idx);
float i_val = *(device const float*)((device const char*)src1 + i_idx);
sum += w_val * i_val;
}
}
}
}
// 5. Write the accumulated value out to RAM
int64_t dst_cn_idx = batch_idx * args.OC + oc;
int64_t d_idx = ow*args.nb0 + oh*args.nb1 + od*args.nb2 + dst_cn_idx*args.nb3;
*(device float*)(dst + d_idx) = sum;
}
// Explicit instantiations so the JIT compiler can find them by name
template [[host_name("kernel_conv_3d_f32_f32")]]
kernel void kernel_conv_3d<float>(
constant ggml_metal_kargs_conv_3d & args,
device const char * src0,
device const char * src1,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]]);
// Explicit instantiation for f16 weights
template [[host_name("kernel_conv_3d_f16_f32")]]
kernel void kernel_conv_3d<half>(
constant ggml_metal_kargs_conv_3d & args,
device const char * src0,
device const char * src1,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]]);
+686
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@@ -0,0 +1,686 @@
#pragma once
#include "common.h"
#define GGML_COMMON_DECL_METAL
#define GGML_COMMON_IMPL_METAL
#if defined(GGML_METAL_EMBED_LIBRARY)
__embed_ggml-common.h__
#else
#include "ggml-common.h"
#endif
#define QK_NL 16 // shared by mul_mm and get_rows_q instantiations
// NOTE: this is not dequantizing - we are simply fitting the template
template <typename type4x4>
void dequantize_f32(device const float4x4 * src, short il, thread type4x4 & reg) {
reg = (type4x4)(*src);
}
template <typename type4>
void dequantize_f32_t4(device const float4 * src, short il, thread type4 & reg) {
reg = (type4)(*src);
}
template <typename type4x4>
void dequantize_f16(device const half4x4 * src, short il, thread type4x4 & reg) {
reg = (type4x4)(*src);
}
template <typename type4>
void dequantize_f16_t4(device const half4 * src, short il, thread type4 & reg) {
reg = (type4)(*(src));
}
#if defined(GGML_METAL_HAS_BF16)
template <typename type4x4>
void dequantize_bf16(device const bfloat4x4 * src, short il, thread type4x4 & reg) {
reg = (type4x4)(*src);
}
template <typename type4>
void dequantize_bf16_t4(device const bfloat4 * src, short il, thread type4 & reg) {
reg = (type4)(*(src));
}
#endif
template <typename type4x4>
void dequantize_q1_0(device const block_q1_0 * xb, short il, thread type4x4 & reg) {
device const uint8_t * qs = xb->qs;
const float d = xb->d;
const float neg_d = -d;
const int byte_offset = il * 2; // il*16 bits = il*2 bytes
const uint8_t b0 = qs[byte_offset];
const uint8_t b1 = qs[byte_offset + 1];
float4x4 reg_f;
reg_f[0][0] = select(neg_d, d, bool(b0 & 0x01));
reg_f[0][1] = select(neg_d, d, bool(b0 & 0x02));
reg_f[0][2] = select(neg_d, d, bool(b0 & 0x04));
reg_f[0][3] = select(neg_d, d, bool(b0 & 0x08));
reg_f[1][0] = select(neg_d, d, bool(b0 & 0x10));
reg_f[1][1] = select(neg_d, d, bool(b0 & 0x20));
reg_f[1][2] = select(neg_d, d, bool(b0 & 0x40));
reg_f[1][3] = select(neg_d, d, bool(b0 & 0x80));
reg_f[2][0] = select(neg_d, d, bool(b1 & 0x01));
reg_f[2][1] = select(neg_d, d, bool(b1 & 0x02));
reg_f[2][2] = select(neg_d, d, bool(b1 & 0x04));
reg_f[2][3] = select(neg_d, d, bool(b1 & 0x08));
reg_f[3][0] = select(neg_d, d, bool(b1 & 0x10));
reg_f[3][1] = select(neg_d, d, bool(b1 & 0x20));
reg_f[3][2] = select(neg_d, d, bool(b1 & 0x40));
reg_f[3][3] = select(neg_d, d, bool(b1 & 0x80));
reg = (type4x4) reg_f;
}
template <typename type4>
void dequantize_q1_0_t4(device const block_q1_0 * xb, short il, thread type4 & reg) {
const float d = xb->d;
const float neg_d = -d;
const int base = il * 4;
const uint8_t byte = xb->qs[base / 8];
const int s = base % 8;
float4 reg_f;
reg_f[0] = select(neg_d, d, bool((byte >> (s )) & 1));
reg_f[1] = select(neg_d, d, bool((byte >> (s + 1)) & 1));
reg_f[2] = select(neg_d, d, bool((byte >> (s + 2)) & 1));
reg_f[3] = select(neg_d, d, bool((byte >> (s + 3)) & 1));
reg = (type4) reg_f;
}
template <typename type4x4>
void dequantize_q4_0(device const block_q4_0 * xb, short il, thread type4x4 & reg) {
device const uint16_t * qs = ((device const uint16_t *)xb + 1);
const float d1 = il ? (xb->d / 16.h) : xb->d;
const float d2 = d1 / 256.f;
const float md = -8.h * xb->d;
const ushort mask0 = il ? 0x00F0 : 0x000F;
const ushort mask1 = mask0 << 8;
float4x4 reg_f;
for (int i = 0; i < 8; i++) {
reg_f[i/2][2*(i%2) + 0] = d1 * (qs[i] & mask0) + md;
reg_f[i/2][2*(i%2) + 1] = d2 * (qs[i] & mask1) + md;
}
reg = (type4x4) reg_f;
}
template <typename type4>
void dequantize_q4_0_t4(device const block_q4_0 * xb, short il, thread type4 & reg) {
device const uint16_t * qs = ((device const uint16_t *)xb + 1);
const float d1 = (il/4) ? (xb->d / 16.h) : xb->d;
const float d2 = d1 / 256.f;
const float md = -8.h * xb->d;
const ushort mask0 = (il/4) ? 0x00F0 : 0x000F;
const ushort mask1 = mask0 << 8;
for (int i = 0; i < 2; i++) {
reg[2*i + 0] = d1 * (qs[2*(il%4) + i] & mask0) + md;
reg[2*i + 1] = d2 * (qs[2*(il%4) + i] & mask1) + md;
}
}
template <typename type4x4>
void dequantize_q4_1(device const block_q4_1 * xb, short il, thread type4x4 & reg) {
device const uint16_t * qs = ((device const uint16_t *)xb + 2);
const float d1 = il ? (xb->d / 16.h) : xb->d;
const float d2 = d1 / 256.f;
const float m = xb->m;
const ushort mask0 = il ? 0x00F0 : 0x000F;
const ushort mask1 = mask0 << 8;
float4x4 reg_f;
for (int i = 0; i < 8; i++) {
reg_f[i/2][2*(i%2) + 0] = ((qs[i] & mask0) * d1) + m;
reg_f[i/2][2*(i%2) + 1] = ((qs[i] & mask1) * d2) + m;
}
reg = (type4x4) reg_f;
}
template <typename type4>
void dequantize_q4_1_t4(device const block_q4_1 * xb, short il, thread type4 & reg) {
device const uint16_t * qs = ((device const uint16_t *)xb + 2);
const float d1 = (il/4) ? (xb->d / 16.h) : xb->d;
const float d2 = d1 / 256.f;
const float m = xb->m;
const ushort mask0 = (il/4) ? 0x00F0 : 0x000F;
const ushort mask1 = mask0 << 8;
for (int i = 0; i < 2; i++) {
reg[2*i + 0] = d1 * (qs[2*(il%4) + i] & mask0) + m;
reg[2*i + 1] = d2 * (qs[2*(il%4) + i] & mask1) + m;
}
}
template <typename type4x4>
void dequantize_q5_0(device const block_q5_0 * xb, short il, thread type4x4 & reg) {
device const uint16_t * qs = ((device const uint16_t *)xb + 3);
const float d = xb->d;
const float md = -16.h * xb->d;
const ushort mask = il ? 0x00F0 : 0x000F;
const uint32_t qh = *((device const uint32_t *)xb->qh);
const int x_mv = il ? 4 : 0;
const int gh_mv = il ? 12 : 0;
const int gh_bk = il ? 0 : 4;
float4x4 reg_f;
for (int i = 0; i < 8; i++) {
// extract the 5-th bits for x0 and x1
const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10;
const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10;
// combine the 4-bits from qs with the 5th bit
const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0);
const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1);
reg_f[i/2][2*(i%2) + 0] = d * x0 + md;
reg_f[i/2][2*(i%2) + 1] = d * x1 + md;
}
reg = (type4x4) reg_f;
}
template <typename type4>
void dequantize_q5_0_t4(device const block_q5_0 * xb, short il, thread type4 & reg) {
device const uint16_t * qs = ((device const uint16_t *)xb + 3);
const float d = xb->d;
const float md = -16.h * xb->d;
const ushort mask = (il/4) ? 0x00F0 : 0x000F;
const uint32_t qh = *((device const uint32_t *)xb->qh);
const int x_mv = (il/4) ? 4 : 0;
const int gh_mv = (il/4) ? 12 : 0;
const int gh_bk = (il/4) ? 0 : 4;
for (int ii = 0; ii < 2; ii++) {
int i = 2*(il%4) + ii;
// extract the 5-th bits for x0 and x1
const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10;
const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10;
// combine the 4-bits from qs with the 5th bit
const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0);
const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1);
reg[2*ii + 0] = d * x0 + md;
reg[2*ii + 1] = d * x1 + md;
}
}
template <typename type4x4>
void dequantize_q5_1(device const block_q5_1 * xb, short il, thread type4x4 & reg) {
device const uint16_t * qs = ((device const uint16_t *)xb + 4);
const float d = xb->d;
const float m = xb->m;
const ushort mask = il ? 0x00F0 : 0x000F;
const uint32_t qh = *((device const uint32_t *)xb->qh);
const int x_mv = il ? 4 : 0;
const int gh_mv = il ? 12 : 0;
const int gh_bk = il ? 0 : 4;
float4x4 reg_f;
for (int i = 0; i < 8; i++) {
// extract the 5-th bits for x0 and x1
const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10;
const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10;
// combine the 4-bits from qs with the 5th bit
const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0);
const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1);
reg_f[i/2][2*(i%2) + 0] = d * x0 + m;
reg_f[i/2][2*(i%2) + 1] = d * x1 + m;
}
reg = (type4x4) reg_f;
}
template <typename type4>
void dequantize_q5_1_t4(device const block_q5_1 * xb, short il, thread type4 & reg) {
device const uint16_t * qs = ((device const uint16_t *)xb + 4);
const float d = xb->d;
const float m = xb->m;
const ushort mask = (il/4) ? 0x00F0 : 0x000F;
const uint32_t qh = *((device const uint32_t *)xb->qh);
const int x_mv = (il/4) ? 4 : 0;
const int gh_mv = (il/4) ? 12 : 0;
const int gh_bk = (il/4) ? 0 : 4;
for (int ii = 0; ii < 2; ii++) {
int i = 2*(il%4) + ii;
// extract the 5-th bits for x0 and x1
const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10;
const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10;
// combine the 4-bits from qs with the 5th bit
const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0);
const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1);
reg[2*ii + 0] = d * x0 + m;
reg[2*ii + 1] = d * x1 + m;
}
}
template <typename type4x4>
void dequantize_q8_0(device const block_q8_0 *xb, short il, thread type4x4 & reg) {
device const int8_t * qs = ((device const int8_t *)xb->qs);
const float d = xb->d;
float4x4 reg_f;
for (int i = 0; i < 16; i++) {
reg_f[i/4][i%4] = (qs[i + 16*il] * d);
}
reg = (type4x4) reg_f;
}
template <typename type4>
void dequantize_q8_0_t4(device const block_q8_0 *xb, short il, thread type4 & reg) {
device const int8_t * qs = ((device const int8_t *)xb->qs);
const float d = xb->d;
for (int i = 0; i < 4; i++) {
reg[i] = (qs[4*(il%4) + i + 16*(il/4)] * d);
}
}
template <typename type4x4>
void dequantize_mxfp4(device const block_mxfp4 * xb, short il, thread type4x4 & reg) {
device const uint8_t * q2 = (device const uint8_t *)xb->qs;
const float d = e8m0_to_fp32(xb->e);
const uint8_t shr = il >= 1 ? 4 : 0;
for (int i = 0; i < 4; ++i) {
reg[i][0] = d * kvalues_mxfp4_f[(q2[4*i + 0] >> shr) & 0x0F];
reg[i][1] = d * kvalues_mxfp4_f[(q2[4*i + 1] >> shr) & 0x0F];
reg[i][2] = d * kvalues_mxfp4_f[(q2[4*i + 2] >> shr) & 0x0F];
reg[i][3] = d * kvalues_mxfp4_f[(q2[4*i + 3] >> shr) & 0x0F];
}
}
template <typename type4>
void dequantize_mxfp4_t4(device const block_mxfp4 * xb, short il, thread type4 & reg) {
device const uint8_t * q2 = (device const uint8_t *)xb->qs;
const float d = e8m0_to_fp32(xb->e);
const short il4 = il%4;
const uint8_t shr = il >= 4 ? 4 : 0;
reg[0] = d * kvalues_mxfp4_f[(q2[4*il4 + 0] >> shr) & 0x0F];
reg[1] = d * kvalues_mxfp4_f[(q2[4*il4 + 1] >> shr) & 0x0F];
reg[2] = d * kvalues_mxfp4_f[(q2[4*il4 + 2] >> shr) & 0x0F];
reg[3] = d * kvalues_mxfp4_f[(q2[4*il4 + 3] >> shr) & 0x0F];
}
template <typename type4x4>
void dequantize_q2_K(device const block_q2_K *xb, short il, thread type4x4 & reg) {
const float d = xb->d;
const float min = xb->dmin;
device const uint8_t * q = (device const uint8_t *)xb->qs;
float dl, ml;
uint8_t sc = xb->scales[il];
q = q + 32*(il/8) + 16*(il&1);
il = (il/2)%4;
half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h);
uchar mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3);
dl = d * (sc & 0xF) * coef, ml = min * (sc >> 4);
for (int i = 0; i < 16; ++i) {
reg[i/4][i%4] = dl * (q[i] & mask) - ml;
}
}
template <typename type4x4>
void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg) {
const half d_all = xb->d;
device const uint8_t * q = (device const uint8_t *)xb->qs;
device const uint8_t * h = (device const uint8_t *)xb->hmask;
device const int8_t * scales = (device const int8_t *)xb->scales;
q = q + 32 * (il/8) + 16 * (il&1);
h = h + 16 * (il&1);
uint8_t m = 1 << (il/2);
uint16_t kmask1 = (il/4)>1 ? ((il/4)>2 ? 192 : 48) : \
((il/4)>0 ? 12 : 3);
uint16_t kmask2 = il/8 ? 0xF0 : 0x0F;
uint16_t scale_2 = scales[il%8], scale_1 = scales[8 + il%4];
int16_t dl_int = (il/4)&1 ? (scale_2&kmask2) | ((scale_1&kmask1) << 2)
: (scale_2&kmask2) | ((scale_1&kmask1) << 4);
float dl = il<8 ? d_all * (dl_int - 32.f) : d_all * (dl_int / 16.f - 32.f);
const float ml = 4.f * dl;
il = (il/2) & 3;
const half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h);
const uint8_t mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3);
dl *= coef;
for (int i = 0; i < 16; ++i) {
reg[i/4][i%4] = dl * (q[i] & mask) - (h[i] & m ? 0 : ml);
}
}
static inline uchar2 get_scale_min_k4_just2(int j, int k, device const uchar * q) {
return j < 4 ? uchar2{uchar(q[j+0+k] & 63), uchar(q[j+4+k] & 63)}
: uchar2{uchar((q[j+4+k] & 0xF) | ((q[j-4+k] & 0xc0) >> 2)), uchar((q[j+4+k] >> 4) | ((q[j-0+k] & 0xc0) >> 2))};
}
template <typename type4x4>
void dequantize_q4_K(device const block_q4_K * xb, short il, thread type4x4 & reg) {
device const uchar * q = xb->qs;
short is = (il/4) * 2;
q = q + (il/4) * 32 + 16 * (il&1);
il = il & 3;
const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales);
const float d = il < 2 ? xb->d : xb->d / 16.h;
const float min = xb->dmin;
const float dl = d * sc[0];
const float ml = min * sc[1];
const ushort mask = il < 2 ? 0x0F : 0xF0;
for (int i = 0; i < 16; ++i) {
reg[i/4][i%4] = dl * (q[i] & mask) - ml;
}
}
template <typename type4x4>
void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg) {
device const uint8_t * q = xb->qs;
device const uint8_t * qh = xb->qh;
short is = (il/4) * 2;
q = q + 32 * (il/4) + 16 * (il&1);
qh = qh + 16 * (il&1);
uint8_t ul = 1 << (il/2);
il = il & 3;
const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales);
const float d = il < 2 ? xb->d : xb->d / 16.f;
const float min = xb->dmin;
const float dl = d * sc[0];
const float ml = min * sc[1];
const ushort mask = il<2 ? 0x0F : 0xF0;
const float qh_val = il<2 ? 16.f : 256.f;
for (int i = 0; i < 16; ++i) {
reg[i/4][i%4] = dl * ((q[i] & mask) + (qh[i] & ul ? qh_val : 0)) - ml;
}
}
template <typename type4x4>
void dequantize_q6_K(device const block_q6_K *xb, short il, thread type4x4 & reg) {
const half d_all = xb->d;
device const uint16_t * ql = (device const uint16_t *)xb->ql;
device const uint16_t * qh = (device const uint16_t *)xb->qh;
device const int8_t * scales = (device const int8_t *)xb->scales;
ql = ql + 32*(il/8) + 16*((il/2)&1) + 8*(il&1);
qh = qh + 16*(il/8) + 8*(il&1);
float sc = scales[(il%2) + 2 * ((il/2))];
il = (il/2) & 3;
const uint32_t kmask1 = il>1 ? (il>2 ? 0xC0C0C0C0 : 0x30303030) : (il>0 ? 0x0C0C0C0C : 0x03030303);
const uint32_t kmask2 = il>1 ? 0xF0F0F0F0 : 0x0F0F0F0F;
const float ml = d_all * sc * 32.f;
const float dl0 = d_all * sc;
const float dl1 = dl0 / 256.f;
const float dl2 = dl0 / (256.f * 256.f);
const float dl3 = dl0 / (256.f * 256.f * 256.f);
const uint8_t shr_h = il>2 ? 2 : 0;
const uint8_t shl_h = il>1 ? 0 : (il>0 ? 2 : 4);
const uint8_t shr_l = il>1 ? 4 : 0;
for (int i = 0; i < 4; ++i) {
const uint32_t low = (ql[2*i] | (uint32_t)(ql[2*i+1] << 16)) & kmask2;
const uint32_t high = (qh[2*i] | (uint32_t)(qh[2*i+1] << 16)) & kmask1;
const uint32_t q = ((high << shl_h) >> shr_h) | (low >> shr_l);
reg[i][0] = dl0 * ((half)(q & 0xFF)) - ml;
reg[i][1] = dl1 * ((float)(q & 0xFF00)) - ml;
reg[i][2] = dl2 * ((float)(q & 0xFF0000)) - ml;
reg[i][3] = dl3 * ((float)(q & 0xFF000000)) - ml;
}
}
template <typename type4x4>
void dequantize_iq2_xxs(device const block_iq2_xxs * xb, short il, thread type4x4 & reg) {
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
const float d = xb->d;
const int ib32 = il/2;
il = il%2;
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
// each block of 32 needs 2 uint32_t's for the quants & scale, so 4 uint16_t's.
device const uint16_t * q2 = xb->qs + 4*ib32;
const uint32_t aux32_g = q2[0] | (q2[1] << 16);
const uint32_t aux32_s = q2[2] | (q2[3] << 16);
thread const uint8_t * aux8 = (thread const uint8_t *)&aux32_g;
const float dl = d * (0.5f + (aux32_s >> 28)) * 0.25f;
constant uint8_t * grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+0]);
uint8_t signs = ksigns_iq2xs[(aux32_s >> 14*il) & 127];
for (int i = 0; i < 8; ++i) {
reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f);
}
grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+1]);
signs = ksigns_iq2xs[(aux32_s >> (14*il+7)) & 127];
for (int i = 0; i < 8; ++i) {
reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f);
}
}
template <typename type4x4>
void dequantize_iq2_xs(device const block_iq2_xs * xb, short il, thread type4x4 & reg) {
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
const float d = xb->d;
const int ib32 = il/2;
il = il%2;
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
device const uint16_t * q2 = xb->qs + 4*ib32;
const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f;
constant uint8_t * grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+0] & 511));
uint8_t signs = ksigns_iq2xs[q2[2*il+0] >> 9];
for (int i = 0; i < 8; ++i) {
reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f);
}
grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+1] & 511));
signs = ksigns_iq2xs[q2[2*il+1] >> 9];
for (int i = 0; i < 8; ++i) {
reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f);
}
}
template <typename type4x4>
void dequantize_iq3_xxs(device const block_iq3_xxs * xb, short il, thread type4x4 & reg) {
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
const float d = xb->d;
const int ib32 = il/2;
il = il%2;
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
device const uint8_t * q3 = xb->qs + 8*ib32;
device const uint16_t * gas = (device const uint16_t *)(xb->qs + QK_K/4) + 2*ib32;
const uint32_t aux32 = gas[0] | (gas[1] << 16);
const float dl = d * (0.5f + (aux32 >> 28)) * 0.5f;
constant uint8_t * grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+0]);
constant uint8_t * grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+1]);
uint8_t signs = ksigns_iq2xs[(aux32 >> 14*il) & 127];
for (int i = 0; i < 4; ++i) {
reg[0][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f);
reg[1][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f);
}
grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+2]);
grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+3]);
signs = ksigns_iq2xs[(aux32 >> (14*il+7)) & 127];
for (int i = 0; i < 4; ++i) {
reg[2][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f);
reg[3][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f);
}
}
template <typename type4x4>
void dequantize_iq3_s(device const block_iq3_s * xb, short il, thread type4x4 & reg) {
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
const float d = xb->d;
const int ib32 = il/2;
il = il%2;
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
device const uint8_t * qs = xb->qs + 8*ib32;
device const uint8_t * signs = xb->signs + 4*ib32 + 2*il;
const uint8_t qh = xb->qh[ib32] >> 4*il;
const float dl = d * (1 + 2*((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf));
constant uint8_t * grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+0] | ((qh << 8) & 256)));
constant uint8_t * grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+1] | ((qh << 7) & 256)));
for (int i = 0; i < 4; ++i) {
reg[0][i] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i+0]);
reg[1][i] = dl * grid2[i] * select(1, -1, signs[0] & kmask_iq2xs[i+4]);
}
grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+2] | ((qh << 6) & 256)));
grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+3] | ((qh << 5) & 256)));
for (int i = 0; i < 4; ++i) {
reg[2][i] = dl * grid1[i] * select(1, -1, signs[1] & kmask_iq2xs[i+0]);
reg[3][i] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i+4]);
}
}
template <typename type4x4>
void dequantize_iq2_s(device const block_iq2_s * xb, short il, thread type4x4 & reg) {
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
const float d = xb->d;
const int ib32 = il/2;
il = il%2;
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
device const uint8_t * signs = qs + QK_K/8;
const uint8_t qh = xb->qh[ib32] >> 4*il;
const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f;
constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[0] | ((qh << 8) & 0x300)));
constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[1] | ((qh << 6) & 0x300)));
for (int i = 0; i < 8; ++i) {
reg[i/4+0][i%4] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i]);
reg[i/4+2][i%4] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i]);
}
}
template <typename type4x4>
void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & reg) {
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
const int ib32 = il/2;
il = il%2;
const float d = xb->d;
device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
device const uint16_t * qh = xb->qh;
const float dl = d * (2*((qh[ib32] >> 12) & 7) + 1);
const float ml = dl * (qh[ib32] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA);
const uint16_t h = qh[ib32] >> 6*il;
constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((h << 8) & 0x700)));
constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((h << 5) & 0x700)));
for (int i = 0; i < 4; ++i) {
reg[0][i] = dl * (grid1[i] & 0xf) + ml;
reg[1][i] = dl * (grid1[i] >> 4) + ml;
reg[2][i] = dl * (grid2[i] & 0xf) + ml;
reg[3][i] = dl * (grid2[i] >> 4) + ml;
}
}
template <typename type4x4>
void dequantize_iq1_m(device const block_iq1_m * xb, short il, thread type4x4 & reg) {
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
const int ib32 = il/2;
il = il%2;
device const uint16_t * sc = (device const uint16_t *)xb->scales;
iq1m_scale_t scale;
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
const float d = scale.f16;
device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
device const uint8_t * qh = xb->qh + 2*ib32 + il;
const float dl = d * (2*((sc[ib32/2] >> (6*(ib32%2)+3*il)) & 7) + 1);
const float ml1 = dl * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
const float ml2 = dl * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700)));
for (int i = 0; i < 4; ++i) {
reg[0][i] = dl * (grid1[i] & 0xf) + ml1;
reg[1][i] = dl * (grid1[i] >> 4) + ml1;
reg[2][i] = dl * (grid2[i] & 0xf) + ml2;
reg[3][i] = dl * (grid2[i] >> 4) + ml2;
}
}
template <typename type4x4>
void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 & reg) {
device const uint16_t * q4 = (device const uint16_t *)xb->qs;
const float d = xb->d;
uint32_t aux32;
thread const uint8_t * q8 = (thread const uint8_t *)&aux32;
for (int i = 0; i < 4; ++i) {
aux32 = ((q4[2*i] | (q4[2*i+1] << 16)) >> 4*il) & 0x0f0f0f0f;
reg[i][0] = d * kvalues_iq4nl_f[q8[0]];
reg[i][1] = d * kvalues_iq4nl_f[q8[1]];
reg[i][2] = d * kvalues_iq4nl_f[q8[2]];
reg[i][3] = d * kvalues_iq4nl_f[q8[3]];
}
}
template <typename type4>
void dequantize_iq4_nl_t4(device const block_iq4_nl * xb, short il, thread type4 & reg) {
device const uint16_t * q4 = (device const uint16_t *)xb->qs;
const float d = xb->d;
uint32_t aux32;
thread const uint8_t * q8 = (thread const uint8_t *)&aux32;
aux32 = ((q4[2*(il%4)] | (q4[2*(il%4)+1] << 16)) >> 4*(il/4)) & 0x0f0f0f0f;
reg[0] = d * kvalues_iq4nl_f[q8[0]];
reg[1] = d * kvalues_iq4nl_f[q8[1]];
reg[2] = d * kvalues_iq4nl_f[q8[2]];
reg[3] = d * kvalues_iq4nl_f[q8[3]];
}
template <typename type4x4>
void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 & reg) {
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
const int ib32 = il/2;
il = il%2;
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
device const uint32_t * q4 = (device const uint32_t *)xb->qs + 4*ib32;
const int ls = ((xb->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((xb->scales_h >> 2*ib32) & 3) << 4);
const float d = (float)xb->d * (ls - 32);
uint32_t aux32;
thread const uint8_t * q8 = (thread const uint8_t *)&aux32;
for (int i = 0; i < 4; ++i) {
aux32 = (q4[i] >> 4*il) & 0x0f0f0f0f;
reg[i][0] = d * kvalues_iq4nl_f[q8[0]];
reg[i][1] = d * kvalues_iq4nl_f[q8[1]];
reg[i][2] = d * kvalues_iq4nl_f[q8[2]];
reg[i][3] = d * kvalues_iq4nl_f[q8[3]];
}
}
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@@ -0,0 +1,250 @@
#include "common.h"
constant short FC_gated_delta_net_ne20 [[function_constant(FC_GATED_DELTA_NET + 0)]];
constant short FC_gated_delta_net_ne30 [[function_constant(FC_GATED_DELTA_NET + 1)]];
constant short FC_gated_delta_net_K [[function_constant(FC_GATED_DELTA_NET + 2)]];
#if 1
template<short NSG>
kernel void kernel_gated_delta_net_impl(
constant ggml_metal_kargs_gated_delta_net & args,
device const char * q,
device const char * k,
device const char * v,
device const char * g,
device const char * b,
device const char * s,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
#define S_v FC_gated_delta_net_ne20
#define G FC_gated_delta_net_ne30
#define K FC_gated_delta_net_K
const uint tx = tpitg.x;
const uint ty = tpitg.y;
const uint i23 = tgpig.z; // B (n_seqs)
const uint i21 = tgpig.y; // H (head)
const uint i20 = tgpig.x*NSG + ty; // row within S_v
const uint i01 = i21 % args.ne01;
const uint i11 = i21 % args.ne11;
const float scale = 1.0f / sqrt((float)S_v);
// input state layout [S_v, S_v, H, n_seqs] (s0 only): per-seq stride is H*D.
// state is stored transposed: M[i20][is] = S[is][i20], so row i20 is contiguous
const uint state_in_base = (i23*args.ne21 + i21)*S_v*S_v + i20*S_v;
device const float * s_ptr = (device const float *) (s) + state_in_base;
float ls[NSG];
FOR_UNROLL (short j = 0; j < NSG; j++) {
const short is = tx*NSG + j;
ls[j] = s_ptr[is];
}
device float * dst_attn = (device float *) (dst) + (i23*args.ne22*args.ne21 + i21)*S_v + i20;
device const float * q_ptr = (device const float *) (q + i23*args.nb03 + i01*args.nb01);
device const float * k_ptr = (device const float *) (k + i23*args.nb13 + i11*args.nb11);
device const float * v_ptr = (device const float *) (v + i23*args.nb23 + i21*args.nb21);
device const float * b_ptr = (device const float *) (b) + (i23*args.ne22*args.ne21 + i21);
device const float * g_ptr = (device const float *) (g) + (i23*args.ne22*args.ne21 + i21)*G;
// snapshot slot mapping: slot 0 = most recent state, slot s = s tokens back.
// When n_tokens < K, only slots 0..n_tokens-1 are written; older slots are caller-owned.
// output state base offset: after attention scores
const uint attn_size = args.ne22 * args.ne21 * S_v * args.ne23;
// output state per-slot size: S_v * S_v * H * n_seqs
const uint state_size_per_snap = S_v * S_v * args.ne21 * args.ne23;
// per-(seq,head) offset within a slot
const uint state_out_base = (i23*args.ne21 + i21)*S_v*S_v + i20*S_v;
for (short t = 0; t < args.ne22; t++) {
float s_k = 0.0f;
if (G == 1) {
const float g_exp = exp(g_ptr[0]);
FOR_UNROLL (short j = 0; j < NSG; j++) {
const short is = tx*NSG + j;
ls[j] *= g_exp;
s_k += ls[j]*k_ptr[is];
}
} else {
// KDA
FOR_UNROLL (short j = 0; j < NSG; j++) {
const short is = tx*NSG + j;
ls[j] *= exp(g_ptr[is]);
s_k += ls[j]*k_ptr[is];
}
}
s_k = simd_sum(s_k);
const float d = (v_ptr[i20] - s_k)*b_ptr[0];
float y = 0.0f;
FOR_UNROLL (short j = 0; j < NSG; j++) {
const short is = tx*NSG + j;
ls[j] += k_ptr[is]*d;
y += ls[j]*q_ptr[is];
}
y = simd_sum(y);
if (tx == 0) {
dst_attn[t*args.ne21*S_v] = y*scale;
}
q_ptr += args.ns02;
k_ptr += args.ns12;
v_ptr += args.ns22;
b_ptr += args.ne21;
g_ptr += args.ne21*G;
if (K > 1) {
const int target_slot = (int)args.ne22 - 1 - (int)t;
if (target_slot >= 0 && target_slot < (int)K) {
device float * dst_state = (device float *) (dst) + attn_size + (uint)target_slot * state_size_per_snap + state_out_base;
FOR_UNROLL (short j = 0; j < NSG; j++) {
const short is = tx*NSG + j;
dst_state[is] = ls[j];
}
}
}
}
if (K == 1) {
device float * dst_state = (device float *) (dst) + attn_size + state_out_base;
FOR_UNROLL (short j = 0; j < NSG; j++) {
const short is = tx*NSG + j;
dst_state[is] = ls[j];
}
}
#undef S_v
#undef G
#undef K
}
typedef decltype(kernel_gated_delta_net_impl<4>) kernel_gated_delta_net_t;
template [[host_name("kernel_gated_delta_net_f32_1")]] kernel kernel_gated_delta_net_t kernel_gated_delta_net_impl<1>;
template [[host_name("kernel_gated_delta_net_f32_2")]] kernel kernel_gated_delta_net_t kernel_gated_delta_net_impl<2>;
template [[host_name("kernel_gated_delta_net_f32_4")]] kernel kernel_gated_delta_net_t kernel_gated_delta_net_impl<4>;
#else
// a simplified version of the above
// no performance improvement, so keep the above version for now
template<typename T, short NSG>
kernel void kernel_gated_delta_net_impl(
constant ggml_metal_kargs_gated_delta_net & args,
device const char * q,
device const char * k,
device const char * v,
device const char * g,
device const char * b,
device const char * s,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
#define S_v FC_gated_delta_net_ne20
#define G FC_gated_delta_net_ne30
const uint tx = tpitg.x;
const uint ty = tpitg.y;
const uint i23 = tgpig.z; // B
const uint i21 = tgpig.y; // H
const uint i20 = tgpig.x*NSG + ty;
const uint i01 = i21 % args.ne01;
const uint i11 = i21 % args.ne11;
const float scale = 1.0f / sqrt((float)S_v);
device const float * s_ptr = (device const float *) (s) + (i23*args.ne21 + i21)*S_v*S_v + i20;
float lsf[NSG];
FOR_UNROLL (short j = 0; j < NSG; j++) {
const short is = tx*NSG + j;
lsf[j] = s_ptr[is*S_v];
}
thread T * ls = (thread T *) (lsf);
device float * dst_attn = (device float *) (dst) + (i23*args.ne22*args.ne21 + i21)*S_v + i20;
device const float * q_ptr = (device const float *) (q + i23*args.nb03 + i01*args.nb01);
device const float * k_ptr = (device const float *) (k + i23*args.nb13 + i11*args.nb11);
device const float * v_ptr = (device const float *) (v + i23*args.nb23 + i21*args.nb21);
device const float * b_ptr = (device const float *) (b) + (i23*args.ne22*args.ne21 + i21);
device const float * g_ptr = (device const float *) (g) + (i23*args.ne22*args.ne21 + i21)*G;
for (short t = 0; t < args.ne22; t++) {
device const T * qt_ptr = (device const T *) (q_ptr);
device const T * kt_ptr = (device const T *) (k_ptr);
device const T * gt_ptr = (device const T *) (g_ptr);
if (G == 1) {
*ls *= exp(g_ptr[0]);
} else {
// KDA
*ls *= exp(gt_ptr[tx]);
}
const float s_k = simd_sum(dot(*ls, kt_ptr[tx]));
const float d = (v_ptr[i20] - s_k)*b_ptr[0];
*ls += kt_ptr[tx]*d;
const float y = simd_sum(dot(*ls, qt_ptr[tx]));
if (tx == 0) {
*dst_attn = y*scale;
}
q_ptr += args.ns02;
k_ptr += args.ns12;
v_ptr += args.ns22;
b_ptr += args.ne21;
g_ptr += args.ne21*G;
dst_attn += args.ne21*S_v;
}
device float * dst_state = (device float *) (dst) + args.ne23*args.ne22*args.ne21*S_v + (i23*args.ne21 + i21)*S_v*S_v + i20;
device T * dstt_state = (device T *) (dst_state);
FOR_UNROLL (short j = 0; j < NSG; j++) {
const short is = tx*NSG + j;
dst_state[is*S_v] = lsf[j];
}
#undef S_v
#undef G
}
typedef decltype(kernel_gated_delta_net_impl<float4, 4>) kernel_gated_delta_net_t;
template [[host_name("kernel_gated_delta_net_f32_1")]] kernel kernel_gated_delta_net_t kernel_gated_delta_net_impl<float, 1>;
template [[host_name("kernel_gated_delta_net_f32_2")]] kernel kernel_gated_delta_net_t kernel_gated_delta_net_impl<float2, 2>;
template [[host_name("kernel_gated_delta_net_f32_4")]] kernel kernel_gated_delta_net_t kernel_gated_delta_net_impl<float4, 4>;
#endif
+347
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@@ -0,0 +1,347 @@
#include "common.h"
kernel void kernel_argmax_f32(
constant ggml_metal_kargs_argmax & args,
device const char * src0,
device char * dst,
threadgroup char * shmem [[threadgroup(0)]],
uint tgpig[[threadgroup_position_in_grid]],
uint tpitg[[thread_position_in_threadgroup]],
uint sgitg[[simdgroup_index_in_threadgroup]],
uint tiisg[[thread_index_in_simdgroup]],
uint ntg[[threads_per_threadgroup]]) {
device const float * x_row = (device const float *) ((device const char *) src0 + tgpig * args.nb01);
float lmax = -INFINITY;
int32_t larg = -1;
for (int i00 = tpitg; i00 < args.ne00; i00 += ntg) {
if (x_row[i00] > lmax) {
lmax = x_row[i00];
larg = i00;
}
}
// find the argmax value in the block
float max_val = simd_max(lmax);
int32_t arg_val = simd_max(select(-1, larg, lmax == max_val));
device int32_t * dst_i32 = (device int32_t *) dst;
threadgroup float * shared_maxval = (threadgroup float *) shmem;
threadgroup int32_t * shared_argmax = (threadgroup int32_t *) shmem + N_SIMDWIDTH;
if (ntg > N_SIMDWIDTH) {
if (sgitg == 0) {
shared_maxval[tiisg] = -INFINITY;
shared_argmax[tiisg] = -1;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (tiisg == 0) {
shared_maxval[sgitg] = max_val;
shared_argmax[sgitg] = arg_val;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
max_val = shared_maxval[tiisg];
arg_val = shared_argmax[tiisg];
float max_val_reduced = simd_max(max_val);
int32_t arg_val_reduced = simd_max(select(-1, arg_val, max_val == max_val_reduced));
dst_i32[tgpig] = arg_val_reduced;
return;
}
dst_i32[tgpig] = arg_val;
}
kernel void kernel_diag_f32(
constant ggml_metal_kargs_diag & args,
device const char * src0,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
ushort tiitg[[thread_index_in_threadgroup]]) {
constexpr short NW = N_SIMDWIDTH;
const int32_t i3 = tgpig.z;
const int32_t i2 = tgpig.y;
const int32_t i1 = tgpig.x;
device const float * src0_ptr = (device const float *)(src0 + i2*args.nb02 + i3*args.nb03);
device float * dst_ptr = (device float *)(dst + i1*args.nb01 + i2*args.nb2 + i3*args.nb3);
for (int i0 = tiitg; i0 < args.ne0; i0 += NW) {
dst_ptr[i0] = i0 == i1 ? src0_ptr[i0] : 0.0f;
}
}
kernel void kernel_roll_f32(
constant ggml_metal_kargs_roll & args,
device const char * src0,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t i3 = tgpig.z;
const int64_t i2 = tgpig.y;
const int64_t i1 = tgpig.x;
device const float * src0_ptr = (device const float *) src0;
device float * dst_ptr = (device float *) dst;
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
// apply shifts and wrap around
int64_t i00 = i0 - args.s0;
int64_t i01 = i1 - args.s1;
int64_t i02 = i2 - args.s2;
int64_t i03 = i3 - args.s3;
if (i00 < 0) { i00 += args.ne00; } else if (i00 >= args.ne00) { i00 -= args.ne00; }
if (i01 < 0) { i01 += args.ne01; } else if (i01 >= args.ne01) { i01 -= args.ne01; }
if (i02 < 0) { i02 += args.ne02; } else if (i02 >= args.ne02) { i02 -= args.ne02; }
if (i03 < 0) { i03 += args.ne03; } else if (i03 >= args.ne03) { i03 -= args.ne03; }
int64_t src_idx = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00 + i00;
int64_t dst_idx = i3 *args.ne2 *args.ne1 *args.ne0 + i2 *args.ne1 *args.ne0 + i1 *args.ne0 + i0;
dst_ptr[dst_idx] = src0_ptr[src_idx];
}
}
template <typename T>
kernel void kernel_pad_impl(
constant ggml_metal_kargs_pad & args,
device const char * src0,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int32_t i3 = tgpig.z;
const int32_t i2 = tgpig.y;
const int32_t k0 = tgpig.x/args.ne1;
const int32_t i1 = tgpig.x - k0*args.ne1;
const int32_t i03 = i3;
const int32_t i02 = i2;
const int32_t i01 = i1;
device const T * src0_ptr = (device const T *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01);
device T * dst_ptr = (device T *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1);
for (int32_t l0 = 0; l0 < 1024; l0 += ntg.x) {
const int32_t i0 = k0*1024 + tpitg.x + l0;
if (i0 >= args.ne0) {
break;
}
if (i0 < args.ne00 && i1 < args.ne01 && i2 < args.ne02 && i3 < args.ne03) {
dst_ptr[i0] = src0_ptr[i0];
} else {
dst_ptr[i0] = 0.0f;
}
}
}
typedef decltype(kernel_pad_impl<float>) kernel_pad_t;
template [[host_name("kernel_pad_f32")]] kernel kernel_pad_t kernel_pad_impl<float>;
template [[host_name("kernel_pad_f32_4")]] kernel kernel_pad_t kernel_pad_impl<float4>;
// TODO: this is slow - optimize
kernel void kernel_pad_reflect_1d_f32(
constant ggml_metal_kargs_pad_reflect_1d & args,
device const char * src0,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tgpg[[threadgroups_per_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t i3 = tgpig.z;
const int64_t i2 = tgpig.y;
const int64_t i1 = tgpig.x;
const int64_t i03 = i3;
const int64_t i02 = i2;
const int64_t i01 = i1;
device const float * src0_ptr = (device const float *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01);
device float * dst_ptr = (device float *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1);
if (i1 < args.ne01 && i2 < args.ne02 && i3 < args.ne03) {
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
if (i0 < args.p0) {
dst_ptr[i0] = src0_ptr[args.p0 - i0];
} else if (i0 < args.ne0 - args.p1) {
dst_ptr[i0] = src0_ptr[i0 - args.p0];
} else {
dst_ptr[i0] = src0_ptr[(args.ne0 - args.p1 - args.p0) - (args.p1 + 1 - (args.ne0 - i0)) - 1];
}
}
}
}
kernel void kernel_arange_f32(
constant ggml_metal_kargs_arange & args,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
device float * dst_ptr = (device float *) dst;
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
dst_ptr[i0] = args.start + args.step * i0;
}
}
kernel void kernel_timestep_embedding_f32(
constant ggml_metal_kargs_timestep_embedding & args,
device const char * src0,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
int i = tgpig.x;
device float * embed_data = (device float *)(dst + i*args.nb1);
int half_ = args.dim / 2;
for (int j = tpitg.x; j < half_; j += ntg.x) {
float timestep = ((device float *)src0)[i];
float freq = (float)exp(-log((float)args.max_period) * j / half_);
float arg = timestep * freq;
embed_data[j ] = cos(arg);
embed_data[j + half_] = sin(arg);
}
if (args.dim % 2 != 0 && tpitg.x == 0) {
embed_data[2 * half_] = 0.f;
}
}
kernel void kernel_opt_step_adamw_f32(
constant ggml_metal_kargs_opt_step_adamw & args,
device float * x,
device const float * g,
device float * g_m,
device float * g_v,
device const float * pars,
uint gid[[thread_position_in_grid]]) {
if (gid >= args.np) {
return;
}
const float alpha = pars[0];
const float beta1 = pars[1];
const float beta2 = pars[2];
const float eps = pars[3];
const float wd = pars[4];
const float beta1h = pars[5];
const float beta2h = pars[6];
const float gi = g[gid];
const float gmi = g_m[gid] * beta1 + gi * (1.0f - beta1);
const float gvi = g_v[gid] * beta2 + gi * gi * (1.0f - beta2);
g_m[gid] = gmi;
g_v[gid] = gvi;
const float mh = gmi * beta1h;
const float vh = sqrt(gvi * beta2h) + eps;
x[gid] = x[gid] * (1.0f - alpha * wd) - alpha * mh / vh;
}
kernel void kernel_opt_step_sgd_f32(
constant ggml_metal_kargs_opt_step_sgd & args,
device float * x,
device const float * g,
device const float * pars,
uint gid[[thread_position_in_grid]]) {
if (gid >= args.np) {
return;
}
x[gid] = x[gid] * (1.0f - pars[0] * pars[1]) - pars[0] * g[gid];
}
template<typename T>
kernel void kernel_memset(
constant ggml_metal_kargs_memset & args,
device T * dst,
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = args.val;
}
typedef decltype(kernel_memset<int64_t>) kernel_memset_t;
template [[host_name("kernel_memset_i64")]] kernel kernel_memset_t kernel_memset<int64_t>;
constant short FC_count_equal_nsg [[function_constant(FC_COUNT_EQUAL + 0)]];
template<typename T>
kernel void kernel_count_equal(
constant ggml_metal_kargs_count_equal & args,
device const char * src0,
device const char * src1,
device atomic_int * dst,
threadgroup int32_t * shmem_i32 [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
const short NSG = FC_count_equal_nsg;
const int i3 = tgpig.z;
const int i2 = tgpig.y;
const int i1 = tgpig.x;
if (i3 >= args.ne03 || i2 >= args.ne02 || i1 >= args.ne01) {
return;
}
int sum = 0;
device const char * base0 = src0 + i1*args.nb01 + i2*args.nb02 + i3*args.nb03;
device const char * base1 = src1 + i1*args.nb11 + i2*args.nb12 + i3*args.nb13;
for (int64_t i0 = tpitg.x; i0 < args.ne00; i0 += ntg.x) {
const T v0 = *(device const T *)(base0 + i0*args.nb00);
const T v1 = *(device const T *)(base1 + i0*args.nb10);
sum += (v0 == v1);
}
sum = simd_sum(sum);
if (tiisg == 0) {
shmem_i32[sgitg] = sum;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (sgitg == 0) {
float v = 0.0f;
if (tpitg.x < NSG) {
v = shmem_i32[tpitg.x];
}
float total = simd_sum(v);
if (tpitg.x == 0) {
atomic_fetch_add_explicit(dst, (int32_t) total, memory_order_relaxed);
}
}
}
typedef decltype(kernel_count_equal<int32_t>) kernel_count_equal_t;
template [[host_name("kernel_count_equal_i32")]] kernel kernel_count_equal_t kernel_count_equal<int32_t>;
+838
View File
@@ -0,0 +1,838 @@
#include "common.h"
#include "dequantize.h"
constant bool FC_mul_mm_bc_inp [[function_constant(FC_MUL_MM + 0)]];
constant bool FC_mul_mm_bc_out [[function_constant(FC_MUL_MM + 1)]];
constant short FC_mul_mm_ne12 [[function_constant(FC_MUL_MM + 2)]];
constant short FC_mul_mm_ne13 [[function_constant(FC_MUL_MM + 3)]];
constant short FC_mul_mm_r2 [[function_constant(FC_MUL_MM + 4)]];
constant short FC_mul_mm_r3 [[function_constant(FC_MUL_MM + 5)]];
// each block_q contains 16*nl weights
#ifdef GGML_METAL_HAS_TENSOR
template<
typename SA, typename SA_4x4, typename SA_8x8,
typename SB, typename SB_2x4, typename SB_8x8,
typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread SA_4x4 &),
typename T0, typename T0_4x4, typename T1, typename T1_2x4>
kernel void kernel_mul_mm(
constant ggml_metal_kargs_mul_mm & args,
device const char * srcA,
device const char * srcB,
device char * dst,
threadgroup char * shmem [[threadgroup(0)]],
uint3 tgpig [[threadgroup_position_in_grid]],
ushort tiitg [[thread_index_in_threadgroup]],
ushort sgitg [[simdgroup_index_in_threadgroup]]) {
(void) sgitg;
// Matrix dimensions: A(M,K) x B(K,N) -> C(M,N)
const int K = args.ne00;
const int M = args.ne0;
const int N = args.ne1;
// Batch dimension handling
const int im = tgpig.z;
const int i12 = im % FC_mul_mm_ne12;
const int i13 = im / FC_mul_mm_ne12;
// Batch offsets for srcA and srcB
const uint64_t offset0 = (i12/FC_mul_mm_r2)*args.nb02 + (i13/FC_mul_mm_r3)*args.nb03;
// Tile dimensions
constexpr int NRB = SZ_SIMDGROUP * N_MM_BLOCK_X * N_MM_SIMD_GROUP_X;
constexpr int NRA = SZ_SIMDGROUP * N_MM_BLOCK_Y * N_MM_SIMD_GROUP_Y;
// Tile offsets in output matrix
const int ra = tgpig.y * NRA;
const int rb = tgpig.x * NRB;
// Threadgroup memory for dequantized A tile only
threadgroup SA * sa = (threadgroup SA *)(shmem);
// Work-item count for A loading
constexpr int A_WORK_ITEMS = NRA * N_MM_NK;
constexpr int NUM_THREADS = N_SIMDWIDTH * N_MM_SIMD_GROUP_X * N_MM_SIMD_GROUP_Y;
// tA wraps threadgroup memory
auto tA = tensor(sa, dextents<int32_t, 2>(N_MM_NK_TOTAL, NRA));
// tB wraps device memory directly
device T1 * ptrB = (device T1 *)(srcB + args.nb12*i12 + args.nb13*i13);
const int strideB = args.nb11 / sizeof(T1);
auto tB = tensor(ptrB, dextents<int32_t, 2>(K, N), array<int, 2>({1, strideB}));
// Configure matmul operation
mpp::tensor_ops::matmul2d<
mpp::tensor_ops::matmul2d_descriptor(
NRB, NRA, N_MM_NK_TOTAL, false, true, true,
mpp::tensor_ops::matmul2d_descriptor::mode::multiply_accumulate),
execution_simdgroups<N_MM_SIMD_GROUP_X * N_MM_SIMD_GROUP_Y>> mm;
auto cT = mm.get_destination_cooperative_tensor<decltype(tB), decltype(tA), float>();
// Accumulate partial results over K dimension
for (int loop_k = 0; loop_k < K; loop_k += N_MM_NK_TOTAL) {
// === PHASE 1: Dequantization of A into threadgroup memory ===
for (int work = tiitg; work < A_WORK_ITEMS; work += NUM_THREADS) {
const int row = work / N_MM_NK;
const int k_chunk = work % N_MM_NK;
const int k_pos = loop_k + k_chunk * 16;
const short k_base = k_chunk * 16;
// Bounds check: skip device read if row is out of matrix bounds
if (ra + row < M) {
if (is_same<T0_4x4, block_q>::value && FC_mul_mm_bc_inp) {
// Element-wise reads when K is not aligned (nb01 not aligned for half4x4/float4x4).
// MSL spec Table 2.5: half4x4 requires 8-byte alignment. When K is odd,
// nb01 = K*2 is not 8-byte aligned, so odd-row pointers are misaligned.
// Mirrors the legacy kernel's existing guard.
device const T0 * row_ptr = (device const T0 *)(srcA + args.nb01 * (ra + row) + offset0);
FOR_UNROLL (short i = 0; i < 16; i++) {
sa[row * N_MM_NK_TOTAL + (k_base + i)] = (k_pos + i < K) ? (SA) row_ptr[k_pos + i] : (SA)0;
}
} else {
const int block_idx = k_pos / (16 * nl);
const short il = (k_pos / 16) % nl;
device const block_q * row_ptr = (device const block_q *)(srcA + args.nb01 * (ra + row) + offset0);
SA_4x4 temp_a;
dequantize_func(row_ptr + block_idx, il, temp_a);
FOR_UNROLL (short i = 0; i < 16; i++) {
// Zero-pad A for K positions beyond valid range (handles partial K iterations)
sa[row * N_MM_NK_TOTAL + (k_base + i)] = (k_pos + i < K) ? temp_a[i/4][i%4] : (SA)0;
}
}
} else {
// Zero-pad rows beyond matrix bounds
FOR_UNROLL (short i = 0; i < 16; i++) {
sa[row * N_MM_NK_TOTAL + (k_base + i)] = (SA)0;
}
}
}
threadgroup_barrier(mem_flags::mem_threadgroup);
// === PHASE 2: Tensor matmul ===
auto mA = tA.slice(0, 0);
auto mB = tB.slice(loop_k, rb);
mm.run(mB, mA, cT);
threadgroup_barrier(mem_flags::mem_threadgroup);
}
// Store result tile to output matrix (with batch offset)
// cT.store handles bounds checking via tD's extents (M, N)
device float * dstBatch = (device float *)dst + im * N * M;
auto tD = tensor(dstBatch, dextents<int32_t, 2>(M, N), array<int, 2>({1, M}));
cT.store(tD.slice(ra, rb));
}
#else
template<
typename S0, typename S0_4x4, typename S0_8x8,
typename S1, typename S1_2x4, typename S1_8x8,
typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread S0_4x4 &),
typename T0, typename T0_4x4, typename T1, typename T1_2x4>
kernel void kernel_mul_mm(
constant ggml_metal_kargs_mul_mm & args,
device const char * src0,
device const char * src1,
device char * dst,
threadgroup char * shmem [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
ushort tiitg[[thread_index_in_threadgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]]) {
threadgroup S0 * sa = (threadgroup S0 *)(shmem);
threadgroup S1 * sb = (threadgroup S1 *)(shmem + 4096);
constexpr int NR0 = 64;
constexpr int NR1 = 32;
constexpr int NK = 32;
constexpr int NL0 = NK/16;
constexpr int NL1 = NK/8;
const int im = tgpig.z;
const int r0 = tgpig.y*NR0;
const int r1 = tgpig.x*NR1;
// if this block is of 64x32 shape or smaller
const short nr0 = (args.ne0 - r0 < NR0) ? (args.ne0 - r0) : NR0;
const short nr1 = (args.ne1 - r1 < NR1) ? (args.ne1 - r1) : NR1;
// a thread shouldn't load data outside of the matrix
const short lr0 = ((short)tiitg/NL0) < nr0 ? ((short)tiitg/NL0) : nr0 - 1; // 0 .. 63
const short lr1 = ((short)tiitg/NL1) < nr1 ? ((short)tiitg/NL1) : nr1 - 1; // 0 .. 31
const short il0 = (tiitg % NL0);
short il = il0;
const int i12 = im % FC_mul_mm_ne12;
const int i13 = im / FC_mul_mm_ne12;
const uint64_t offset0 = (i12/FC_mul_mm_r2)*args.nb02 + (i13/FC_mul_mm_r3)*args.nb03;
const short offset1 = il0/nl;
device const block_q * x = (device const block_q *)(src0 + args.nb01*(r0 + lr0) + offset0) + offset1;
const short iy = 8*(tiitg % NL1);
device const T1 * y = (device const T1 *)(src1
+ args.nb13*i13
+ args.nb12*i12
+ args.nb11*(r1 + lr1)
+ args.nb10*iy);
S0_8x8 ma[4];
S1_8x8 mb[2];
simdgroup_float8x8 mc[8];
for (short i = 0; i < 8; i++){
mc[i] = make_filled_simdgroup_matrix<float, 8>(0.f);
}
for (int loop_k = 0; loop_k < args.ne00; loop_k += NK) {
// load data and store to threadgroup memory
if (is_same<T0_4x4, block_q>::value && FC_mul_mm_bc_inp) {
threadgroup_barrier(mem_flags::mem_threadgroup);
// no need for dequantization
for (short i = 0; i < 16; i++) {
const short sx = 2*il0 + i/8;
const short sy = (tiitg/NL0)/8;
//const short lx = i%8;
//const short ly = (tiitg/NL0)%8;
const short lx = (tiitg/NL0)%8;
const short ly = i%8;
const short ib = 8*sx + sy;
*(sa + 64*ib + 8*ly + lx) = loop_k + 16*il + i < args.ne00 ? *((device T0 *) x + i) : 0;
}
} else {
S0_4x4 temp_a;
dequantize_func(x, il, temp_a);
threadgroup_barrier(mem_flags::mem_threadgroup);
FOR_UNROLL (short i = 0; i < 16; i++) {
const short sx = 2*il0 + i/8;
const short sy = (tiitg/NL0)/8;
//const short lx = i%8;
//const short ly = (tiitg/NL0)%8;
const short lx = (tiitg/NL0)%8;
const short ly = i%8;
const short ib = 8*sx + sy;
// NOTE: this is massively slower.. WTF?
//sa[64*ib + 8*ly + lx] = temp_a[i/4][i%4];
*(sa + 64*ib + 8*ly + lx) = temp_a[i/4][i%4];
}
}
if (FC_mul_mm_bc_inp) {
for (short i = 0; i < 8; ++i) {
const short sx = (tiitg%NL1);
const short sy = (tiitg/NL1)/8;
const short lx = i;
const short ly = (tiitg/NL1)%8;
//const short lx = (tiitg/NL1)%8;
//const short ly = i;
const short ib = 4*sx + sy;
*(sb + 64*ib + 8*ly + lx) = loop_k + iy + i < args.ne00 ? (S1) *((device T1 *) y + i) : 0;
}
} else {
const short sx = (tiitg%NL1);
const short sy = (tiitg/NL1)/8;
//const short dx = sx;
//const short dy = sy;
const short ly = (tiitg/NL1)%8;
const short ib = 4*sx + sy;
*(threadgroup S1_2x4 *)(sb + 64*ib + 8*ly) = (S1_2x4)(*((device T1_2x4 *) y));
}
il = (il + 2 < nl) ? il + 2 : il % 2;
x = (il < 2) ? x + (2 + nl - 1)/nl : x;
y += NK;
threadgroup_barrier(mem_flags::mem_threadgroup);
// load matrices from threadgroup memory and conduct outer products
threadgroup const S0 * lsma = (sa + 4*64*(sgitg%2));
threadgroup const S1 * lsmb = (sb + 2*64*(sgitg/2));
FOR_UNROLL (short ik = 0; ik < NK/8; ik++) {
simdgroup_barrier(mem_flags::mem_none);
FOR_UNROLL (short i = 0; i < 4; i++) {
simdgroup_load(ma[i], lsma + 64*i, 8, 0, false);
}
simdgroup_barrier(mem_flags::mem_none);
FOR_UNROLL (short i = 0; i < 2; i++) {
simdgroup_load(mb[i], lsmb + 64*i, 8, 0, false);
}
simdgroup_barrier(mem_flags::mem_none);
FOR_UNROLL (short i = 0; i < 8; i++){
simdgroup_multiply_accumulate(mc[i], mb[i/4], ma[i%4], mc[i]);
}
lsma += 8*64;
lsmb += 4*64;
}
}
if (!FC_mul_mm_bc_out || (r0 + NR0 <= args.ne0 && r1 + NR1 <= args.ne1)) {
// if no bounds checks on the output are needed, we can directly write to device memory
device float * C = (device float *) dst +
(r0 + 32*(sgitg & 1)) + \
(r1 + 16*(sgitg >> 1)) * args.ne0 + im*args.ne1*args.ne0;
for (short i = 0; i < 8; i++) {
simdgroup_store(mc[i], C + 8*(i%4) + 8*args.ne0*(i/4), args.ne0, 0, false);
}
} else {
// block is smaller than 64x32, we should avoid writing data outside of the matrix
threadgroup_barrier(mem_flags::mem_threadgroup);
threadgroup float * temp_str = ((threadgroup float *) shmem) + 32*(sgitg&1) + (16*(sgitg >> 1))*NR0;
for (short i = 0; i < 8; i++) {
simdgroup_store(mc[i], temp_str + 8*(i%4) + 8*NR0*(i/4), NR0, 0, false);
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (sgitg == 0) {
for (int j = tiitg; j < nr1; j += NR1) {
device float * D = (device float *) dst + r0 + (r1 + j)*args.ne0 + im*args.ne1*args.ne0;
device float4 * D4 = (device float4 *) D;
threadgroup float * C = temp_str + (j*NR0);
threadgroup float4 * C4 = (threadgroup float4 *) C;
int i = 0;
for (; i < nr0/4; i++) {
*(D4 + i) = *(C4 + i);
}
i *= 4;
for (; i < nr0; i++) {
*(D + i) = *(C + i);
}
}
}
}
}
#endif // GGML_METAL_HAS_TENSOR
template<short ne20> // n_expert_used
kernel void kernel_mul_mm_id_map0(
constant ggml_metal_kargs_mul_mm_id_map0 & args,
device const char * src2,
device char * htpe,
device char * hids,
threadgroup char * shmem [[threadgroup(0)]],
ushort tpitg[[thread_position_in_threadgroup]],
ushort ntg[[threads_per_threadgroup]]) {
const short ide = tpitg; // expert id
uint32_t n_all = 0;
device int32_t * ids_i32 = (device int32_t *) hids + ide*args.ne21;
for (int i21 = 0; i21 < args.ne21; i21 += ntg) { // n_tokens
if (i21 + tpitg < args.ne21) {
device const int32_t * src2_i32 = (device const int32_t *) (src2 + (i21 + tpitg)*args.nb21);
threadgroup uint16_t * sids = (threadgroup uint16_t *) shmem + tpitg*ne20;
#pragma unroll(ne20)
for (short i20 = 0; i20 < ne20; i20++) {
sids[i20] = src2_i32[i20];
}
}
threadgroup_barrier(mem_flags::mem_threadgroup);
for (short t = 0; t < ntg; t++) {
if (i21 + t >= args.ne21) {
break;
}
threadgroup const uint16_t * sids = (threadgroup const uint16_t *) shmem + t*ne20;
short sel = 0;
#pragma unroll(ne20)
for (short i20 = 0; i20 < ne20; i20++) {
sel += (sids[i20] == ide)*(i20 + 1);
}
ids_i32[n_all] = (i21 + t)*ne20 + sel - 1;
n_all += sel > 0;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
}
device uint32_t * tpe_u32 = (device uint32_t *) (htpe);
tpe_u32[ide] = n_all;
}
typedef decltype(kernel_mul_mm_id_map0<1>) kernel_mul_mm_id_map0_t;
template [[host_name("kernel_mul_mm_id_map0_ne20_1" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<1>;
template [[host_name("kernel_mul_mm_id_map0_ne20_2" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<2>;
template [[host_name("kernel_mul_mm_id_map0_ne20_4" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<4>;
template [[host_name("kernel_mul_mm_id_map0_ne20_5" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<5>;
template [[host_name("kernel_mul_mm_id_map0_ne20_6" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<6>;
template [[host_name("kernel_mul_mm_id_map0_ne20_8" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<8>;
template [[host_name("kernel_mul_mm_id_map0_ne20_10")]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<10>;
template [[host_name("kernel_mul_mm_id_map0_ne20_16")]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<16>;
template [[host_name("kernel_mul_mm_id_map0_ne20_22")]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<22>;
template<typename S0, typename S0_4x4, typename S0_8x8, typename S1, typename S1_2x4, typename S1_8x8, typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread S0_4x4 &), typename T0, typename T0_4x4, typename T1, typename T1_2x4>
kernel void kernel_mul_mm_id(
constant ggml_metal_kargs_mul_mm_id & args,
device const char * src0,
device const char * src1,
device const char * htpe,
device const char * hids,
device char * dst,
threadgroup char * shmem [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
ushort tiitg[[thread_index_in_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]]) {
threadgroup S0 * sa = (threadgroup S0 *)(shmem);
threadgroup S1 * sb = (threadgroup S1 *)(shmem + 4096);
#ifdef GGML_METAL_HAS_TENSOR
threadgroup float * sc = (threadgroup float *)(shmem);
#endif
constexpr int NR0 = 64;
constexpr int NR1 = 32;
constexpr int NK = 32;
constexpr int NL0 = NK/16;
constexpr int NL1 = NK/8;
const int im = tgpig.z; // expert
const int r0 = tgpig.y*NR0;
const int r1 = tgpig.x*NR1;
device const uint32_t * tpe_u32 = (device const uint32_t *) (htpe);
device const int32_t * ids_i32 = (device const int32_t *) (hids);
const int32_t neh1 = tpe_u32[im];
if (r1 >= neh1) {
return;
}
// if this block is of 64x32 shape or smaller
const short nr0 = (args.ne0 - r0 < NR0) ? (args.ne0 - r0) : NR0;
const short nr1 = ( neh1 - r1 < NR1) ? ( neh1 - r1) : NR1;
// a thread shouldn't load data outside of the matrix
const short lr0 = ((short)tiitg/NL0) < nr0 ? ((short)tiitg/NL0) : nr0 - 1; // 0 .. 63
const short lr1 = ((short)tiitg/NL1) < nr1 ? ((short)tiitg/NL1) : nr1 - 1; // 0 .. 31
const short il0 = (tiitg % NL0);
short il = il0;
const int id = ids_i32[im*args.ne21 + r1 + lr1];
const short i11 = (id % args.ne20) % args.ne11;
const short i12 = (id / args.ne20);
const short i13 = 0;
const uint64_t offset0 = im*args.nb02 + i13*args.nb03;
const short offset1 = il0/nl;
device const block_q * x = (device const block_q *)(src0 + args.nb01*(r0 + lr0) + offset0) + offset1;
const short iy = 8*(tiitg % NL1);
device const T1 * y = (device const T1 *)(src1
+ args.nb13*i13
+ args.nb12*i12
+ args.nb11*i11
+ args.nb10*iy);
#ifndef GGML_METAL_HAS_TENSOR
S0_8x8 ma[4];
S1_8x8 mb[2];
simdgroup_float8x8 mc[8];
for (short i = 0; i < 8; i++){
mc[i] = make_filled_simdgroup_matrix<float, 8>(0.f);
}
#else
auto tA = tensor<threadgroup S0, dextents<int32_t, 2>, tensor_inline>(sa, dextents<int32_t, 2>(NK, NR0));
auto tB = tensor<threadgroup S1, dextents<int32_t, 2>, tensor_inline>(sb, dextents<int32_t, 2>(NR1, NK ));
mpp::tensor_ops::matmul2d<
mpp::tensor_ops::matmul2d_descriptor(NR1, NR0, NK, false, true, false, mpp::tensor_ops::matmul2d_descriptor::mode::multiply_accumulate),
execution_simdgroups<4>> mm;
auto cT = mm.get_destination_cooperative_tensor<decltype(tA), decltype(tB), float>();
#endif
for (int loop_k = 0; loop_k < args.ne00; loop_k += NK) {
#ifndef GGML_METAL_HAS_TENSOR
// load data and store to threadgroup memory
if (is_same<T0_4x4, block_q>::value && FC_mul_mm_bc_inp) {
threadgroup_barrier(mem_flags::mem_threadgroup);
// no need for dequantization
for (short i = 0; i < 16; i++) {
const short sx = 2*il0 + i/8;
const short sy = (tiitg/NL0)/8;
//const short lx = i%8;
//const short ly = (tiitg/NL0)%8;
const short lx = (tiitg/NL0)%8;
const short ly = i%8;
const short ib = 8*sx + sy;
*(sa + 64*ib + 8*ly + lx) = loop_k + 16*il + i < args.ne00 ? (S0) *((device T0 *) x + i) : (S0) 0;
}
} else {
S0_4x4 temp_a;
dequantize_func(x, il, temp_a);
threadgroup_barrier(mem_flags::mem_threadgroup);
FOR_UNROLL (short i = 0; i < 16; i++) {
const short sx = 2*il0 + i/8;
const short sy = (tiitg/NL0)/8;
//const short lx = i%8;
//const short ly = (tiitg/NL0)%8;
const short lx = (tiitg/NL0)%8;
const short ly = i%8;
const short ib = 8*sx + sy;
// NOTE: this is massively slower.. WTF?
//sa[64*ib + 8*ly + lx] = temp_a[i/4][i%4];
*(sa + 64*ib + 8*ly + lx) = temp_a[i/4][i%4];
}
}
if (FC_mul_mm_bc_inp) {
for (short i = 0; i < 8; ++i) {
const short sx = (tiitg%NL1);
const short sy = (tiitg/NL1)/8;
const short lx = i;
const short ly = (tiitg/NL1)%8;
//const short lx = (tiitg/NL1)%8;
//const short ly = i;
const short ib = 4*sx + sy;
*(sb + 64*ib + 8*ly + lx) = loop_k + iy + i < args.ne00 ? (S1) *((device T1 *) y + i) : 0;
}
} else {
const short sx = (tiitg%NL1);
const short sy = (tiitg/NL1)/8;
//const short dx = sx;
//const short dy = sy;
const short ly = (tiitg/NL1)%8;
const short ib = 4*sx + sy;
*(threadgroup S1_2x4 *)(sb + 64*ib + 8*ly) = (S1_2x4)(*((device T1_2x4 *) y));
}
#else
// load data and store to threadgroup memory
if (is_same<T0_4x4, block_q>::value && FC_mul_mm_bc_inp) {
threadgroup_barrier(mem_flags::mem_threadgroup);
// no need for dequantization
for (short i = 0; i < 16; i++) {
const short sx = 2*il0 + i/8;
const short sy = (tiitg/NL0)/8;
const short lx = i%8;
const short ly = (tiitg/NL0)%8;
//const short lx = (tiitg/NL0)%8;
//const short ly = i%8;
*(sa + NK*(8*sy + ly) + 8*sx + lx) = loop_k + 16*il + i < args.ne00 ? *((device T0 *) x + i) : 0;
}
} else {
S0_4x4 temp_a;
dequantize_func(x, il, temp_a);
threadgroup_barrier(mem_flags::mem_threadgroup);
FOR_UNROLL (short i = 0; i < 16; i++) {
const short sx = 2*il0 + i/8;
const short sy = (tiitg/NL0)/8;
const short lx = i%8;
const short ly = (tiitg/NL0)%8;
//const short lx = (tiitg/NL0)%8;
//const short ly = i%8;
*(sa + NK*(8*sy + ly) + 8*sx + lx) = temp_a[i/4][i%4];
}
}
if (FC_mul_mm_bc_inp) {
for (short i = 0; i < 8; ++i) {
const short sx = (tiitg%NL1);
const short sy = (tiitg/NL1)/8;
const short lx = i;
const short ly = (tiitg/NL1)%8;
//const short lx = (tiitg/NL1)%8;
//const short ly = i;
*(sb + NK*(8*sy + ly) + 8*sx + lx) = loop_k + iy + i < args.ne00 ? (S1) *((device T1 *) y + i) : 0;
}
} else {
const short sx = (tiitg%NL1);
const short sy = (tiitg/NL1)/8;
//const short lx = i;
const short ly = (tiitg/NL1)%8;
//const short lx = (tiitg/NL1)%8;
//const short ly = i;
*(threadgroup S1_2x4 *)(sb + NK*(8*sy + ly) + 8*sx) = (S1_2x4)(*((device T1_2x4 *) y));
}
#endif
il = (il + 2 < nl) ? il + 2 : il % 2;
x = (il < 2) ? x + (2 + nl - 1)/nl : x;
y += NK;
threadgroup_barrier(mem_flags::mem_threadgroup);
#ifndef GGML_METAL_HAS_TENSOR
// load matrices from threadgroup memory and conduct outer products
threadgroup const S0 * lsma = (sa + 4*64*(sgitg%2));
threadgroup const S1 * lsmb = (sb + 2*64*(sgitg/2));
FOR_UNROLL (short ik = 0; ik < NK/8; ik++) {
simdgroup_barrier(mem_flags::mem_none);
FOR_UNROLL (short i = 0; i < 4; i++) {
simdgroup_load(ma[i], lsma + 64*i, 8, 0, false);
}
simdgroup_barrier(mem_flags::mem_none);
FOR_UNROLL (short i = 0; i < 2; i++) {
simdgroup_load(mb[i], lsmb + 64*i, 8, 0, false);
}
simdgroup_barrier(mem_flags::mem_none);
FOR_UNROLL (short i = 0; i < 8; i++){
simdgroup_multiply_accumulate(mc[i], mb[i/4], ma[i%4], mc[i]);
}
lsma += 8*64;
lsmb += 4*64;
}
#else
auto sA = tA.slice(0, 0);
auto sB = tB.slice(0, 0);
mm.run(sB, sA, cT);
#endif
}
// block is smaller than 64x32, we should avoid writing data outside of the matrix
threadgroup_barrier(mem_flags::mem_threadgroup);
#ifdef GGML_METAL_HAS_TENSOR
auto tC = tensor<threadgroup float, dextents<int32_t, 2>, tensor_inline>(sc, dextents<int32_t, 2>(NR0, NR1));
cT.store(tC);
#else
threadgroup float * temp_str = ((threadgroup float *) shmem) + 32*(sgitg&1) + (16*(sgitg >> 1))*NR0;
for (short i = 0; i < 8; i++) {
simdgroup_store(mc[i], temp_str + 8*(i%4) + 8*NR0*(i/4), NR0, 0, false);
}
#endif
threadgroup_barrier(mem_flags::mem_threadgroup);
for (short j = sgitg; j < nr1; j += 4) {
const int id = ids_i32[im*args.ne21 + r1 + j];
const short ide = id % args.ne20;
const short idt = id / args.ne20;
device float * D = (device float *) dst + r0 + ide*args.ne0 + idt*args.ne1*args.ne0;
device float4 * D4 = (device float4 *) D;
threadgroup float * C = (threadgroup float *) shmem + j*NR0;
threadgroup float4 * C4 = (threadgroup float4 *) C;
int i = tiisg;
for (; i < nr0/4; i += 32) {
*(D4 + i) = *(C4 + i);
}
i = (4*(nr0/4)) + tiisg;
for (; i < nr0; i += 32) {
*(D + i) = *(C + i);
}
}
}
//
// matrix-matrix multiplication
//
typedef decltype(kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, float4x4, 1, dequantize_f32, float, float4x4, float, float2x4>) mul_mm_t;
template [[host_name("kernel_mul_mm_f32_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, float4x4, 1, dequantize_f32, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_f16_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, half4x4, 1, dequantize_f16, half, half4x4, float, float2x4>;
#if defined(GGML_METAL_HAS_BF16)
template [[host_name("kernel_mul_mm_bf16_f32")]] kernel mul_mm_t kernel_mul_mm<bfloat, bfloat4x4, simdgroup_bfloat8x8, bfloat, bfloat2x4, simdgroup_bfloat8x8, bfloat4x4, 1, dequantize_bf16, bfloat, bfloat4x4, float, float2x4>;
#endif
template [[host_name("kernel_mul_mm_q1_0_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q1_0, 8, dequantize_q1_0, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_q4_0_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_0, 2, dequantize_q4_0, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_q4_1_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_1, 2, dequantize_q4_1, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_q5_0_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_0, 2, dequantize_q5_0, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_q5_1_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_1, 2, dequantize_q5_1, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_q8_0_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q8_0, 2, dequantize_q8_0, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_mxfp4_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_mxfp4, 2, dequantize_mxfp4, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_q2_K_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q2_K, QK_NL, dequantize_q2_K, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_q3_K_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q3_K, QK_NL, dequantize_q3_K, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_q4_K_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_K, QK_NL, dequantize_q4_K, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_q5_K_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_K, QK_NL, dequantize_q5_K, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_q6_K_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q6_K, QK_NL, dequantize_q6_K, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_xxs, QK_NL, dequantize_iq2_xxs, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_iq2_xs_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_xs, QK_NL, dequantize_iq2_xs, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq3_xxs, QK_NL, dequantize_iq3_xxs, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_iq3_s_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq3_s, QK_NL, dequantize_iq3_s, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_iq2_s_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_s, QK_NL, dequantize_iq2_s, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_iq1_s_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq1_s, QK_NL, dequantize_iq1_s, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_iq1_m_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq1_m, QK_NL, dequantize_iq1_m, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq4_nl, 2, dequantize_iq4_nl, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq4_xs, QK_NL, dequantize_iq4_xs, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_f32_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, float4x4, 1, dequantize_f32, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_f16_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, half4x4, 1, dequantize_f16, half, half4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_q1_0_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q1_0, 8, dequantize_q1_0, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_q4_0_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_0, 2, dequantize_q4_0, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_q4_1_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_1, 2, dequantize_q4_1, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_q5_0_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_0, 2, dequantize_q5_0, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_q5_1_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_1, 2, dequantize_q5_1, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_q8_0_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q8_0, 2, dequantize_q8_0, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_mxfp4_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_mxfp4, 2, dequantize_mxfp4, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_q2_K_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q2_K, QK_NL, dequantize_q2_K, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_q3_K_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q3_K, QK_NL, dequantize_q3_K, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_q4_K_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_K, QK_NL, dequantize_q4_K, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_q5_K_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_K, QK_NL, dequantize_q5_K, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_q6_K_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q6_K, QK_NL, dequantize_q6_K, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_iq2_xxs_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_xxs, QK_NL, dequantize_iq2_xxs, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_iq2_xs_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_xs, QK_NL, dequantize_iq2_xs, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_iq3_xxs_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq3_xxs, QK_NL, dequantize_iq3_xxs, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_iq3_s_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq3_s, QK_NL, dequantize_iq3_s, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_iq2_s_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_s, QK_NL, dequantize_iq2_s, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_iq1_s_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq1_s, QK_NL, dequantize_iq1_s, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_iq1_m_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq1_m, QK_NL, dequantize_iq1_m, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_iq4_nl_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq4_nl, 2, dequantize_iq4_nl, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_iq4_xs_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq4_xs, QK_NL, dequantize_iq4_xs, float, float4x4, half, half2x4>;
//
// indirect matrix-matrix multiplication
//
typedef decltype(kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, float4x4, 1, dequantize_f32, float, float4x4, float, float2x4>) mul_mm_id;
template [[host_name("kernel_mul_mm_id_f32_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, float4x4, 1, dequantize_f32, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_f16_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, half4x4, 1, dequantize_f16, half, half4x4, float, float2x4>;
#if defined(GGML_METAL_HAS_BF16)
template [[host_name("kernel_mul_mm_id_bf16_f32")]] kernel mul_mm_id kernel_mul_mm_id<bfloat, bfloat4x4, simdgroup_bfloat8x8, bfloat, bfloat2x4, simdgroup_bfloat8x8, bfloat4x4, 1, dequantize_bf16, bfloat, bfloat4x4, float, float2x4>;
#endif
template [[host_name("kernel_mul_mm_id_q1_0_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q1_0, 8, dequantize_q1_0, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_q4_0_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_0, 2, dequantize_q4_0, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_q4_1_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_1, 2, dequantize_q4_1, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_q5_0_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_0, 2, dequantize_q5_0, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_q5_1_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_1, 2, dequantize_q5_1, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_q8_0_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q8_0, 2, dequantize_q8_0, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_mxfp4_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_mxfp4, 2, dequantize_mxfp4, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_q2_K_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q2_K, QK_NL, dequantize_q2_K, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_q3_K_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q3_K, QK_NL, dequantize_q3_K, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_q4_K_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_K, QK_NL, dequantize_q4_K, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_q5_K_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_K, QK_NL, dequantize_q5_K, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_q6_K_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q6_K, QK_NL, dequantize_q6_K, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_iq2_xxs_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_xxs, QK_NL, dequantize_iq2_xxs, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_iq2_xs_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_xs, QK_NL, dequantize_iq2_xs, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_iq3_xxs_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq3_xxs, QK_NL, dequantize_iq3_xxs, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_iq3_s_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq3_s, QK_NL, dequantize_iq3_s, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_iq2_s_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_s, QK_NL, dequantize_iq2_s, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_iq1_s_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq1_s, QK_NL, dequantize_iq1_s, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_iq1_m_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq1_m, QK_NL, dequantize_iq1_m, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq4_nl, 2, dequantize_iq4_nl, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq4_xs, QK_NL, dequantize_iq4_xs, float, float4x4, float, float2x4>;
template [[host_name("kernel_mul_mm_id_f32_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, float4x4, 1, dequantize_f32, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_f16_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, half4x4, 1, dequantize_f16, half, half4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_q1_0_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q1_0, 8, dequantize_q1_0, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_q4_0_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_0, 2, dequantize_q4_0, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_q4_1_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_1, 2, dequantize_q4_1, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_q5_0_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_0, 2, dequantize_q5_0, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_q5_1_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_1, 2, dequantize_q5_1, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_q8_0_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q8_0, 2, dequantize_q8_0, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_mxfp4_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_mxfp4, 2, dequantize_mxfp4, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_q2_K_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q2_K, QK_NL, dequantize_q2_K, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_q3_K_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q3_K, QK_NL, dequantize_q3_K, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_q4_K_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_K, QK_NL, dequantize_q4_K, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_q5_K_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_K, QK_NL, dequantize_q5_K, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_q6_K_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q6_K, QK_NL, dequantize_q6_K, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_iq2_xxs_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_xxs, QK_NL, dequantize_iq2_xxs, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_iq2_xs_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_xs, QK_NL, dequantize_iq2_xs, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_iq3_xxs_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq3_xxs, QK_NL, dequantize_iq3_xxs, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_iq3_s_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq3_s, QK_NL, dequantize_iq3_s, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_iq2_s_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_s, QK_NL, dequantize_iq2_s, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_iq1_s_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq1_s, QK_NL, dequantize_iq1_s, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_iq1_m_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq1_m, QK_NL, dequantize_iq1_m, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_iq4_nl_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq4_nl, 2, dequantize_iq4_nl, float, float4x4, half, half2x4>;
template [[host_name("kernel_mul_mm_id_iq4_xs_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq4_xs, QK_NL, dequantize_iq4_xs, float, float4x4, half, half2x4>;
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#include "common.h"
// F == 1 : norm (no fuse)
// F == 2 : norm + mul
// F == 3 : norm + mul + add
template <typename T, short F>
kernel void kernel_norm_fuse_impl(
constant ggml_metal_kargs_norm & args,
device const char * src0,
device const char * src1_0,
device const char * src1_1,
device char * dst,
threadgroup float * shmem_f32 [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
if (sgitg == 0) {
shmem_f32[tiisg] = 0.0f;
}
const int i01 = tgpig.x;
const int i02 = tgpig.y;
const int i03 = tgpig.z;
device const T * x = (device const T *) (src0 + i03*args.nbf3[0] + i02*args.nbf2[0] + i01*args.nbf1[0]);
device const T * f0 = (device const T *) (src1_0 + (i03%args.nef3[1])*args.nbf3[1] + (i02%args.nef2[1])*args.nbf2[1] + (i01%args.nef1[1])*args.nbf1[1]);
device const T * f1 = (device const T *) (src1_1 + (i03%args.nef3[2])*args.nbf3[2] + (i02%args.nef2[2])*args.nbf2[2] + (i01%args.nef1[2])*args.nbf1[2]);
T sumft(0.0f);
float sumf = 0.0f;
for (int i00 = tpitg.x; i00 < args.ne00_t; i00 += ntg.x) {
sumft += x[i00];
}
sumf = dot(sumft, T(1.0f));
sumf = simd_sum(sumf);
threadgroup_barrier(mem_flags::mem_threadgroup);
if (tiisg == 0) {
shmem_f32[sgitg] = sumf;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
sumf = shmem_f32[tiisg];
sumf = simd_sum(sumf);
const float mean = sumf/args.ne00;
device T * y = (device T *) (dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1);
sumf = 0.0f;
for (int i00 = tpitg.x; i00 < args.ne00_t; i00 += ntg.x) {
y[i00] = x[i00] - mean;
sumf += dot(y[i00], y[i00]);
}
sumf = simd_sum(sumf);
threadgroup_barrier(mem_flags::mem_threadgroup);
if (tiisg == 0) {
shmem_f32[sgitg] = sumf;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
sumf = shmem_f32[tiisg];
sumf = simd_sum(sumf);
const float variance = sumf/args.ne00;
const float scale = 1.0f/sqrt(variance + args.eps);
for (int i00 = tpitg.x; i00 < args.ne00_t; i00 += ntg.x) {
if (F == 1) {
y[i00] = (y[i00]*scale);
}
if (F == 2) {
y[i00] = (y[i00]*scale)*f0[i00];
}
if (F == 3) {
y[i00] = (y[i00]*scale)*f0[i00] + f1[i00];
}
}
}
typedef decltype(kernel_norm_fuse_impl<float4, 1>) kernel_norm_fuse_t;
template [[host_name("kernel_norm_f32")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float, 1>;
template [[host_name("kernel_norm_mul_f32")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float, 2>;
template [[host_name("kernel_norm_mul_add_f32")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float, 3>;
template [[host_name("kernel_norm_f32_4")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float4, 1>;
template [[host_name("kernel_norm_mul_f32_4")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float4, 2>;
template [[host_name("kernel_norm_mul_add_f32_4")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float4, 3>;
// F == 1 : rms_norm (no fuse)
// F == 2 : rms_norm + mul
// F == 3 : rms_norm + mul + add
template <typename T, short F>
kernel void kernel_rms_norm_fuse_impl(
constant ggml_metal_kargs_norm & args,
device const char * src0,
device const char * src1_0,
device const char * src1_1,
device char * dst,
threadgroup float * shmem_f32 [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
if (sgitg == 0) {
shmem_f32[tiisg] = 0.0f;
}
const int i01 = tgpig.x;
const int i02 = tgpig.y;
const int i03 = tgpig.z;
device const T * x = (device const T *) (src0 + i03*args.nbf3[0] + i02*args.nbf2[0] + i01*args.nbf1[0]);
device const T * f0 = (device const T *) (src1_0 + (i03%args.nef3[1])*args.nbf3[1] + (i02%args.nef2[1])*args.nbf2[1] + (i01%args.nef1[1])*args.nbf1[1]);
device const T * f1 = (device const T *) (src1_1 + (i03%args.nef3[2])*args.nbf3[2] + (i02%args.nef2[2])*args.nbf2[2] + (i01%args.nef1[2])*args.nbf1[2]);
float sumf = 0.0f;
// parallel sum
for (int i00 = tpitg.x; i00 < args.ne00_t; i00 += ntg.x) {
sumf += dot(x[i00], x[i00]);
}
sumf = simd_sum(sumf);
threadgroup_barrier(mem_flags::mem_threadgroup);
if (tiisg == 0) {
shmem_f32[sgitg] = sumf;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
sumf = shmem_f32[tiisg];
sumf = simd_sum(sumf);
const float mean = sumf/args.ne00;
const float scale = 1.0f/sqrt(mean + args.eps);
device T * y = (device T *) (dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1);
for (int i00 = tpitg.x; i00 < args.ne00_t; i00 += ntg.x) {
if (F == 1) {
y[i00] = (x[i00]*scale);
}
if (F == 2) {
y[i00] = (x[i00]*scale)*f0[i00];
}
if (F == 3) {
y[i00] = (x[i00]*scale)*f0[i00] + f1[i00];
}
}
}
typedef decltype(kernel_rms_norm_fuse_impl<float4, 1>) kernel_rms_norm_fuse_t;
template [[host_name("kernel_rms_norm_f32")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float, 1>;
template [[host_name("kernel_rms_norm_mul_f32")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float, 2>;
template [[host_name("kernel_rms_norm_mul_add_f32")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float, 3>;
template [[host_name("kernel_rms_norm_f32_4")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float4, 1>;
template [[host_name("kernel_rms_norm_mul_f32_4")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float4, 2>;
template [[host_name("kernel_rms_norm_mul_add_f32_4")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float4, 3>;
template <typename T0, typename T>
kernel void kernel_l2_norm_impl(
constant ggml_metal_kargs_l2_norm & args,
device const char * src0,
device char * dst,
threadgroup float * shmem_f32 [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
const int i03 = tgpig.z;
const int i02 = tgpig.y;
const int i01 = tgpig.x;
if (sgitg == 0) {
shmem_f32[tiisg] = 0.0f;
}
device const T0 * x = (device const T0 *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01);
device T * y = (device T *) (dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1);
float sumf = 0.0f;
// parallel sum
for (int i00 = tpitg.x; i00 < args.ne00; i00 += ntg.x) {
sumf += dot(x[i00], x[i00]);
}
sumf = simd_sum(sumf);
threadgroup_barrier(mem_flags::mem_threadgroup);
if (tiisg == 0) {
shmem_f32[sgitg] = sumf;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
sumf = shmem_f32[tiisg];
sumf = simd_sum(sumf);
const float scale = 1.0f/max(sqrt(sumf), args.eps);
for (int i00 = tpitg.x; i00 < args.ne00; i00 += ntg.x) {
y[i00] = x[i00] * scale;
}
}
typedef decltype(kernel_l2_norm_impl<float, float>) kernel_l2_norm_t;
template [[host_name("kernel_l2_norm_f32_f32")]] kernel kernel_l2_norm_t kernel_l2_norm_impl<float, float>;
template [[host_name("kernel_l2_norm_f32_f32_4")]] kernel kernel_l2_norm_t kernel_l2_norm_impl<float4, float4>;
kernel void kernel_group_norm_f32(
constant ggml_metal_kargs_group_norm & args,
device const float * src0,
device float * dst,
threadgroup float * buf [[threadgroup(0)]],
uint tgpig[[threadgroup_position_in_grid]],
uint tpitg[[thread_position_in_threadgroup]],
uint sgitg[[simdgroup_index_in_threadgroup]],
uint tiisg[[thread_index_in_simdgroup]],
uint ntg[[threads_per_threadgroup]]) {
const int64_t ne = args.ne00*args.ne01*args.ne02;
const int64_t gs = args.ne00*args.ne01*((args.ne02 + args.ngrp - 1) / args.ngrp);
int start = tgpig * gs;
int end = start + gs;
start += tpitg;
if (end >= ne) {
end = ne;
}
float tmp = 0.0f; // partial sum for thread in warp
for (int j = start; j < end; j += ntg) {
tmp += src0[j];
}
threadgroup_barrier(mem_flags::mem_threadgroup);
tmp = simd_sum(tmp);
if (ntg > N_SIMDWIDTH) {
if (sgitg == 0) {
buf[tiisg] = 0.0f;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (tiisg == 0) {
buf[sgitg] = tmp;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
tmp = buf[tiisg];
tmp = simd_sum(tmp);
}
const float mean = tmp / gs;
tmp = 0.0f;
for (int j = start; j < end; j += ntg) {
float xi = src0[j] - mean;
dst[j] = xi;
tmp += xi * xi;
}
tmp = simd_sum(tmp);
if (ntg > N_SIMDWIDTH) {
if (sgitg == 0) {
buf[tiisg] = 0.0f;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (tiisg == 0) {
buf[sgitg] = tmp;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
tmp = buf[tiisg];
tmp = simd_sum(tmp);
}
const float variance = tmp / gs;
const float scale = 1.0f/sqrt(variance + args.eps);
for (int j = start; j < end; j += ntg) {
dst[j] *= scale;
}
}
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#include "common.h"
kernel void kernel_pool_2d_max_f32(
constant ggml_metal_kargs_pool_2d & args,
device const float * src0,
device float * dst,
uint gid[[thread_position_in_grid]]) {
if (gid >= args.np) {
return;
}
const int idx = gid;
const int I_HW = args.IH * args.IW;
const int O_HW = args.OH * args.OW;
const int nc = idx / O_HW;
const int cur_oh = idx % O_HW / args.OW;
const int cur_ow = idx % O_HW % args.OW;
device const float * i_ptr = src0 + nc * I_HW;
device float * o_ptr = dst + nc * O_HW;
const int start_h = cur_oh * args.s1 - args.p1;
const int bh = MAX(0, start_h);
const int eh = MIN(args.IH, start_h + args.k1);
const int start_w = cur_ow * args.s0 - args.p0;
const int bw = MAX(0, start_w);
const int ew = MIN(args.IW, start_w + args.k0);
float res = -INFINITY;
for (int i = bh; i < eh; i += 1) {
for (int j = bw; j < ew; j += 1) {
res = MAX(res, i_ptr[i * args.IW + j]);
}
}
o_ptr[cur_oh * args.OW + cur_ow] = res;
}
kernel void kernel_pool_2d_avg_f32(
constant ggml_metal_kargs_pool_2d & args,
device const float * src0,
device float * dst,
uint gid[[thread_position_in_grid]]) {
if (gid >= args.np) {
return;
}
const int idx = gid;
const int I_HW = args.IH * args.IW;
const int O_HW = args.OH * args.OW;
const int nc = idx / O_HW;
const int cur_oh = idx % O_HW / args.OW;
const int cur_ow = idx % O_HW % args.OW;
device const float * i_ptr = src0 + nc * I_HW;
device float * o_ptr = dst + nc * O_HW;
const int start_h = cur_oh * args.s1 - args.p1;
const int bh = MAX(0, start_h);
const int eh = MIN(args.IH, start_h + args.k1);
const int start_w = cur_ow * args.s0 - args.p0;
const int bw = MAX(0, start_w);
const int ew = MIN(args.IW, start_w + args.k0);
// const float scale = 1. / ((eh - bh) * (ew - bw));
const float scale = 1. / (args.k0 * args.k1);
float res = 0;
for (int i = bh; i < eh; i += 1) {
for (int j = bw; j < ew; j += 1) {
float cur = i_ptr[i * args.IW + j];
res += cur * scale;
}
}
o_ptr[cur_oh * args.OW + cur_ow] = res;
}
kernel void kernel_pool_1d_max_f32(
constant ggml_metal_kargs_pool_1d & args,
device const float * src,
device float * dst,
uint gid [[thread_position_in_grid]]
) {
if (gid >= args.np) {
return;
}
const int ow = (int)gid % args.OW;
const int row = (int)gid / args.OW;
const int base = ow * args.s0 - args.p0;
float acc = -INFINITY;
const int src_off = row * args.IW;
const int dst_off = row * args.OW;
for (int ki = 0; ki < args.k0; ++ki) {
int j = base + ki;
if (j < 0 || j >= args.IW){
continue;
}
float v = src[src_off + j];
acc = max(acc, v);
}
dst[dst_off + ow] = acc;
}
kernel void kernel_pool_1d_avg_f32(
constant ggml_metal_kargs_pool_1d & args,
device const float * src,
device float * dst,
uint gid [[thread_position_in_grid]]
) {
if (gid >= args.np) {
return;
}
const int ow = (int)gid % args.OW;
const int row = (int)gid / args.OW;
const int base = ow * args.s0 - args.p0;
float acc = 0.0f;
int cnt = 0;
const int src_off = row * args.IW;
const int dst_off = row * args.OW;
for (int ki = 0; ki < args.k0; ++ki) {
const int j = base + ki;
if (j < 0 || j >= args.IW) {
continue;
}
acc += src[src_off + j];
cnt += 1;
}
dst[dst_off + ow] = (cnt > 0) ? (acc / (float)cnt) : 0.0f;
}
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#pragma once
#include "common.h"
void quantize_q1_0(device const float * src, device block_q1_0 & dst) {
float sum_abs = 0.0f;
for (int j = 0; j < QK1_0; j++) {
sum_abs += fabs(src[j]);
}
dst.d = sum_abs / QK1_0;
for (int j = 0; j < QK1_0 / 8; j++) {
dst.qs[j] = 0;
}
for (int j = 0; j < QK1_0; j++) {
if (src[j] >= 0.0f) {
dst.qs[j / 8] |= (1 << (j % 8));
}
}
}
void quantize_q4_0(device const float * src, device block_q4_0 & dst) {
#pragma METAL fp math_mode(safe)
float amax = 0.0f; // absolute max
float max = 0.0f;
for (int j = 0; j < QK4_0; j++) {
const float v = src[j];
if (amax < fabs(v)) {
amax = fabs(v);
max = v;
}
}
const float d = max / -8;
const float id = d ? 1.0f/d : 0.0f;
dst.d = d;
for (int j = 0; j < QK4_0/2; ++j) {
const float x0 = src[0 + j]*id;
const float x1 = src[QK4_0/2 + j]*id;
const uint8_t xi0 = MIN(15, (int8_t)(x0 + 8.5f));
const uint8_t xi1 = MIN(15, (int8_t)(x1 + 8.5f));
dst.qs[j] = xi0;
dst.qs[j] |= xi1 << 4;
}
}
void quantize_q4_1(device const float * src, device block_q4_1 & dst) {
#pragma METAL fp math_mode(safe)
float min = FLT_MAX;
float max = -FLT_MAX;
for (int j = 0; j < QK4_1; j++) {
const float v = src[j];
if (min > v) min = v;
if (max < v) max = v;
}
const float d = (max - min) / ((1 << 4) - 1);
const float id = d ? 1.0f/d : 0.0f;
dst.d = d;
dst.m = min;
for (int j = 0; j < QK4_1/2; ++j) {
const float x0 = (src[0 + j] - min)*id;
const float x1 = (src[QK4_1/2 + j] - min)*id;
const uint8_t xi0 = MIN(15, (int8_t)(x0 + 0.5f));
const uint8_t xi1 = MIN(15, (int8_t)(x1 + 0.5f));
dst.qs[j] = xi0;
dst.qs[j] |= xi1 << 4;
}
}
void quantize_q5_0(device const float * src, device block_q5_0 & dst) {
#pragma METAL fp math_mode(safe)
float amax = 0.0f; // absolute max
float max = 0.0f;
for (int j = 0; j < QK5_0; j++) {
const float v = src[j];
if (amax < fabs(v)) {
amax = fabs(v);
max = v;
}
}
const float d = max / -16;
const float id = d ? 1.0f/d : 0.0f;
dst.d = d;
uint32_t qh = 0;
for (int j = 0; j < QK5_0/2; ++j) {
const float x0 = src[0 + j]*id;
const float x1 = src[QK5_0/2 + j]*id;
const uint8_t xi0 = MIN(31, (int8_t)(x0 + 16.5f));
const uint8_t xi1 = MIN(31, (int8_t)(x1 + 16.5f));
dst.qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4);
qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_0/2);
}
thread const uint8_t * qh8 = (thread const uint8_t *)&qh;
for (int j = 0; j < 4; ++j) {
dst.qh[j] = qh8[j];
}
}
void quantize_q5_1(device const float * src, device block_q5_1 & dst) {
#pragma METAL fp math_mode(safe)
float max = src[0];
float min = src[0];
for (int j = 1; j < QK5_1; j++) {
const float v = src[j];
min = v < min ? v : min;
max = v > max ? v : max;
}
const float d = (max - min) / 31;
const float id = d ? 1.0f/d : 0.0f;
dst.d = d;
dst.m = min;
uint32_t qh = 0;
for (int j = 0; j < QK5_1/2; ++j) {
const float x0 = (src[0 + j] - min)*id;
const float x1 = (src[QK5_1/2 + j] - min)*id;
const uint8_t xi0 = (uint8_t)(x0 + 0.5f);
const uint8_t xi1 = (uint8_t)(x1 + 0.5f);
dst.qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4);
qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_1/2);
}
thread const uint8_t * qh8 = (thread const uint8_t *)&qh;
for (int j = 0; j < 4; ++j) {
dst.qh[j] = qh8[j];
}
}
void quantize_q8_0(device const float * src, device block_q8_0 & dst) {
#pragma METAL fp math_mode(safe)
float amax = 0.0f; // absolute max
for (int j = 0; j < QK8_0; j++) {
const float v = src[j];
amax = MAX(amax, fabs(v));
}
const float d = amax / ((1 << 7) - 1);
const float id = d ? 1.0f/d : 0.0f;
dst.d = d;
for (int j = 0; j < QK8_0; ++j) {
const float x0 = src[j]*id;
dst.qs[j] = round(x0);
}
}
void quantize_iq4_nl(device const float * src, device block_iq4_nl & dst) {
#pragma METAL fp math_mode(safe)
float amax = 0.0f; // absolute max
float max = 0.0f;
for (int j = 0; j < QK4_NL; j++) {
const float v = src[j];
if (amax < fabs(v)) {
amax = fabs(v);
max = v;
}
}
const float d = max / kvalues_iq4nl_f[0];
const float id = d ? 1.0f/d : 0.0f;
float sumqx = 0, sumq2 = 0;
for (int j = 0; j < QK4_NL/2; ++j) {
const float x0 = src[0 + j]*id;
const float x1 = src[QK4_NL/2 + j]*id;
const uint8_t xi0 = best_index_int8(16, kvalues_iq4nl_f, x0);
const uint8_t xi1 = best_index_int8(16, kvalues_iq4nl_f, x1);
dst.qs[j] = xi0 | (xi1 << 4);
const float v0 = kvalues_iq4nl_f[xi0];
const float v1 = kvalues_iq4nl_f[xi1];
const float w0 = src[0 + j]*src[0 + j];
const float w1 = src[QK4_NL/2 + j]*src[QK4_NL/2 + j];
sumqx += w0*v0*src[j] + w1*v1*src[QK4_NL/2 + j];
sumq2 += w0*v0*v0 + w1*v1*v1;
}
dst.d = sumq2 > 0 ? sumqx/sumq2 : d;
}
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#include "common.h"
#include "dequantize.h"
#include "quantize.h"
template<typename T0, typename T1>
kernel void kernel_cpy_t_t(
constant ggml_metal_kargs_cpy & args,
device const char * src0,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
const int32_t i03 = tgpig[2];
const int32_t i02 = tgpig[1];
const int32_t i01 = ntg[1] == 1 ? tgpig[0]%args.ne01 : tgpig[0]*ntg[1] + tpitg.y;
const int32_t iw0 = ntg[1] == 1 ? tgpig[0]/args.ne01 : 0;
if (i01 >= args.ne01) {
return;
}
const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00;
const int32_t i3 = n/(args.ne2*args.ne1*args.ne0);
const int32_t i2 = (n - i3*args.ne2*args.ne1*args.ne0)/(args.ne1*args.ne0);
const int32_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0)/args.ne0;
const int32_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0);
device T1 * dst_data = (device T1 *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
for (int32_t i00 = iw0*ntg[0] + tpitg.x; i00 < args.ne00;) {
device const T0 * src = (device T0 *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + i00*args.nb00);
dst_data[i00] = (T1) src[0];
break;
}
}
typedef decltype(kernel_cpy_t_t<float, float>) kernel_cpy_t;
template [[host_name("kernel_cpy_f32_f32")]] kernel kernel_cpy_t kernel_cpy_t_t<float, float>;
template [[host_name("kernel_cpy_f32_f16")]] kernel kernel_cpy_t kernel_cpy_t_t<float, half>;
template [[host_name("kernel_cpy_f32_i32")]] kernel kernel_cpy_t kernel_cpy_t_t<float, int32_t>;
template [[host_name("kernel_cpy_i32_f32")]] kernel kernel_cpy_t kernel_cpy_t_t<int32_t, float>;
template [[host_name("kernel_cpy_i32_i32")]] kernel kernel_cpy_t kernel_cpy_t_t<int32_t, int32_t>;
#if defined(GGML_METAL_HAS_BF16)
template [[host_name("kernel_cpy_f32_bf16")]] kernel kernel_cpy_t kernel_cpy_t_t<float, bfloat>;
#endif
template [[host_name("kernel_cpy_f16_f32")]] kernel kernel_cpy_t kernel_cpy_t_t<half, float>;
template [[host_name("kernel_cpy_f16_f16")]] kernel kernel_cpy_t kernel_cpy_t_t<half, half>;
#if defined(GGML_METAL_HAS_BF16)
template [[host_name("kernel_cpy_bf16_f32")]] kernel kernel_cpy_t kernel_cpy_t_t<bfloat, float>;
template [[host_name("kernel_cpy_bf16_bf16")]] kernel kernel_cpy_t kernel_cpy_t_t<bfloat, bfloat>;
#endif
template<short QK,
typename block_q,
void (*quantize_func)(device const float *, device block_q &)>
kernel void kernel_cpy_f32_q(
constant ggml_metal_kargs_cpy & args,
device const char * src0,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
const int32_t i03 = tgpig[2];
const int32_t i02 = tgpig[1];
const int32_t i01 = ntg[1] == 1 ? tgpig[0]%args.ne01 : tgpig[0]*ntg[1] + tpitg.y;
const int32_t iw0 = ntg[1] == 1 ? tgpig[0]/args.ne01 : 0;
if (i01 >= args.ne01) {
return;
}
const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00;
const int32_t i3 = n / (args.ne2*args.ne1*args.ne0);
const int32_t i2 = (n - i3*args.ne2*args.ne1*args.ne0) / (args.ne1*args.ne0);
const int32_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0) / args.ne0;
const int32_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0)/QK;
device block_q * dst_data = (device block_q *)(dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
for (int32_t i00 = iw0*ntg[0] + tpitg.x; i00 < args.nk0;) {
device const float * src = (device const float *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + (i00*QK)*args.nb00);
quantize_func(src, dst_data[i00]);
break;
}
}
typedef decltype(kernel_cpy_f32_q<QK8_0, block_q8_0, quantize_q8_0>) cpy_f_q_t;
template [[host_name("kernel_cpy_f32_q8_0")]] kernel cpy_f_q_t kernel_cpy_f32_q<QK8_0, block_q8_0, quantize_q8_0>;
template [[host_name("kernel_cpy_f32_q1_0")]] kernel cpy_f_q_t kernel_cpy_f32_q<QK1_0, block_q1_0, quantize_q1_0>;
template [[host_name("kernel_cpy_f32_q4_0")]] kernel cpy_f_q_t kernel_cpy_f32_q<QK4_0, block_q4_0, quantize_q4_0>;
template [[host_name("kernel_cpy_f32_q4_1")]] kernel cpy_f_q_t kernel_cpy_f32_q<QK4_1, block_q4_1, quantize_q4_1>;
template [[host_name("kernel_cpy_f32_q5_0")]] kernel cpy_f_q_t kernel_cpy_f32_q<QK5_0, block_q5_0, quantize_q5_0>;
template [[host_name("kernel_cpy_f32_q5_1")]] kernel cpy_f_q_t kernel_cpy_f32_q<QK5_1, block_q5_1, quantize_q5_1>;
template [[host_name("kernel_cpy_f32_iq4_nl")]] kernel cpy_f_q_t kernel_cpy_f32_q<QK4_NL, block_iq4_nl, quantize_iq4_nl>;
template<typename T4x4, typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread T4x4 &)>
kernel void kernel_cpy_q_f32(
constant ggml_metal_kargs_cpy & args,
device const char * src0,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
const int32_t i03 = tgpig[2];
const int32_t i02 = tgpig[1];
const int32_t i01 = ntg[1] == 1 ? tgpig[0]%args.ne01 : tgpig[0]*ntg[1] + tpitg.y;
const int32_t iw0 = ntg[1] == 1 ? tgpig[0]/args.ne01 : 0;
if (i01 >= args.ne01) {
return;
}
const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00;
const int32_t i3 = n/(args.ne2*args.ne1*args.ne0);
const int32_t i2 = (n - i3*args.ne2*args.ne1*args.ne0)/(args.ne1*args.ne0);
const int32_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0)/args.ne0;
const int32_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0);
device const block_q * src_data = (device const block_q *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01);
device T4x4 * dst_data = (device T4x4 *)(dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
for (int32_t i00 = iw0*ntg[0] + tpitg.x; i00 < args.nk0;) {
T4x4 temp;
dequantize_func(src_data + i00/nl, i00%nl, temp);
dst_data[i00] = temp;
break;
}
}
typedef decltype(kernel_cpy_q_f32<float4x4, block_q4_0, 2, dequantize_q4_0>) cpy_q_f_t;
template [[host_name("kernel_cpy_q1_0_f32")]] kernel cpy_q_f_t kernel_cpy_q_f32<float4x4, block_q1_0, 8, dequantize_q1_0>;
template [[host_name("kernel_cpy_q4_0_f32")]] kernel cpy_q_f_t kernel_cpy_q_f32<float4x4, block_q4_0, 2, dequantize_q4_0>;
template [[host_name("kernel_cpy_q4_1_f32")]] kernel cpy_q_f_t kernel_cpy_q_f32<float4x4, block_q4_1, 2, dequantize_q4_1>;
template [[host_name("kernel_cpy_q5_0_f32")]] kernel cpy_q_f_t kernel_cpy_q_f32<float4x4, block_q5_0, 2, dequantize_q5_0>;
template [[host_name("kernel_cpy_q5_1_f32")]] kernel cpy_q_f_t kernel_cpy_q_f32<float4x4, block_q5_1, 2, dequantize_q5_1>;
template [[host_name("kernel_cpy_q8_0_f32")]] kernel cpy_q_f_t kernel_cpy_q_f32<float4x4, block_q8_0, 2, dequantize_q8_0>;
template [[host_name("kernel_cpy_q1_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q1_0, 8, dequantize_q1_0>;
template [[host_name("kernel_cpy_q4_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q4_0, 2, dequantize_q4_0>;
template [[host_name("kernel_cpy_q4_1_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q4_1, 2, dequantize_q4_1>;
template [[host_name("kernel_cpy_q5_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q5_0, 2, dequantize_q5_0>;
template [[host_name("kernel_cpy_q5_1_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q5_1, 2, dequantize_q5_1>;
template [[host_name("kernel_cpy_q8_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q8_0, 2, dequantize_q8_0>;
template<typename T>
kernel void kernel_concat(
constant ggml_metal_kargs_concat & args,
device const char * src0,
device const char * src1,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
const int i3 = tgpig.z;
const int i2 = tgpig.y;
const int i1 = ntg.y == 1 ? tgpig.x : tgpig.x*ntg.y + tpitg.y;
if (i1 >= args.ne1) {
return;
}
int o[4] = {0, 0, 0, 0};
o[args.dim] = args.dim == 0 ? args.ne00 : (args.dim == 1 ? args.ne01 : (args.dim == 2 ? args.ne02 : args.ne03));
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
device const T * x;
if (i0 < args.ne00 && i1 < args.ne01 && i2 < args.ne02 && i3 < args.ne03) {
x = (device const T *)(src0 + (i3 )*args.nb03 + (i2 )*args.nb02 + (i1 )*args.nb01 + (i0 )*args.nb00);
} else {
x = (device const T *)(src1 + (i3 - o[3])*args.nb13 + (i2 - o[2])*args.nb12 + (i1 - o[1])*args.nb11 + (i0 - o[0])*args.nb10);
}
device T * y = (device T *)(dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
*y = *x;
}
}
typedef decltype(kernel_concat<float>) kernel_concat_t;
template [[host_name("kernel_concat_f32")]] kernel kernel_concat_t kernel_concat<float>;
template [[host_name("kernel_concat_f16")]] kernel kernel_concat_t kernel_concat<half>;
#if defined(GGML_METAL_HAS_BF16)
template [[host_name("kernel_concat_bf16")]] kernel kernel_concat_t kernel_concat<bfloat>;
#endif
template [[host_name("kernel_concat_i8")]] kernel kernel_concat_t kernel_concat<char>;
template [[host_name("kernel_concat_i16")]] kernel kernel_concat_t kernel_concat<short>;
template [[host_name("kernel_concat_i32")]] kernel kernel_concat_t kernel_concat<int>;
template [[host_name("kernel_concat_i64")]] kernel kernel_concat_t kernel_concat<long>;
template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread float4x4 &)>
kernel void kernel_get_rows_q(
constant ggml_metal_kargs_get_rows & args,
device const void * src0,
device const void * src1,
device void * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
ushort tiitg[[thread_index_in_threadgroup]],
ushort3 ntg [[threads_per_threadgroup]]) {
const int32_t iw0 = tgpig.x/args.ne10;
const int32_t i10 = tgpig.x%args.ne10;
const int32_t i11 = tgpig.y;
const int32_t i12 = tgpig.z;
const int32_t r = ((const device int32_t *) ((const device char *) src1 + i12*args.nb12 + i11*args.nb11 + i10*args.nb10))[0];
const int32_t i02 = i11;
const int32_t i03 = i12;
auto psrc = (device const block_q *) ((const device char *) src0 + i03*args.nb03 + i02*args.nb02 + r*args.nb01);
auto pdst = (device float4x4 *) (( device char *) dst + i12*args.nb3 + i11*args.nb2 + i10*args.nb1);
for (int ind = iw0*ntg.x + tiitg; ind < args.ne00t;) {
float4x4 temp;
dequantize_func(psrc + ind/nl, ind%nl, temp);
pdst[ind] = temp;
break;
}
}
template<typename T0, typename T>
kernel void kernel_get_rows_f(
constant ggml_metal_kargs_get_rows & args,
device const void * src0,
device const void * src1,
device void * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
ushort tiitg[[thread_index_in_threadgroup]],
ushort3 ntg [[threads_per_threadgroup]]) {
const int32_t iw0 = tgpig.x/args.ne10;
const int32_t i10 = tgpig.x%args.ne10;
const int32_t i11 = tgpig.y;
const int32_t i12 = tgpig.z;
const int32_t r = ((const device int32_t *) ((const device char *) src1 + i12*args.nb12 + i11*args.nb11 + i10*args.nb10))[0];
const int32_t i02 = i11;
const int32_t i03 = i12;
auto psrc = (const device T0 *) ((const device char *) src0 + i03*args.nb03 + i02*args.nb02 + r*args.nb01);
auto pdst = ( device T *) (( device char *) dst + i12*args.nb3 + i11*args.nb2 + i10*args.nb1);
for (int ind = iw0*ntg.x + tiitg; ind < args.ne00t;) {
pdst[ind] = psrc[ind];
break;
}
}
typedef decltype(kernel_get_rows_f<float, float>) get_rows_f_t;
template [[host_name("kernel_get_rows_f32")]] kernel get_rows_f_t kernel_get_rows_f<float, float>;
template [[host_name("kernel_get_rows_f16")]] kernel get_rows_f_t kernel_get_rows_f<half, float>;
template [[host_name("kernel_get_rows_i32")]] kernel get_rows_f_t kernel_get_rows_f<int32_t, int32_t>;
#if defined(GGML_METAL_HAS_BF16)
template [[host_name("kernel_get_rows_bf16")]] kernel get_rows_f_t kernel_get_rows_f<bfloat, float>;
#endif
typedef decltype(kernel_get_rows_q<block_q4_0, 2, dequantize_q4_0>) get_rows_q_t;
template [[host_name("kernel_get_rows_q1_0")]] kernel get_rows_q_t kernel_get_rows_q<block_q1_0, 8, dequantize_q1_0>;
template [[host_name("kernel_get_rows_q4_0")]] kernel get_rows_q_t kernel_get_rows_q<block_q4_0, 2, dequantize_q4_0>;
template [[host_name("kernel_get_rows_q4_1")]] kernel get_rows_q_t kernel_get_rows_q<block_q4_1, 2, dequantize_q4_1>;
template [[host_name("kernel_get_rows_q5_0")]] kernel get_rows_q_t kernel_get_rows_q<block_q5_0, 2, dequantize_q5_0>;
template [[host_name("kernel_get_rows_q5_1")]] kernel get_rows_q_t kernel_get_rows_q<block_q5_1, 2, dequantize_q5_1>;
template [[host_name("kernel_get_rows_q8_0")]] kernel get_rows_q_t kernel_get_rows_q<block_q8_0, 2, dequantize_q8_0>;
template [[host_name("kernel_get_rows_mxfp4")]] kernel get_rows_q_t kernel_get_rows_q<block_mxfp4, 2, dequantize_mxfp4>;
template [[host_name("kernel_get_rows_q2_K")]] kernel get_rows_q_t kernel_get_rows_q<block_q2_K, QK_NL, dequantize_q2_K>;
template [[host_name("kernel_get_rows_q3_K")]] kernel get_rows_q_t kernel_get_rows_q<block_q3_K, QK_NL, dequantize_q3_K>;
template [[host_name("kernel_get_rows_q4_K")]] kernel get_rows_q_t kernel_get_rows_q<block_q4_K, QK_NL, dequantize_q4_K>;
template [[host_name("kernel_get_rows_q5_K")]] kernel get_rows_q_t kernel_get_rows_q<block_q5_K, QK_NL, dequantize_q5_K>;
template [[host_name("kernel_get_rows_q6_K")]] kernel get_rows_q_t kernel_get_rows_q<block_q6_K, QK_NL, dequantize_q6_K>;
template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_q_t kernel_get_rows_q<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
template [[host_name("kernel_get_rows_iq2_xs")]] kernel get_rows_q_t kernel_get_rows_q<block_iq2_xs, QK_NL, dequantize_iq2_xs>;
template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_q_t kernel_get_rows_q<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
template [[host_name("kernel_get_rows_iq3_s")]] kernel get_rows_q_t kernel_get_rows_q<block_iq3_s, QK_NL, dequantize_iq3_s>;
template [[host_name("kernel_get_rows_iq2_s")]] kernel get_rows_q_t kernel_get_rows_q<block_iq2_s, QK_NL, dequantize_iq2_s>;
template [[host_name("kernel_get_rows_iq1_s")]] kernel get_rows_q_t kernel_get_rows_q<block_iq1_s, QK_NL, dequantize_iq1_s>;
template [[host_name("kernel_get_rows_iq1_m")]] kernel get_rows_q_t kernel_get_rows_q<block_iq1_m, QK_NL, dequantize_iq1_m>;
template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_q_t kernel_get_rows_q<block_iq4_nl, 2, dequantize_iq4_nl>;
template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_q_t kernel_get_rows_q<block_iq4_xs, QK_NL, dequantize_iq4_xs>;
template<typename TS, typename TI, typename block_q, void (*quantize_func)(device const float *, device block_q &)>
kernel void kernel_set_rows_q32(
constant ggml_metal_kargs_set_rows & args,
device const void * src0,
device const void * src1,
device float * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiitg[[thread_index_in_threadgroup]],
uint3 tptg [[threads_per_threadgroup]]) {
const int32_t i03 = tgpig.z;
const int32_t i02 = tgpig.y;
const int32_t i12 = i03%args.ne12;
const int32_t i11 = i02%args.ne11;
const int32_t i01 = tgpig.x*tptg.y + tiitg/tptg.x;
if (i01 >= args.ne01) {
return;
}
const int32_t i10 = i01;
const TI i1 = ((const device TI *) ((const device char *) src1 + i10*args.nb10 + i11*args.nb11 + i12*args.nb12))[0];
device block_q * dst_row = ( device block_q *) (( device char *) dst + i1*args.nb1 + i02*args.nb2 + i03*args.nb3);
const device TS * src_row = (const device TS *) ((const device char *) src0 + i01*args.nb01 + i02*args.nb02 + i03*args.nb03);
for (int ind = tiitg%tptg.x; ind < args.nk0; ind += tptg.x) {
quantize_func(src_row + 32*ind, dst_row[ind]);
}
}
template<typename TS, typename TI, typename TD>
kernel void kernel_set_rows_f(
constant ggml_metal_kargs_set_rows & args,
device const void * src0,
device const void * src1,
device float * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiitg[[thread_index_in_threadgroup]],
uint3 tptg [[threads_per_threadgroup]]) {
const int32_t i03 = tgpig.z;
const int32_t i02 = tgpig.y;
const int32_t i12 = i03%args.ne12;
const int32_t i11 = i02%args.ne11;
const int32_t i01 = tgpig.x*tptg.y + tiitg/tptg.x;
if (i01 >= args.ne01) {
return;
}
const int32_t i10 = i01;
const TI i1 = ((const device TI *) ((const device char *) src1 + i10*args.nb10 + i11*args.nb11 + i12*args.nb12))[0];
device TD * dst_row = ( device TD *) (( device char *) dst + i1*args.nb1 + i02*args.nb2 + i03*args.nb3);
const device TS * src_row = (const device TS *) ((const device char *) src0 + i01*args.nb01 + i02*args.nb02 + i03*args.nb03);
for (int ind = tiitg%tptg.x; ind < args.nk0; ind += tptg.x) {
dst_row[ind] = (TD) src_row[ind];
}
}
typedef decltype(kernel_set_rows_f<float, int64_t, float>) set_rows_f_t;
template [[host_name("kernel_set_rows_f32_i64_f32")]] kernel set_rows_f_t kernel_set_rows_f<float, int64_t, float>;
template [[host_name("kernel_set_rows_f32_i32_f32")]] kernel set_rows_f_t kernel_set_rows_f<float, int32_t, float>;
template [[host_name("kernel_set_rows_f32_i64_f16")]] kernel set_rows_f_t kernel_set_rows_f<float, int64_t, half>;
template [[host_name("kernel_set_rows_f32_i32_f16")]] kernel set_rows_f_t kernel_set_rows_f<float, int32_t, half>;
#if defined(GGML_METAL_HAS_BF16)
template [[host_name("kernel_set_rows_f32_i64_bf16")]] kernel set_rows_f_t kernel_set_rows_f<float, int64_t, bfloat>;
template [[host_name("kernel_set_rows_f32_i32_bf16")]] kernel set_rows_f_t kernel_set_rows_f<float, int32_t, bfloat>;
#endif
template [[host_name("kernel_set_rows_f16_i64_f16")]] kernel set_rows_f_t kernel_set_rows_f<half, int64_t, half>;
template [[host_name("kernel_set_rows_f16_i32_f16")]] kernel set_rows_f_t kernel_set_rows_f<half, int32_t, half>;
#if defined(GGML_METAL_HAS_BF16)
template [[host_name("kernel_set_rows_bf16_i64_bf16")]] kernel set_rows_f_t kernel_set_rows_f<bfloat, int64_t, bfloat>;
template [[host_name("kernel_set_rows_bf16_i32_bf16")]] kernel set_rows_f_t kernel_set_rows_f<bfloat, int32_t, bfloat>;
#endif
typedef decltype(kernel_set_rows_q32<float, int64_t, block_q8_0, quantize_q8_0>) set_rows_q32_t;
template [[host_name("kernel_set_rows_f32_i64_q8_0")]] kernel set_rows_q32_t kernel_set_rows_q32<float, int64_t, block_q8_0, quantize_q8_0>;
template [[host_name("kernel_set_rows_f32_i32_q8_0")]] kernel set_rows_q32_t kernel_set_rows_q32<float, int32_t, block_q8_0, quantize_q8_0>;
template [[host_name("kernel_set_rows_f32_i64_q4_0")]] kernel set_rows_q32_t kernel_set_rows_q32<float, int64_t, block_q4_0, quantize_q4_0>;
template [[host_name("kernel_set_rows_f32_i32_q4_0")]] kernel set_rows_q32_t kernel_set_rows_q32<float, int32_t, block_q4_0, quantize_q4_0>;
template [[host_name("kernel_set_rows_f32_i64_q4_1")]] kernel set_rows_q32_t kernel_set_rows_q32<float, int64_t, block_q4_1, quantize_q4_1>;
template [[host_name("kernel_set_rows_f32_i32_q4_1")]] kernel set_rows_q32_t kernel_set_rows_q32<float, int32_t, block_q4_1, quantize_q4_1>;
template [[host_name("kernel_set_rows_f32_i64_q5_0")]] kernel set_rows_q32_t kernel_set_rows_q32<float, int64_t, block_q5_0, quantize_q5_0>;
template [[host_name("kernel_set_rows_f32_i32_q5_0")]] kernel set_rows_q32_t kernel_set_rows_q32<float, int32_t, block_q5_0, quantize_q5_0>;
template [[host_name("kernel_set_rows_f32_i64_q5_1")]] kernel set_rows_q32_t kernel_set_rows_q32<float, int64_t, block_q5_1, quantize_q5_1>;
template [[host_name("kernel_set_rows_f32_i32_q5_1")]] kernel set_rows_q32_t kernel_set_rows_q32<float, int32_t, block_q5_1, quantize_q5_1>;
template [[host_name("kernel_set_rows_f32_i64_iq4_nl")]] kernel set_rows_q32_t kernel_set_rows_q32<float, int64_t, block_iq4_nl, quantize_iq4_nl>;
template [[host_name("kernel_set_rows_f32_i32_iq4_nl")]] kernel set_rows_q32_t kernel_set_rows_q32<float, int32_t, block_iq4_nl, quantize_iq4_nl>;
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#include "common.h"
kernel void kernel_op_sum_f32(
constant ggml_metal_kargs_sum & args,
device const float * src0,
device float * dst,
threadgroup float * shmem_f32 [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
if (args.np == 0) {
return;
}
// TODO: become function constant
const uint nsg = (ntg.x + 31) / 32;
float sumf = 0;
for (uint64_t i0 = tpitg.x; i0 < args.np; i0 += ntg.x) {
sumf += src0[i0];
}
sumf = simd_sum(sumf);
if (tiisg == 0) {
shmem_f32[sgitg] = sumf;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
float total = 0;
if (sgitg == 0) {
float v = 0;
if (tpitg.x < nsg) {
v = shmem_f32[tpitg.x];
}
total = simd_sum(v);
if (tpitg.x == 0) {
dst[0] = total;
}
}
}
constant short FC_sum_rows_op [[function_constant(FC_SUM_ROWS + 0)]];
template <typename T0, typename T>
kernel void kernel_sum_rows_impl(
constant ggml_metal_kargs_sum_rows & args,
device const char * src0,
device char * dst,
threadgroup char * shmem [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
#define FC_OP FC_sum_rows_op
const int i3 = tgpig.z;
const int i2 = tgpig.y;
const int i1 = tgpig.x;
threadgroup T0 * shmem_t = (threadgroup T0 *) shmem;
if (sgitg == 0) {
shmem_t[tiisg] = 0.0f;
}
device const T0 * src_row = (device const T0 *) (src0 + i1*args.nb01 + i2*args.nb02 + i3*args.nb03);
device T * dst_row = (device T *) (dst + i1*args.nb1 + i2*args.nb2 + i3*args.nb3);
T0 sumf = T0(0.0f);
for (int64_t i0 = tpitg.x; i0 < args.ne00; i0 += ntg.x) {
sumf += src_row[i0];
}
sumf = simd_sum(sumf);
threadgroup_barrier(mem_flags::mem_threadgroup);
if (tiisg == 0) {
shmem_t[sgitg] = sumf;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
sumf = shmem_t[tiisg];
sumf = simd_sum(sumf);
if (tpitg.x == 0) {
if (FC_OP == OP_SUM_ROWS_NUM_MEAN) {
if (is_same<float4, T0>::value) {
dst_row[0] = sum(sumf) / (4*args.ne00);
} else {
dst_row[0] = sum(sumf) / args.ne00;
}
} else {
dst_row[0] = sum(sumf);
}
}
#undef FC_OP
}
typedef decltype(kernel_sum_rows_impl<float, float>) kernel_sum_rows_t;
template [[host_name("kernel_sum_rows_f32_f32")]] kernel kernel_sum_rows_t kernel_sum_rows_impl<float, float>;
template [[host_name("kernel_sum_rows_f32_f32_4")]] kernel kernel_sum_rows_t kernel_sum_rows_impl<float4, float>;
template<typename T>
kernel void kernel_cumsum_blk(
constant ggml_metal_kargs_cumsum_blk & args,
device const char * src0,
device char * tmp,
device char * dst,
threadgroup char * shmem [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
const int ib = tgpig[0]/args.ne01;
const int i00 = ib*ntg.x;
const int i01 = tgpig[0]%args.ne01;
const int i02 = tgpig[1];
const int i03 = tgpig[2];
device const float * src0_row = (device const float *) (src0 +
args.nb01*i01 +
args.nb02*i02 +
args.nb03*i03);
threadgroup float * shmem_f32 = (threadgroup float *) shmem;
float v = 0.0f;
if (i00 + tpitg.x < args.ne00) {
v = src0_row[i00 + tpitg.x];
}
float s = simd_prefix_inclusive_sum(v);
if (tiisg == N_SIMDWIDTH - 1) {
shmem_f32[sgitg] = s;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (sgitg == 0) {
shmem_f32[tiisg] = simd_prefix_exclusive_sum(shmem_f32[tiisg]);
}
threadgroup_barrier(mem_flags::mem_threadgroup);
s += shmem_f32[sgitg];
device float * dst_row = (device float *) dst +
args.ne00*i01 +
args.ne00*args.ne01*i02 +
args.ne00*args.ne01*args.ne02*i03;
if (i00 + tpitg.x < args.ne00) {
dst_row[i00 + tpitg.x] = s;
}
if (args.outb && tpitg.x == ntg.x - 1) {
device float * tmp_row = (device float *) tmp +
args.net0*i01 +
args.net0*args.net1*i02 +
args.net0*args.net1*args.net2*i03;
tmp_row[ib] = s;
}
}
typedef decltype(kernel_cumsum_blk<float>) kernel_cumsum_blk_t;
template [[host_name("kernel_cumsum_blk_f32")]] kernel kernel_cumsum_blk_t kernel_cumsum_blk<float>;
template<typename T>
kernel void kernel_cumsum_add(
constant ggml_metal_kargs_cumsum_add & args,
device const char * tmp,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
const int ib = tgpig[0]/args.ne01;
if (ib == 0) {
return;
}
const int i00 = ib*ntg.x;
const int i01 = tgpig[0]%args.ne01;
const int i02 = tgpig[1];
const int i03 = tgpig[2];
device const float * tmp_row = (device const float *) (tmp +
args.nbt1*i01 +
args.nbt2*i02 +
args.nbt3*i03);
device float * dst_row = (device float *) dst +
args.ne00*i01 +
args.ne00*args.ne01*i02 +
args.ne00*args.ne01*args.ne02*i03;
if (i00 + tpitg.x < args.ne00) {
dst_row[i00 + tpitg.x] += tmp_row[ib - 1];
}
}
typedef decltype(kernel_cumsum_add<float>) kernel_cumsum_add_t;
template [[host_name("kernel_cumsum_add_f32")]] kernel kernel_cumsum_add_t kernel_cumsum_add<float>;
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#include "common.h"
constant bool FC_rope_is_imrope [[function_constant(FC_ROPE + 0)]];
constant bool FC_rope_is_back [[function_constant(FC_ROPE + 1)]];
static float rope_yarn_ramp(const float low, const float high, const int i0) {
const float y = (i0 / 2 - low) / max(0.001f, high - low);
return 1.0f - min(1.0f, max(0.0f, y));
}
// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
static void rope_yarn(
float theta_extrap, float freq_scale, float corr_dims[2], int i0, float ext_factor, float mscale,
thread float * cos_theta, thread float * sin_theta) {
// Get n-d rotational scaling corrected for extrapolation
float theta_interp = freq_scale * theta_extrap;
float theta = theta_interp;
if (ext_factor != 0.0f) {
float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor;
theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
// Get n-d magnitude scaling corrected for interpolation
mscale *= 1.0f + 0.1f * log(1.0f / freq_scale);
}
*cos_theta = cos(theta) * mscale;
*sin_theta = sin(theta) * mscale;
if (FC_rope_is_back) {
*sin_theta *= -1.0f;
}
}
// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get
// `corr_fac(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))`
static float rope_yarn_corr_factor(int n_dims, int n_ctx_orig, float n_rot, float base) {
return n_dims * log(n_ctx_orig / (n_rot * 2 * M_PI_F)) / (2 * log(base));
}
static void rope_yarn_corr_dims(
int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2]
) {
// start and end correction dims
dims[0] = max(0.0f, floor(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_fast, freq_base)));
dims[1] = min(n_dims - 1.0f, ceil(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_slow, freq_base)));
}
template<typename T>
kernel void kernel_rope_norm(
constant ggml_metal_kargs_rope & args,
device const char * src0,
device const char * src1,
device const char * src2,
device char * dst,
ushort tiitg[[thread_index_in_threadgroup]],
ushort3 tptg [[threads_per_threadgroup]],
uint3 tgpig[[threadgroup_position_in_grid]]) {
const int i3 = tgpig[2];
const int i2 = tgpig[1];
const int i1 = tgpig[0];
float corr_dims[2];
rope_yarn_corr_dims(args.n_dims, args.n_ctx_orig, args.freq_base, args.beta_fast, args.beta_slow, corr_dims);
device const int32_t * pos = (device const int32_t *) src1;
const float theta_base = (float) pos[i2];
const float inv_ndims = -1.f/args.n_dims;
float cos_theta;
float sin_theta;
for (int i0 = 2*tiitg; i0 < args.ne0; i0 += 2*tptg.x) {
if (i0 < args.n_dims) {
const int ic = i0/2;
const float theta = theta_base * pow(args.freq_base, inv_ndims*i0);
const float freq_factor = args.src2 ? ((device const float *) src2)[ic] : 1.0f;
rope_yarn(theta/freq_factor, args.freq_scale, corr_dims, i0, args.ext_factor, args.attn_factor, &cos_theta, &sin_theta);
device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + i0*args.nb00);
device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
const float x0 = src[0];
const float x1 = src[1];
dst_data[0] = x0*cos_theta - x1*sin_theta;
dst_data[1] = x0*sin_theta + x1*cos_theta;
} else {
device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + i0*args.nb00);
device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
dst_data[0] = src[0];
dst_data[1] = src[1];
}
}
}
template<typename T>
kernel void kernel_rope_neox(
constant ggml_metal_kargs_rope & args,
device const char * src0,
device const char * src1,
device const char * src2,
device char * dst,
ushort tiitg[[thread_index_in_threadgroup]],
ushort3 tptg [[threads_per_threadgroup]],
uint3 tgpig[[threadgroup_position_in_grid]]) {
const int i3 = tgpig[2];
const int i2 = tgpig[1];
const int i1 = tgpig[0];
float corr_dims[2];
rope_yarn_corr_dims(args.n_dims, args.n_ctx_orig, args.freq_base, args.beta_fast, args.beta_slow, corr_dims);
device const int32_t * pos = (device const int32_t *) src1;
const float theta_base = (float) pos[i2];
const float inv_ndims = -1.f/args.n_dims;
float cos_theta;
float sin_theta;
for (int i0 = 2*tiitg; i0 < args.ne0; i0 += 2*tptg.x) {
if (i0 < args.n_dims) {
const int ic = i0/2;
const float theta = theta_base * pow(args.freq_base, inv_ndims*i0);
const float freq_factor = args.src2 ? ((device const float *) src2)[ic] : 1.0f;
rope_yarn(theta/freq_factor, args.freq_scale, corr_dims, i0, args.ext_factor, args.attn_factor, &cos_theta, &sin_theta);
device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + ic*args.nb00);
device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + ic*args.nb0);
const float x0 = src[0];
const float x1 = src[args.n_dims/2];
dst_data[0] = x0*cos_theta - x1*sin_theta;
dst_data[args.n_dims/2] = x0*sin_theta + x1*cos_theta;
} else {
device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + i0*args.nb00);
device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
dst_data[0] = src[0];
dst_data[1] = src[1];
}
}
}
template<typename T>
kernel void kernel_rope_multi(
constant ggml_metal_kargs_rope & args,
device const char * src0,
device const char * src1,
device const char * src2,
device char * dst,
ushort tiitg[[thread_index_in_threadgroup]],
ushort3 tptg [[threads_per_threadgroup]],
uint3 tgpig[[threadgroup_position_in_grid]]) {
const int i3 = tgpig[2];
const int i2 = tgpig[1];
const int i1 = tgpig[0];
float corr_dims[2];
rope_yarn_corr_dims(args.n_dims, args.n_ctx_orig, args.freq_base, args.beta_fast, args.beta_slow, corr_dims);
device const int32_t * pos = (device const int32_t *) src1;
const float inv_ndims = -1.f/args.n_dims;
float cos_theta;
float sin_theta;
for (int i0 = 2*tiitg; i0 < args.ne0; i0 += 2*tptg.x) {
if (i0 < args.n_dims) {
const int ic = i0/2;
// mrope theta calculations
// note: the rest is the same as kernel_rope_neox
const int sect_dims = args.sect_0 + args.sect_1 + args.sect_2 + args.sect_3;
const int sec_w01 = args.sect_0 + args.sect_1; // end of section 1
const int sec_w012 = args.sect_0 + args.sect_1 + args.sect_2; // end of section 2
const int sector = ic % sect_dims;
float theta_base;
if (FC_rope_is_imrope) {
if (sector % 3 == 1 && sector < 3 * args.sect_1) { // h
theta_base = (float) pos[i2 + args.ne02 * 1];
} else if (sector % 3 == 2 && sector < 3 * args.sect_2) { // w
theta_base = (float) pos[i2 + args.ne02 * 2];
} else if (sector % 3 == 0 && sector < 3 * args.sect_0) { // t
theta_base = (float) pos[i2 + args.ne02 * 0];
} else { // e
theta_base = (float) pos[i2 + args.ne02 * 3];
}
} else {
if (sector < args.sect_0) {
theta_base = (float) pos[i2];
} else if (sector < sec_w01) {
theta_base = (float) pos[i2 + args.ne02 * 1];
} else if (sector < sec_w012) {
theta_base = (float) pos[i2 + args.ne02 * 2];
} else {
theta_base = (float) pos[i2 + args.ne02 * 3];
}
}
// end of mrope
const float theta = theta_base * pow(args.freq_base, inv_ndims*i0);
const float freq_factor = args.src2 ? ((device const float *) src2)[ic] : 1.0f;
rope_yarn(theta/freq_factor, args.freq_scale, corr_dims, i0, args.ext_factor, args.attn_factor, &cos_theta, &sin_theta);
device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + ic*args.nb00);
device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + ic*args.nb0);
const float x0 = src[0];
const float x1 = src[args.n_dims/2];
dst_data[0] = x0*cos_theta - x1*sin_theta;
dst_data[args.n_dims/2] = x0*sin_theta + x1*cos_theta;
} else {
device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + i0*args.nb00);
device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
dst_data[0] = src[0];
dst_data[1] = src[1];
}
}
}
template<typename T>
kernel void kernel_rope_vision(
constant ggml_metal_kargs_rope & args,
device const char * src0,
device const char * src1,
device const char * src2,
device char * dst,
ushort tiitg[[thread_index_in_threadgroup]],
ushort3 tptg [[threads_per_threadgroup]],
uint3 tgpig[[threadgroup_position_in_grid]]) {
const int i3 = tgpig[2];
const int i2 = tgpig[1];
const int i1 = tgpig[0];
float corr_dims[2];
rope_yarn_corr_dims(args.n_dims, args.n_ctx_orig, args.freq_base, args.beta_fast, args.beta_slow, corr_dims);
device const int32_t * pos = (device const int32_t *) src1;
const float inv_ndims = -1.f/args.n_dims;
float cos_theta;
float sin_theta;
for (int i0 = 2*tiitg; i0 < args.ne0; i0 += 2*tptg.x) {
if (i0 < 2*args.n_dims) { // different from kernel_rope_multi
const int ic = i0/2;
// mrope theta calculations (only support 2 dimensions)
const int sect_dims = args.sect_0 + args.sect_1;
const int sector = ic % sect_dims;
float p;
float theta_base;
if (sector < args.sect_1) {
p = (float) sector;
theta_base = (float) pos[i2];
} else {
p = (float) sector - args.sect_0;
theta_base = (float) pos[i2 + args.ne02];
}
const float theta = theta_base * pow(args.freq_base, 2.0f * inv_ndims * p);
// end of mrope
const float freq_factor = args.src2 ? ((device const float *) src2)[ic] : 1.0f;
rope_yarn(theta/freq_factor, args.freq_scale, corr_dims, i0, args.ext_factor, args.attn_factor, &cos_theta, &sin_theta);
device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + ic*args.nb00);
device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + ic*args.nb0);
const float x0 = src[0];
const float x1 = src[args.n_dims]; // different from kernel_rope_multi
dst_data[0] = x0*cos_theta - x1*sin_theta;
dst_data[args.n_dims] = x0*sin_theta + x1*cos_theta; // different from kernel_rope_multi
} else {
device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + i0*args.nb00);
device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
dst_data[0] = src[0];
dst_data[1] = src[1];
}
}
}
typedef decltype(kernel_rope_norm<float>) kernel_rope_norm_t;
typedef decltype(kernel_rope_neox<float>) kernel_rope_neox_t;
typedef decltype(kernel_rope_multi<float>) kernel_rope_multi_t;
typedef decltype(kernel_rope_vision<float>) kernel_rope_vision_t;
template [[host_name("kernel_rope_norm_f32")]] kernel kernel_rope_norm_t kernel_rope_norm<float>;
template [[host_name("kernel_rope_norm_f16")]] kernel kernel_rope_norm_t kernel_rope_norm<half>;
template [[host_name("kernel_rope_neox_f32")]] kernel kernel_rope_neox_t kernel_rope_neox<float>;
template [[host_name("kernel_rope_neox_f16")]] kernel kernel_rope_neox_t kernel_rope_neox<half>;
template [[host_name("kernel_rope_multi_f32")]] kernel kernel_rope_multi_t kernel_rope_multi<float>;
template [[host_name("kernel_rope_multi_f16")]] kernel kernel_rope_multi_t kernel_rope_multi<half>;
template [[host_name("kernel_rope_vision_f32")]] kernel kernel_rope_vision_t kernel_rope_vision<float>;
template [[host_name("kernel_rope_vision_f16")]] kernel kernel_rope_vision_t kernel_rope_vision<half>;
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#include "common.h"
template<typename T>
kernel void kernel_soft_max(
constant ggml_metal_kargs_soft_max & args,
device const char * src0,
device const char * src1,
device const char * src2,
device char * dst,
threadgroup float * buf [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint sgitg[[simdgroup_index_in_threadgroup]],
uint tiisg[[thread_index_in_simdgroup]],
uint3 tptg[[threads_per_threadgroup]]) {
const int32_t i03 = tgpig.z;
const int32_t i02 = tgpig.y;
const int32_t i01 = tgpig.x;
const int32_t i13 = i03%args.ne13;
const int32_t i12 = i02%args.ne12;
const int32_t i11 = i01;
device const float * psrc0 = (device const float *) (src0 + i01*args.nb01 + i02*args.nb02 + i03*args.nb03);
device const T * pmask = src1 != src0 ? (device const T * ) (src1 + i11*args.nb11 + i12*args.nb12 + i13*args.nb13) : nullptr;
device const float * psrc2 = src2 != src0 ? (device const float *) (src2) : nullptr;
device float * pdst = (device float *) (dst + i01*args.nb1 + i02*args.nb2 + i03*args.nb3);
float slope = 1.0f;
// ALiBi
if (args.max_bias > 0.0f) {
const int32_t h = i02;
const float base = h < args.n_head_log2 ? args.m0 : args.m1;
const int exp = h < args.n_head_log2 ? h + 1 : 2*(h - args.n_head_log2) + 1;
slope = pow(base, exp);
}
// parallel max
float lmax = psrc2 ? psrc2[i02] : -INFINITY;
for (int i00 = tpitg.x; i00 < args.ne00; i00 += tptg.x) {
lmax = MAX(lmax, psrc0[i00]*args.scale + (pmask ? slope*pmask[i00] : 0.0f));
}
// find the max value in the block
float max_val = simd_max(lmax);
if (tptg.x > N_SIMDWIDTH) {
if (sgitg == 0) {
buf[tiisg] = -INFINITY;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (tiisg == 0) {
buf[sgitg] = max_val;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
max_val = buf[tiisg];
max_val = simd_max(max_val);
}
// parallel sum
float lsum = 0.0f;
for (int i00 = tpitg.x; i00 < args.ne00; i00 += tptg.x) {
const float exp_psrc0 = exp((psrc0[i00]*args.scale + (pmask ? slope*pmask[i00] : 0.0f)) - max_val);
lsum += exp_psrc0;
pdst[i00] = exp_psrc0;
}
// This barrier fixes a failing test
// ref: https://github.com/ggml-org/ggml/pull/621#discussion_r1425156335
threadgroup_barrier(mem_flags::mem_none);
float sum = simd_sum(lsum);
if (tptg.x > N_SIMDWIDTH) {
if (sgitg == 0) {
buf[tiisg] = 0.0f;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (tiisg == 0) {
buf[sgitg] = sum;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
sum = buf[tiisg];
sum = simd_sum(sum);
}
if (psrc2) {
sum += exp(psrc2[i02] - max_val);
}
const float inv_sum = 1.0f/sum;
for (int i00 = tpitg.x; i00 < args.ne00; i00 += tptg.x) {
pdst[i00] *= inv_sum;
}
}
template<typename T>
kernel void kernel_soft_max_4(
constant ggml_metal_kargs_soft_max & args,
device const char * src0,
device const char * src1,
device const char * src2,
device char * dst,
threadgroup float * buf [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint sgitg[[simdgroup_index_in_threadgroup]],
uint tiisg[[thread_index_in_simdgroup]],
uint3 tptg[[threads_per_threadgroup]]) {
const int32_t i03 = tgpig.z;
const int32_t i02 = tgpig.y;
const int32_t i01 = tgpig.x;
const int32_t i13 = i03%args.ne13;
const int32_t i12 = i02%args.ne12;
const int32_t i11 = i01;
device const float4 * psrc4 = (device const float4 *) (src0 + i01*args.nb01 + i02*args.nb02 + i03*args.nb03);
device const T * pmask = src1 != src0 ? (device const T * ) (src1 + i11*args.nb11 + i12*args.nb12 + i13*args.nb13) : nullptr;
device const float * psrc2 = src2 != src0 ? (device const float * ) (src2) : nullptr;
device float4 * pdst4 = (device float4 *) (dst + i01*args.nb1 + i02*args.nb2 + i03*args.nb3);
float slope = 1.0f;
if (args.max_bias > 0.0f) {
const int32_t h = i02;
const float base = h < args.n_head_log2 ? args.m0 : args.m1;
const int exp = h < args.n_head_log2 ? h + 1 : 2*(h - args.n_head_log2) + 1;
slope = pow(base, exp);
}
// parallel max
float4 lmax4 = psrc2 ? psrc2[i02] : -INFINITY;
for (int i00 = tpitg.x; i00 < args.ne00/4; i00 += tptg.x) {
lmax4 = fmax(lmax4, psrc4[i00]*args.scale + (float4)((pmask ? slope*pmask[i00] : 0.0f)));
}
const float lmax = MAX(MAX(lmax4[0], lmax4[1]), MAX(lmax4[2], lmax4[3]));
float max_val = simd_max(lmax);
if (tptg.x > N_SIMDWIDTH) {
if (sgitg == 0) {
buf[tiisg] = -INFINITY;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (tiisg == 0) {
buf[sgitg] = max_val;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
max_val = buf[tiisg];
max_val = simd_max(max_val);
}
// parallel sum
float4 lsum4 = 0.0f;
for (int i00 = tpitg.x; i00 < args.ne00/4; i00 += tptg.x) {
const float4 exp_psrc4 = exp((psrc4[i00]*args.scale + (float4)((pmask ? slope*pmask[i00] : 0.0f))) - max_val);
lsum4 += exp_psrc4;
pdst4[i00] = exp_psrc4;
}
const float lsum = lsum4[0] + lsum4[1] + lsum4[2] + lsum4[3];
// This barrier fixes a failing test
// ref: https://github.com/ggml-org/ggml/pull/621#discussion_r1425156335
threadgroup_barrier(mem_flags::mem_none);
float sum = simd_sum(lsum);
if (tptg.x > N_SIMDWIDTH) {
if (sgitg == 0) {
buf[tiisg] = 0.0f;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (tiisg == 0) {
buf[sgitg] = sum;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
sum = buf[tiisg];
sum = simd_sum(sum);
}
if (psrc2) {
sum += exp(psrc2[i02] - max_val);
}
const float inv_sum = 1.0f/sum;
for (int i00 = tpitg.x; i00 < args.ne00/4; i00 += tptg.x) {
pdst4[i00] *= inv_sum;
}
}
typedef decltype(kernel_soft_max<float>) kernel_soft_max_t;
typedef decltype(kernel_soft_max_4<float4>) kernel_soft_max_4_t;
template [[host_name("kernel_soft_max_f16")]] kernel kernel_soft_max_t kernel_soft_max<half>;
template [[host_name("kernel_soft_max_f32")]] kernel kernel_soft_max_t kernel_soft_max<float>;
template [[host_name("kernel_soft_max_f16_4")]] kernel kernel_soft_max_4_t kernel_soft_max_4<half4>;
template [[host_name("kernel_soft_max_f32_4")]] kernel kernel_soft_max_4_t kernel_soft_max_4<float4>;
@@ -0,0 +1,75 @@
#include "common.h"
constant short FC_solve_tri_nsg [[function_constant(FC_SOLVE_TRI + 0)]];
constant short FC_solve_tri_n [[function_constant(FC_SOLVE_TRI + 1)]];
constant short FC_solve_tri_k [[function_constant(FC_SOLVE_TRI + 2)]];
kernel void kernel_solve_tri_f32(
constant ggml_metal_kargs_solve_tri & args,
device const char * src0,
device const char * src1,
device char * dst,
threadgroup char * shmem [[threadgroup(0)]],
ushort3 tgpig[[threadgroup_position_in_grid]],
ushort sgitg[[simdgroup_index_in_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
constexpr short NW = N_SIMDWIDTH;
const short NSG = FC_solve_tri_nsg;
const short N = FC_solve_tri_n;
const short K = FC_solve_tri_k;
const short NP = PAD2(N, NW);
const int32_t i03 = tgpig.z;
const int32_t i02 = tgpig.y;
const int32_t i01 = tgpig.x*NSG + sgitg;
threadgroup float * sh0 = (threadgroup float *) shmem;
device const float * src0_ptr = (device const float *)(src0 + i02 * args.nb02 + i03 * args.nb03) + sgitg*N;
device const float * src1_ptr = (device const float *)(src1 + i02 * args.nb12 + i03 * args.nb13) + i01;
device float * dst_ptr = (device float *)(dst + i02 * args.nb2 + i03 * args.nb3) + i01;
for (short rr = 0; rr < N; rr += NSG) {
threadgroup_barrier(mem_flags::mem_threadgroup);
{
threadgroup float * sh0_cur = sh0 + sgitg*NP;
for (short t = 0; t*NW < N; ++t) {
const short idx = t*NW + tiisg;
sh0_cur[idx] = src0_ptr[idx];
}
src0_ptr += NSG*N;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (i01 >= args.ne10) {
continue;
}
for (short ir = 0; ir < NSG && rr + ir < N; ++ir) {
const short r = rr + ir;
threadgroup float * sh0_cur = sh0 + ir*NP;
float sum = 0.0f;
for (short t = 0; t*NW < r; ++t) {
const short idx = t*NW + tiisg;
sum += sh0_cur[idx] * dst_ptr[idx*K] * (idx < r);
}
sum = simd_sum(sum);
if (tiisg == 0) {
const float diag = sh0_cur[r];
dst_ptr[r*K] = (src1_ptr[r*K] - sum) / diag;
}
}
}
}
+279
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@@ -0,0 +1,279 @@
#include "common.h"
// ref: ggml.c:ggml_compute_forward_ssm_conv_f32
kernel void kernel_ssm_conv_f32_f32(
constant ggml_metal_kargs_ssm_conv & args,
device const void * src0,
device const void * src1,
device float * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t ir = tgpig.x;
const int64_t i2 = tgpig.y;
const int64_t i3 = tgpig.z;
const int64_t nc = args.ne10;
//const int64_t ncs = args.ne00;
//const int64_t nr = args.ne01;
//const int64_t n_t = args.ne1;
//const int64_t n_s = args.ne2;
device const float * s = (device const float *) ((device const char *) src0 + ir*args.nb01 + i2*args.nb00 + i3*args.nb02);
device const float * c = (device const float *) ((device const char *) src1 + ir*args.nb11);
device float * x = (device float *) ((device char *) dst + ir*args.nb0 + i2*args.nb1 + i3*args.nb2);
float sumf = 0.0f;
for (int64_t i0 = 0; i0 < nc; ++i0) {
sumf += s[i0] * c[i0];
}
x[0] = sumf;
}
kernel void kernel_ssm_conv_f32_f32_4(
constant ggml_metal_kargs_ssm_conv & args,
device const void * src0,
device const void * src1,
device float * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t ir = tgpig.x;
const int64_t i2 = tgpig.y;
const int64_t i3 = tgpig.z;
const int64_t nc = args.ne10;
//const int64_t ncs = args.ne00;
//const int64_t nr = args.ne01;
//const int64_t n_t = args.ne1;
//const int64_t n_s = args.ne2;
device const float4 * s = (device const float4 *) ((device const char *) src0 + ir*args.nb01 + i2*args.nb00 + i3*args.nb02);
device const float4 * c = (device const float4 *) ((device const char *) src1 + ir*args.nb11);
device float * x = (device float *) ((device char *) dst + ir*args.nb0 + i2*args.nb1 + i3*args.nb2);
float sumf = 0.0f;
for (int64_t i0 = 0; i0 < nc/4; ++i0) {
sumf += dot(s[i0], c[i0]);
}
x[0] = sumf;
}
constant short FC_ssm_conv_bs [[function_constant(FC_SSM_CONV + 0)]];
// Batched version: each threadgroup processes multiple tokens for better efficiency
// Thread layout: each thread handles one token, threadgroup covers BATCH_SIZE tokens
kernel void kernel_ssm_conv_f32_f32_batched(
constant ggml_metal_kargs_ssm_conv & args,
device const void * src0,
device const void * src1,
device float * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
// tgpig.x = row index (ir)
// tgpig.y = batch of tokens (i2_base / BATCH_SIZE)
// tgpig.z = sequence index (i3)
// tpitg.x = thread within batch (0..BATCH_SIZE-1)
const short BATCH_SIZE = FC_ssm_conv_bs;
const int64_t ir = tgpig.x;
const int64_t i2_base = tgpig.y * BATCH_SIZE;
const int64_t i3 = tgpig.z;
const int64_t i2_off = tpitg.x;
const int64_t i2 = i2_base + i2_off;
const int64_t nc = args.ne10; // conv kernel size (typically 4)
const int64_t n_t = args.ne1; // number of tokens
// Bounds check for partial batches at the end
if (i2 >= n_t) {
return;
}
// Load conv weights (shared across all tokens for this row)
device const float * c = (device const float *) ((device const char *) src1 + ir*args.nb11);
// Load source for this specific token
device const float * s = (device const float *) ((device const char *) src0 + ir*args.nb01 + i2*args.nb00 + i3*args.nb02);
// Output location for this token
device float * x = (device float *) ((device char *) dst + ir*args.nb0 + i2*args.nb1 + i3*args.nb2);
float sumf = 0.0f;
for (int64_t i0 = 0; i0 < nc; ++i0) {
sumf += s[i0] * c[i0];
}
x[0] = sumf;
}
kernel void kernel_ssm_conv_f32_f32_batched_4(
constant ggml_metal_kargs_ssm_conv & args,
device const void * src0,
device const void * src1,
device float * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
// tgpig.x = row index (ir)
// tgpig.y = batch of tokens (i2_base / BATCH_SIZE)
// tgpig.z = sequence index (i3)
// tpitg.x = thread within batch (0..BATCH_SIZE-1)
const short BATCH_SIZE = FC_ssm_conv_bs;
const int64_t ir = tgpig.x;
const int64_t i2_base = tgpig.y * BATCH_SIZE;
const int64_t i3 = tgpig.z;
const int64_t i2_off = tpitg.x;
const int64_t i2 = i2_base + i2_off;
const int64_t nc = args.ne10; // conv kernel size (typically 4)
const int64_t n_t = args.ne1; // number of tokens
// Bounds check for partial batches at the end
if (i2 >= n_t) {
return;
}
// Load conv weights (shared across all tokens for this row)
device const float4 * c = (device const float4 *) ((device const char *) src1 + ir*args.nb11);
// Load source for this specific token
device const float4 * s = (device const float4 *) ((device const char *) src0 + ir*args.nb01 + i2*args.nb00 + i3*args.nb02);
// Output location for this token
device float * x = (device float *) ((device char *) dst + ir*args.nb0 + i2*args.nb1 + i3*args.nb2);
float sumf = 0.0f;
for (int64_t i0 = 0; i0 < nc/4; ++i0) {
sumf += dot(s[i0], c[i0]);
}
x[0] = sumf;
}
// ref: ggml.c:ggml_compute_forward_ssm_scan_f32, Mamba-2 part
// Optimized version: reduces redundant memory loads by having one thread load shared values
kernel void kernel_ssm_scan_f32(
constant ggml_metal_kargs_ssm_scan & args,
device const void * src0,
device const void * src1,
device const void * src2,
device const void * src3,
device const void * src4,
device const void * src5,
device const void * src6,
device float * dst,
threadgroup float * shared [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort sgptg[[simdgroups_per_threadgroup]],
uint3 tgpg[[threadgroups_per_grid]]) {
constexpr short NW = N_SIMDWIDTH;
// Shared memory layout:
// [0..sgptg*NW-1]: partial sums for reduction (existing)
// [sgptg*NW..sgptg*NW+sgptg-1]: pre-computed x_dt values for each token in batch
// [sgptg*NW+sgptg..sgptg*NW+2*sgptg-1]: pre-computed dA values for each token in batch
threadgroup float * shared_sums = shared;
threadgroup float * shared_x_dt = shared + sgptg * NW;
threadgroup float * shared_dA = shared + sgptg * NW + sgptg;
shared_sums[tpitg.x] = 0.0f;
const int32_t i0 = tpitg.x;
const int32_t i1 = tgpig.x;
const int32_t ir = tgpig.y; // current head
const int32_t i3 = tgpig.z; // current seq
const int32_t nc = args.d_state;
const int32_t nr = args.d_inner;
const int32_t nh = args.n_head;
const int32_t ng = args.n_group;
const int32_t n_t = args.n_seq_tokens;
const int32_t s_off = args.s_off;
device const int32_t * ids = (device const int32_t *) src6;
device const float * s0_buff = (device const float *) ((device const char *) src0 + ir*args.nb02 + ids[i3]*args.nb03);
device float * s_buff = (device float *) ((device char *) dst + ir*args.nb02 + i3*args.nb03 + s_off);
const int32_t i = i0 + i1*nc;
const int32_t g = ir / (nh / ng); // repeat_interleave
float s0 = s0_buff[i];
float s = 0.0f;
device const float * A = (device const float *) ((device const char *) src3 + ir*args.nb31); // {ne30, nh}
const float A0 = A[i0%args.ne30];
device const float * x = (device const float *)((device const char *) src1 + i1*args.nb10 + ir*args.nb11 + i3*args.nb13); // {dim, nh, nt, ns}
device const float * dt = (device const float *)((device const char *) src2 + ir*args.nb20 + i3*args.nb22); // {nh, nt, ns}
device const float * B = (device const float *)((device const char *) src4 + g*args.nb41 + i3*args.nb43); // {d_state, ng, nt, ns}
device const float * C = (device const float *)((device const char *) src5 + g*args.nb51 + i3*args.nb53); // {d_state, ng, nt, ns}
device float * y = dst + (i1 + ir*(nr) + i3*(n_t*nh*nr)); // {dim, nh, nt, ns}
for (int i2 = 0; i2 < n_t; i2 += sgptg) {
threadgroup_barrier(mem_flags::mem_threadgroup);
// Pre-compute x_dt and dA for this batch of tokens
// Only first sgptg threads do the loads and expensive math
if (i0 < sgptg && i2 + i0 < n_t) {
// ns12 and ns21 are element strides (nb12/nb10, nb21/nb20)
device const float * x_t = x + i0 * args.ns12;
device const float * dt_t = dt + i0 * args.ns21;
const float dt0 = dt_t[0];
const float dtsp = dt0 <= 20.0f ? log(1.0f + exp(dt0)) : dt0;
shared_x_dt[i0] = x_t[0] * dtsp;
shared_dA[i0] = dtsp; // Store dtsp, compute exp(dtsp * A0) per-thread since A0 varies
}
threadgroup_barrier(mem_flags::mem_threadgroup);
for (int t = 0; t < sgptg && i2 + t < n_t; t++) {
const float x_dt = shared_x_dt[t];
const float dA = exp(shared_dA[t] * A0);
s = (s0 * dA) + (B[i0] * x_dt);
const float sumf = simd_sum(s * C[i0]);
if (tiisg == 0) {
shared_sums[t*NW + sgitg] = sumf;
}
// recurse
s0 = s;
B += args.ns42;
C += args.ns52;
}
// Advance pointers for next batch
x += sgptg * args.ns12;
dt += sgptg * args.ns21;
threadgroup_barrier(mem_flags::mem_threadgroup);
const float sumf = simd_sum(shared_sums[sgitg*NW + tiisg]);
if (tiisg == 0 && i2 + sgitg < n_t) {
y[sgitg*nh*nr] = sumf;
}
y += sgptg*nh*nr;
}
s_buff[i] = s;
}
+69
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@@ -0,0 +1,69 @@
#include "common.h"
template<uint32_t ttype>
bool _ggml_vec_tri_cmp(const int i, const int r);
template<>
bool _ggml_vec_tri_cmp</* GGML_TRI_TYPE_LOWER */ 3>(const int i, const int r) {
return i < r;
}
template<>
bool _ggml_vec_tri_cmp</* GGML_TRI_TYPE_LOWER_DIAG */ 2>(const int i, const int r) {
return i <= r;
}
template<>
bool _ggml_vec_tri_cmp</* GGML_TRI_TYPE_UPPER */ 1>(const int i, const int r) {
return i > r;
}
template<>
bool _ggml_vec_tri_cmp</* GGML_TRI_TYPE_UPPER_DIAG */ 0>(const int i, const int r) {
return i >= r;
}
template<typename T, int ttype>
kernel void kernel_tri(
constant ggml_metal_kargs_tri & args,
device const char * src0,
device const char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
const int i3 = tgpig.z;
const int i2 = tgpig.y;
const int i1 = tgpig.x;
if (i3 >= args.ne03 || i2 >= args.ne02 || i1 >= args.ne01) {
return;
}
device const T * src_row = (device const T *) ((device const char *) src0 + i1*args.nb01 + i2*args.nb02 + i3*args.nb03);
device T * dst_row = (device T *) ((device char *) dst + i1*args.nb1 + i2*args.nb2 + i3*args.nb3);
// Each thread is a single element of the row if ne00 < max threads per
// threadgroup, so this will loop once for each index that this thread is
// responsible for
for (int64_t i0 = tpitg.x; i0 < args.ne00; i0 += ntg.x) {
// Use the comparison as a mask for branchless
dst_row[i0] = static_cast<T>(_ggml_vec_tri_cmp<ttype>(i0, i1)) * src_row[i0];
}
}
typedef decltype(kernel_tri<float, 0>) kernel_tri_t;
template [[host_name("kernel_tri_f32_0")]] kernel kernel_tri_t kernel_tri<float, 0>;
template [[host_name("kernel_tri_f32_1")]] kernel kernel_tri_t kernel_tri<float, 1>;
template [[host_name("kernel_tri_f32_2")]] kernel kernel_tri_t kernel_tri<float, 2>;
template [[host_name("kernel_tri_f32_3")]] kernel kernel_tri_t kernel_tri<float, 3>;
template [[host_name("kernel_tri_f16_0")]] kernel kernel_tri_t kernel_tri<half, 0>;
template [[host_name("kernel_tri_f16_1")]] kernel kernel_tri_t kernel_tri<half, 1>;
template [[host_name("kernel_tri_f16_2")]] kernel kernel_tri_t kernel_tri<half, 2>;
template [[host_name("kernel_tri_f16_3")]] kernel kernel_tri_t kernel_tri<half, 3>;
#if defined(GGML_METAL_HAS_BF16)
template [[host_name("kernel_tri_bf16_0")]] kernel kernel_tri_t kernel_tri<bfloat, 0>;
template [[host_name("kernel_tri_bf16_1")]] kernel kernel_tri_t kernel_tri<bfloat, 1>;
template [[host_name("kernel_tri_bf16_2")]] kernel kernel_tri_t kernel_tri<bfloat, 2>;
template [[host_name("kernel_tri_bf16_3")]] kernel kernel_tri_t kernel_tri<bfloat, 3>;
#endif
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#include "common.h"
constant short FC_unary_op [[function_constant(FC_UNARY + 0)]];
constant bool FC_unary_cnt[[function_constant(FC_UNARY + 1)]];
template <typename T0, typename T, typename TC>
kernel void kernel_unary_impl(
constant ggml_metal_kargs_unary & args,
device const char * src0,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
#define FC_OP FC_unary_op
#define FC_CNT FC_unary_cnt
device const T0 * src0_ptr;
device T * dst_ptr;
int i0;
if (FC_CNT) {
i0 = tgpig.x;
src0_ptr = (device const T0 *) (src0);
dst_ptr = (device T *) (dst);
} else {
const int i03 = tgpig.z;
const int i02 = tgpig.y;
const int k0 = tgpig.x/args.ne01;
const int i01 = tgpig.x - k0*args.ne01;
i0 = k0*ntg.x + tpitg.x;
src0_ptr = (device const T0 *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01);
dst_ptr = (device T *) (dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1 );
}
{
//threadgroup_barrier(mem_flags::mem_none);
if (!FC_CNT) {
if (i0 >= args.ne0) {
return;
}
}
const TC x = (TC) src0_ptr[i0];
if (FC_OP == OP_UNARY_NUM_SCALE) {
dst_ptr[i0] = (T) (args.scale * x + args.bias);
}
if (FC_OP == OP_UNARY_NUM_FILL) {
dst_ptr[i0] = (T) args.val;
}
if (FC_OP == OP_UNARY_NUM_CLAMP) {
dst_ptr[i0] = (T) clamp(x, args.min, args.max);
}
if (FC_OP == OP_UNARY_NUM_SQR) {
dst_ptr[i0] = (T) (x * x);
}
if (FC_OP == OP_UNARY_NUM_SQRT) {
dst_ptr[i0] = (T) sqrt(x);
}
if (FC_OP == OP_UNARY_NUM_SIN) {
dst_ptr[i0] = (T) sin(x);
}
if (FC_OP == OP_UNARY_NUM_COS) {
dst_ptr[i0] = (T) cos(x);
}
if (FC_OP == OP_UNARY_NUM_LOG) {
dst_ptr[i0] = (T) log(x);
}
if (FC_OP == OP_UNARY_NUM_LEAKY_RELU) {
dst_ptr[i0] = (T) (TC(x > 0)*x + TC(x <= 0)*(x * args.slope));
}
if (FC_OP == OP_UNARY_NUM_TANH) {
dst_ptr[i0] = (T) precise::tanh(x);
}
if (FC_OP == OP_UNARY_NUM_RELU) {
dst_ptr[i0] = (T) fmax(0, x);
}
if (FC_OP == OP_UNARY_NUM_SIGMOID) {
dst_ptr[i0] = (T) (1 / (1 + exp(-x)));
}
if (FC_OP == OP_UNARY_NUM_GELU) {
dst_ptr[i0] = (T) (0.5*x*(1 + precise::tanh(SQRT_2_OVER_PI*x*(1 + GELU_COEF_A*x*x))));
}
if (FC_OP == OP_UNARY_NUM_GELU_ERF) {
dst_ptr[i0] = (T) (0.5*x*(1 + erf_approx(SQRT_2_INV*x)));
}
if (FC_OP == OP_UNARY_NUM_GELU_QUICK) {
dst_ptr[i0] = (T) (x * (1/(1 + exp(GELU_QUICK_COEF*x))));
}
if (FC_OP == OP_UNARY_NUM_SILU) {
dst_ptr[i0] = (T) (x / (1 + exp(-x)));
}
if (FC_OP == OP_UNARY_NUM_ELU) {
dst_ptr[i0] = (T) elu_approx(x);
}
if (FC_OP == OP_UNARY_NUM_NEG) {
dst_ptr[i0] = (T) -x;
}
if (FC_OP == OP_UNARY_NUM_ABS) {
dst_ptr[i0] = (T) fabs(x);
}
if (FC_OP == OP_UNARY_NUM_SGN) {
dst_ptr[i0] = T(x > 0) - T(x < 0);
}
if (FC_OP == OP_UNARY_NUM_STEP) {
dst_ptr[i0] = T(x > 0);
}
if (FC_OP == OP_UNARY_NUM_HARDSWISH) {
dst_ptr[i0] = (T) (x * fmax(0, fmin(1, x/6 + 0.5)));
}
if (FC_OP == OP_UNARY_NUM_HARDSIGMOID) {
dst_ptr[i0] = (T) fmax(0, fmin(1, x/6 + 0.5));
}
if (FC_OP == OP_UNARY_NUM_EXP) {
dst_ptr[i0] = (T) exp(x);
}
if (FC_OP == OP_UNARY_NUM_SOFTPLUS) {
dst_ptr[i0] = (T) select(log(1 + exp(x)), x, x > 20);
}
if (FC_OP == OP_UNARY_NUM_EXPM1) {
// TODO: precise implementation
dst_ptr[i0] = (T) (exp(x) - 1);
}
if (FC_OP == OP_UNARY_NUM_FLOOR) {
dst_ptr[i0] = (T) floor(x);
}
if (FC_OP == OP_UNARY_NUM_CEIL) {
dst_ptr[i0] = (T) ceil(x);
}
if (FC_OP == OP_UNARY_NUM_ROUND) {
dst_ptr[i0] = (T) round(x);
}
if (FC_OP == OP_UNARY_NUM_TRUNC) {
dst_ptr[i0] = (T) trunc(x);
}
if (FC_OP == OP_UNARY_NUM_XIELU) {
const TC xi = x;
const TC gate = TC(xi > TC(0.0f));
const TC clamped = fmin(xi, TC(args.val));
const TC y_pos = TC(args.scale) * xi * xi + TC(args.bias) * xi;
const TC y_neg = (exp(clamped) - TC(1.0f) - xi) * TC(args.slope) + TC(args.bias) * xi;
dst_ptr[i0] = (T) (gate * y_pos + (TC(1.0f) - gate) * y_neg);
}
}
#undef FC_OP
#undef FC_CNT
}
typedef decltype(kernel_unary_impl<float, float, float>) kernel_unary_t;
template [[host_name("kernel_unary_f32_f32")]] kernel kernel_unary_t kernel_unary_impl<float, float, float>;
template [[host_name("kernel_unary_f32_f32_4")]] kernel kernel_unary_t kernel_unary_impl<float4, float4, float4>;
template [[host_name("kernel_unary_f16_f16")]] kernel kernel_unary_t kernel_unary_impl<half, half, float>;
template [[host_name("kernel_unary_f16_f16_4")]] kernel kernel_unary_t kernel_unary_impl<half4, half4, float4>;
template<typename T>
kernel void kernel_reglu(
constant ggml_metal_kargs_glu & args,
device const char * src0,
device const char * src1,
device char * dst,
uint tgpig[[threadgroup_position_in_grid]],
uint tpitg[[thread_position_in_threadgroup]],
uint ntg[[threads_per_threadgroup]]) {
device const T * src0_row = (device const T *) ((device const char *) src0 + tgpig*args.nb01) + args.i00;
device const T * src1_row = (device const T *) ((device const char *) src1 + tgpig*args.nb11) + args.i10;
device T * dst_row = (device T *) ((device char *) dst + tgpig*args.nb1);
for (int i0 = tpitg; i0 < args.ne0; i0 += ntg) {
const float x0 = src0_row[i0];
const float x1 = src1_row[i0];
dst_row[i0] = (T)(x0*x1*(x0 > 0.0f));
}
}
typedef decltype(kernel_reglu<float>) kernel_reglu_t;
template [[host_name("kernel_reglu_f32")]] kernel kernel_reglu_t kernel_reglu<float>;
template [[host_name("kernel_reglu_f16")]] kernel kernel_reglu_t kernel_reglu<half>;
template<typename T>
kernel void kernel_geglu(
constant ggml_metal_kargs_glu & args,
device const char * src0,
device const char * src1,
device char * dst,
uint tgpig[[threadgroup_position_in_grid]],
uint tpitg[[thread_position_in_threadgroup]],
uint ntg[[threads_per_threadgroup]]) {
device const T * src0_row = (device const T *) ((device const char *) src0 + tgpig*args.nb01) + args.i00;
device const T * src1_row = (device const T *) ((device const char *) src1 + tgpig*args.nb11) + args.i10;
device T * dst_row = (device T *) ((device char *) dst + tgpig*args.nb1);
for (int i0 = tpitg; i0 < args.ne0; i0 += ntg) {
const float x0 = src0_row[i0];
const float x1 = src1_row[i0];
const float gelu = 0.5f*x0*(1.0f + precise::tanh(SQRT_2_OVER_PI*x0*(1.0f + GELU_COEF_A*x0*x0)));
dst_row[i0] = (T)(gelu*x1);
}
}
typedef decltype(kernel_geglu<float>) kernel_geglu_t;
template [[host_name("kernel_geglu_f32")]] kernel kernel_geglu_t kernel_geglu<float>;
template [[host_name("kernel_geglu_f16")]] kernel kernel_geglu_t kernel_geglu<half>;
template<typename T>
kernel void kernel_swiglu(
constant ggml_metal_kargs_glu & args,
device const char * src0,
device const char * src1,
device char * dst,
uint tgpig[[threadgroup_position_in_grid]],
uint tpitg[[thread_position_in_threadgroup]],
uint ntg[[threads_per_threadgroup]]) {
device const T * src0_row = (device const T *) ((device const char *) src0 + tgpig*args.nb01) + args.i00;
device const T * src1_row = (device const T *) ((device const char *) src1 + tgpig*args.nb11) + args.i10;
device T * dst_row = (device T *) ((device char *) dst + tgpig*args.nb1);
for (int i0 = tpitg; i0 < args.ne0; i0 += ntg) {
const float x0 = src0_row[i0];
const float x1 = src1_row[i0];
const float silu = x0 / (1.0f + exp(-x0));
dst_row[i0] = (T)(silu*x1);
}
}
typedef decltype(kernel_swiglu<float>) kernel_swiglu_t;
template [[host_name("kernel_swiglu_f32")]] kernel kernel_swiglu_t kernel_swiglu<float>;
template [[host_name("kernel_swiglu_f16")]] kernel kernel_swiglu_t kernel_swiglu<half>;
template<typename T>
kernel void kernel_swiglu_oai(
constant ggml_metal_kargs_glu & args,
device const char * src0,
device const char * src1,
device char * dst,
uint tgpig[[threadgroup_position_in_grid]],
uint tpitg[[thread_position_in_threadgroup]],
uint ntg[[threads_per_threadgroup]]) {
device const T * src0_row = (device const T *) ((device const char *) src0 + tgpig*args.nb01) + args.i00;
device const T * src1_row = (device const T *) ((device const char *) src1 + tgpig*args.nb11) + args.i10;
device T * dst_row = (device T *) ((device char *) dst + tgpig*args.nb1);
for (int i0 = tpitg; i0 < args.ne0; i0 += ntg) {
float x0 = src0_row[i0];
float x1 = src1_row[i0];
x0 = min(x0, args.limit);
x1 = max(min(x1, args.limit), -args.limit);
float out_glu = x0 / (1.0f + exp(-x0 * args.alpha));
out_glu = out_glu * (1.0f + x1);
dst_row[i0] = (T)out_glu;
}
}
typedef decltype(kernel_swiglu_oai<float>) kernel_swiglu_oai_t;
template [[host_name("kernel_swiglu_oai_f32")]] kernel kernel_swiglu_oai_t kernel_swiglu_oai<float>;
template [[host_name("kernel_swiglu_oai_f16")]] kernel kernel_swiglu_oai_t kernel_swiglu_oai<half>;
template<typename T>
kernel void kernel_geglu_erf(
constant ggml_metal_kargs_glu & args,
device const char * src0,
device const char * src1,
device char * dst,
uint tgpig[[threadgroup_position_in_grid]],
uint tpitg[[thread_position_in_threadgroup]],
uint ntg[[threads_per_threadgroup]]) {
device const T * src0_row = (device const T *) ((device const char *) src0 + tgpig*args.nb01) + args.i00;
device const T * src1_row = (device const T *) ((device const char *) src1 + tgpig*args.nb11) + args.i10;
device T * dst_row = (device T *) ((device char *) dst + tgpig*args.nb1);
for (int i0 = tpitg; i0 < args.ne0; i0 += ntg) {
const float x0 = src0_row[i0];
const float x1 = src1_row[i0];
const float gelu_erf = 0.5f*x0*(1.0f+erf_approx<float>(x0*SQRT_2_INV));
dst_row[i0] = (T)(gelu_erf*x1);
}
}
typedef decltype(kernel_geglu_erf<float>) kernel_geglu_erf_t;
template [[host_name("kernel_geglu_erf_f32")]] kernel kernel_geglu_erf_t kernel_geglu_erf<float>;
template [[host_name("kernel_geglu_erf_f16")]] kernel kernel_geglu_erf_t kernel_geglu_erf<half>;
template<typename T>
kernel void kernel_geglu_quick(
constant ggml_metal_kargs_glu & args,
device const char * src0,
device const char * src1,
device char * dst,
uint tgpig[[threadgroup_position_in_grid]],
uint tpitg[[thread_position_in_threadgroup]],
uint ntg[[threads_per_threadgroup]]) {
device const T * src0_row = (device const T *) ((device const char *) src0 + tgpig*args.nb01) + args.i00;
device const T * src1_row = (device const T *) ((device const char *) src1 + tgpig*args.nb11) + args.i10;
device T * dst_row = (device T *) ((device char *) dst + tgpig*args.nb1);
for (int i0 = tpitg; i0 < args.ne0; i0 += ntg) {
const float x0 = src0_row[i0];
const float x1 = src1_row[i0];
const float gelu_quick = x0*(1.0f/(1.0f+exp(GELU_QUICK_COEF*x0)));
dst_row[i0] = (T)(gelu_quick*x1);
}
}
typedef decltype(kernel_geglu_quick<float>) kernel_geglu_quick_t;
template [[host_name("kernel_geglu_quick_f32")]] kernel kernel_geglu_quick_t kernel_geglu_quick<float>;
template [[host_name("kernel_geglu_quick_f16")]] kernel kernel_geglu_quick_t kernel_geglu_quick<half>;
+179
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#include "common.h"
constant bool FC_upscale_aa [[function_constant(FC_UPSCALE + 0)]];
kernel void kernel_upscale_nearest_f32(
constant ggml_metal_kargs_upscale & args,
device const char * src0,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t i3 = tgpig.z;
const int64_t i2 = tgpig.y;
const int64_t i1 = tgpig.x;
const int64_t i03 = i3/args.sf3;
const int64_t i02 = i2/args.sf2;
const int64_t i01 = i1/args.sf1;
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
const int64_t i00 = i0/args.sf0;
device const float * src0_ptr = (device const float *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + i00*args.nb00);
device float * dst_ptr = (device float *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
dst_ptr[0] = src0_ptr[0];
}
}
static inline float bilinear_tri(float x) {
return MAX(0.0f, 1.0f - fabs(x));
}
kernel void kernel_upscale_bilinear_f32(
constant ggml_metal_kargs_upscale & args,
device const char * src0,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t i3 = tgpig.z;
const int64_t i2 = tgpig.y;
const int64_t i1 = tgpig.x;
const int64_t i03 = i3 / args.sf3;
const int64_t i02 = i2 / args.sf2;
const float f01 = ((float)i1 + args.poffs) / args.sf1 - args.poffs;
const int64_t i01 = MAX(0, MIN(args.ne01 - 1, (int64_t)floor(f01)));
const int64_t i01p = MAX(0, MIN(args.ne01 - 1, i01 + 1));
const float fd1 = MAX(0.0f, MIN(1.0f, f01 - (float)i01));
src0 += i03*args.nb03 + i02*args.nb02;
device float * dst_ptr = (device float *)(dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1);
if (FC_upscale_aa) {
const float support0 = MAX(1.0f, 1.0f / args.sf0);
const float invscale0 = 1.0f / support0;
const float support1 = MAX(1.0f, 1.0f / args.sf1);
const float invscale1 = 1.0f / support1;
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
const float f00 = ((float)i0 + args.poffs) / args.sf0 - args.poffs;
int64_t x_min = MAX((int64_t)0, (int64_t)floor(f00 - support0 + args.poffs));
int64_t x_max = MIN(args.ne00, (int64_t)ceil (f00 + support0 + args.poffs));
int64_t y_min = MAX((int64_t)0, (int64_t)floor(f01 - support1 + args.poffs));
int64_t y_max = MIN(args.ne01, (int64_t)ceil (f01 + support1 + args.poffs));
float sum = 0.0f;
float wsum = 0.0f;
for (int64_t sy = y_min; sy < y_max; ++sy) {
const float wy = MAX(0.0f, 1.0f - fabs((float)sy - f01) * invscale1);
for (int64_t sx = x_min; sx < x_max; ++sx) {
const float wx = MAX(0.0f, 1.0f - fabs((float)sx - f00) * invscale0);
const float w = wx * wy;
device const float * src_ptr = (device const float *)(src0 + sy*args.nb01 + sx*args.nb00);
sum += (*src_ptr) * w;
wsum += w;
}
}
const float v = (wsum > 0.0f) ? (sum / wsum) : 0.0f;
dst_ptr[i0] = v;
}
} else {
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
const float f00 = ((float)i0 + args.poffs) / args.sf0 - args.poffs;
const int64_t i00 = MAX(0, MIN(args.ne00 - 1, (int64_t)floor(f00)));
const int64_t i00p = MAX(0, MIN(args.ne00 - 1, i00 + 1));
const float fd0 = MAX(0.0f, MIN(1.0f, f00 - (float)i00));
device const float * src00 = (device const float *)(src0 + i01*args.nb01 + i00*args.nb00);
device const float * src10 = (device const float *)(src0 + i01*args.nb01 + i00p*args.nb00);
device const float * src01 = (device const float *)(src0 + i01p*args.nb01 + i00*args.nb00);
device const float * src11 = (device const float *)(src0 + i01p*args.nb01 + i00p*args.nb00);
const float v =
(*src00) * (1.0f - fd0) * (1.0f - fd1) +
(*src10) * fd0 * (1.0f - fd1) +
(*src01) * (1.0f - fd0) * fd1 +
(*src11) * fd0 * fd1;
dst_ptr[i0] = v;
}
}
}
static inline float bicubic_weight1(float x) {
const float a = -0.75f;
return ((a + 2) * x - (a + 3)) * x * x + 1;
}
static inline float bicubic_weight2(float x) {
const float a = -0.75f;
return ((a * x - 5 * a) * x + 8 * a) * x - 4 * a;
}
kernel void kernel_upscale_bicubic_f32(
constant ggml_metal_kargs_upscale & args,
device const char * src0,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t i3 = tgpig.z;
const int64_t i2 = tgpig.y;
const int64_t i1 = tgpig.x;
const int64_t i03 = i3 / args.sf3;
const int64_t i02 = i2 / args.sf2;
const float f01 = ((float)i1 + args.poffs) / args.sf1 - args.poffs;
const int64_t i01 = (int64_t)floor(f01);
const float fd1 = f01 - (float)i01;
const float w_y0 = bicubic_weight2(fd1 + 1.0f);
const float w_y1 = bicubic_weight1(fd1);
const float w_y2 = bicubic_weight1(1.0f - fd1);
const float w_y3 = bicubic_weight2(2.0f - fd1);
const device const char * src_slice = src0 + i03 * args.nb03 + i02 * args.nb02;
device float * dst_ptr = (device float *)(dst + i3 * args.nb3 + i2 * args.nb2 + i1 * args.nb1);
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
const float f00 = ((float)i0 + args.poffs) / args.sf0 - args.poffs;
const int64_t i00 = (int64_t)floor(f00);
const float fd0 = f00 - (float)i00;
const float w_x0 = bicubic_weight2(fd0 + 1.0f);
const float w_x1 = bicubic_weight1(fd0);
const float w_x2 = bicubic_weight1(1.0f - fd0);
const float w_x3 = bicubic_weight2(2.0f - fd0);
float sum = 0.0f;
for (int dy = -1; dy <= 2; ++dy) {
const int64_t iy = MAX(0, MIN(args.ne01 - 1, i01 + dy));
const float wy = (dy == -1) ? w_y0 : (dy == 0) ? w_y1 : (dy == 1) ? w_y2 : w_y3;
for (int dx = -1; dx <= 2; ++dx) {
const int64_t ix = MAX(0, MIN(args.ne00 - 1, i00 + dx));
const float wx = (dx == -1) ? w_x0 : (dx == 0) ? w_x1 : (dx == 1) ? w_x2 : w_x3;
device const float * src_ptr = (device const float *)(src_slice + iy * args.nb01 + ix * args.nb00);
sum += (*src_ptr) * wx * wy;
}
}
dst_ptr[i0] = sum;
}
}
+179
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#include "common.h"
kernel void kernel_rwkv_wkv6_f32(
device const float * k,
device const float * v,
device const float * r,
device const float * tf,
device const float * td,
device const float * state_in,
device float * dst,
constant uint & B,
constant uint & T,
constant uint & C,
constant uint & H,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const uint head_size = 64; // TODO: support head_size = 128
const uint batch_id = tgpig.x / H;
const uint head_id = tgpig.x % H;
const uint tid = tpitg.x;
if (batch_id >= B || head_id >= H) {
return;
}
const uint state_size = C * head_size;
const uint n_seq_tokens = T / B;
threadgroup float _k[head_size];
threadgroup float _r[head_size];
threadgroup float _tf[head_size];
threadgroup float _td[head_size];
float state[head_size];
for (uint i = 0; i < head_size; i++) {
state[i] = state_in[batch_id * state_size + head_id * head_size * head_size
+ i * head_size + tid];
}
threadgroup_barrier(mem_flags::mem_threadgroup);
_tf[tid] = tf[head_id * head_size + tid];
threadgroup_barrier(mem_flags::mem_threadgroup);
const uint start_t = batch_id * n_seq_tokens * C + head_id * head_size + tid;
const uint end_t = (batch_id + 1) * n_seq_tokens * C + head_id * head_size + tid;
for (uint t = start_t; t < end_t; t += C) {
threadgroup_barrier(mem_flags::mem_threadgroup);
_k[tid] = k[t];
_r[tid] = r[t];
_td[tid] = td[t];
threadgroup_barrier(mem_flags::mem_threadgroup);
const float v_val = v[t];
float y = 0.0;
for (uint j = 0; j < head_size; j += 4) {
float4 k_vec = float4(_k[j], _k[j+1], _k[j+2], _k[j+3]);
float4 r_vec = float4(_r[j], _r[j+1], _r[j+2], _r[j+3]);
float4 tf_vec = float4(_tf[j], _tf[j+1], _tf[j+2], _tf[j+3]);
float4 td_vec = float4(_td[j], _td[j+1], _td[j+2], _td[j+3]);
float4 s_vec = float4(state[j], state[j+1], state[j+2], state[j+3]);
float4 kv = k_vec * v_val;
float4 temp = tf_vec * kv + s_vec;
y += dot(r_vec, temp);
s_vec = s_vec * td_vec + kv;
state[j] = s_vec[0];
state[j+1] = s_vec[1];
state[j+2] = s_vec[2];
state[j+3] = s_vec[3];
}
dst[t] = y;
}
for (uint i = 0; i < head_size; i++) {
dst[T * C + batch_id * state_size + head_id * head_size * head_size
+ i * head_size + tid] = state[i];
}
}
kernel void kernel_rwkv_wkv7_f32(
device const float * r,
device const float * w,
device const float * k,
device const float * v,
device const float * a,
device const float * b,
device const float * state_in,
device float * dst,
constant uint & B,
constant uint & T,
constant uint & C,
constant uint & H,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const uint head_size = 64; // TODO: support head_size = 128
const uint batch_id = tgpig.x / H;
const uint head_id = tgpig.x % H;
const uint tid = tpitg.x;
if (batch_id >= B || head_id >= H) {
return;
}
const uint state_size = C * head_size;
const uint n_seq_tokens = T / B;
threadgroup float _r[head_size];
threadgroup float _w[head_size];
threadgroup float _k[head_size];
threadgroup float _a[head_size];
threadgroup float _b[head_size];
float state[head_size];
for (uint i = 0; i < head_size; i++) {
state[i] = state_in[batch_id * state_size + head_id * head_size * head_size
+ tid * head_size + i];
}
const uint start_t = batch_id * n_seq_tokens * C + head_id * head_size + tid;
const uint end_t = (batch_id + 1) * n_seq_tokens * C + head_id * head_size + tid;
for (uint t = start_t; t < end_t; t += C) {
threadgroup_barrier(mem_flags::mem_threadgroup);
_r[tid] = r[t];
_w[tid] = w[t];
_k[tid] = k[t];
_a[tid] = a[t];
_b[tid] = b[t];
threadgroup_barrier(mem_flags::mem_threadgroup);
const float v_val = v[t];
float y = 0.0, sa = 0.0;
float4 sa_vec(0.0);
for (uint j = 0; j < head_size; j += 4) {
float4 a_vec = float4(_a[j], _a[j+1], _a[j+2], _a[j+3]);
float4 s_vec = float4(state[j], state[j+1], state[j+2], state[j+3]);
sa_vec += a_vec * s_vec;
}
sa = sa_vec[0] + sa_vec[1] + sa_vec[2] + sa_vec[3];
for (uint j = 0; j < head_size; j += 4) {
float4 r_vec = float4(_r[j], _r[j+1], _r[j+2], _r[j+3]);
float4 w_vec = float4(_w[j], _w[j+1], _w[j+2], _w[j+3]);
float4 k_vec = float4(_k[j], _k[j+1], _k[j+2], _k[j+3]);
float4 b_vec = float4(_b[j], _b[j+1], _b[j+2], _b[j+3]);
float4 s_vec = float4(state[j], state[j+1], state[j+2], state[j+3]);
float4 kv = k_vec * v_val;
s_vec = s_vec * w_vec + kv + sa * b_vec;
y += dot(s_vec, r_vec);
state[j] = s_vec[0];
state[j+1] = s_vec[1];
state[j+2] = s_vec[2];
state[j+3] = s_vec[3];
}
dst[t] = y;
}
for (uint i = 0; i < head_size; i++) {
dst[T * C + batch_id * state_size + head_id * head_size * head_size
+ tid * head_size + i] = state[i];
}
}
+70 -4
View File
@@ -505,7 +505,7 @@ llama_ubatch llama_batch_allocr::split_simple(uint32_t n_ubatch) {
return ubatch_add(idxs, idxs.size(), false);
}
llama_ubatch llama_batch_allocr::split_equal(uint32_t n_ubatch, bool sequential) {
llama_ubatch llama_batch_allocr::split_equal(uint32_t n_ubatch, bool sequential, uint32_t n_keep_tail) {
if (sequential && has_cpl) {
LLAMA_LOG_ERROR("%s: sequential split is not supported when there are coupled sequences in the input batch (you may need to use the -kvu flag)\n", __func__);
@@ -548,7 +548,7 @@ llama_ubatch llama_batch_allocr::split_equal(uint32_t n_ubatch, bool sequential)
}
}
const uint32_t n_seqs = cur_seq_set.size();
uint32_t n_seqs = cur_seq_set.size();
// we are done
if (n_seqs == 0) {
@@ -569,7 +569,7 @@ llama_ubatch llama_batch_allocr::split_equal(uint32_t n_ubatch, bool sequential)
std::vector<idx_vec_t> idxs_per_seq(n_seqs);
while (true) {
// we can only add new n_seq_tokens tokens if all the sequence sets have at least one more unused token and
// we can only add new n_seq_tokens tokens if all the sequence sets have at least 1 more unused tokens and
// if we haven't reached n_ubatch
bool can_expand = true;
@@ -600,6 +600,72 @@ llama_ubatch llama_batch_allocr::split_equal(uint32_t n_ubatch, bool sequential)
}
}
// if n_keep_tail > 0, keep only the seqs that either finish in this ubatch or have at least
// n_keep_tail tokens remaining for a future ubatch, so that the trailing n_keep_tail tokens
// of each seq are never split across ubatches
if (n_keep_tail > 0) {
GGML_ASSERT(n_ubatch > n_keep_tail);
auto n_remaining = [&](uint32_t s) {
return (uint32_t) (seq_set_map[cur_seq_set[s]].size() - cur_idx[s]);
};
// keep the longest prefix of seqs that satisfy the constraint, to preserve sequential seq ids
uint32_t n_keep = 0;
while (n_keep < n_seqs) {
const uint32_t remaining = n_remaining(n_keep);
if (remaining != 0 && remaining < n_keep_tail) {
break;
}
n_keep++;
}
// all seqs violate the constraint - resolve the first one directly and emit it alone
if (n_keep == 0) {
auto & idxs = idxs_per_seq[0];
const auto & seq_idxs = seq_set_map[cur_seq_set[0]];
if (idxs.size() + n_remaining(0) <= n_ubatch) {
// extend the seq to completion
while (n_remaining(0) > 0) {
const int32_t idx = seq_idxs[cur_idx[0]];
idxs.push_back(idx);
used[idx] = true;
++n_used;
++cur_idx[0];
}
} else {
// truncate the seq so that at least n_keep_tail tokens remain
while (n_remaining(0) < n_keep_tail) {
used[idxs.back()] = false;
--n_used;
idxs.pop_back();
--cur_idx[0];
}
}
n_keep = 1;
}
// return the tokens of the deferred seqs back to the pool
for (uint32_t s = n_keep; s < n_seqs; ++s) {
for (const int32_t idx : idxs_per_seq[s]) {
used[idx] = false;
--n_used;
}
}
n_seqs = n_keep;
}
// concat the per-sequence-set lists
std::vector<int32_t> idxs;
@@ -814,7 +880,7 @@ void llama_batch_allocr::ubatch_print(const llama_ubatch & ubatch, int debug) {
LLAMA_LOG_DEBUG("%s: output = %p\n", __func__, (void *) ubatch.output);
LLAMA_LOG_DEBUG("%s: n_outputs = %d\n", __func__, n_outputs);
if (debug > 1) {
if (debug > 0) {
int seq_id_max = 0;
for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
for (int s = 0; s < ubatch.n_seq_id[i]; ++s) {
+2 -1
View File
@@ -104,7 +104,8 @@ public:
// make ubatches of equal-length sequences sets
// if sequential == true, the tokens in the ubatch will have increasing sequential sequence ids
llama_ubatch split_equal(uint32_t n_ubatch, bool sequential);
// n_keep_tail = minimum trailing tokens of a seq that must land in the same ubatch
llama_ubatch split_equal(uint32_t n_ubatch, bool sequential, uint32_t n_keep_tail);
// sequence-set-wise split - each ubatch contains a single sequence-set
llama_ubatch split_seq(uint32_t n_ubatch);
+89 -122
View File
@@ -17,6 +17,7 @@
#include <cstring>
#include <limits>
#include <stdexcept>
#include <string>
//
// llama_context
@@ -30,6 +31,30 @@ static llm_graph_type ctx_type_to_graph_type(llama_context_type ctx_type) {
throw std::runtime_error("Unsupported ctx type");
}
struct llm_fused_op_probe {
llm_fused_op op;
const char * name;
uint32_t n_tokens_per_seq;
};
static const llm_fused_op_probe llm_fused_op_flash_attn_probe = {
/*.op =*/ LLM_FUSED_OP_FLASH_ATTN,
/*.name =*/ "Flash Attention",
/*.n_tokens_per_seq =*/ 1,
};
static const llm_fused_op_probe llm_fused_op_gdn_ar_probe = {
/*.op =*/ LLM_FUSED_OP_GDN_AR,
/*.name =*/ "fused Gated Delta Net (autoregressive)",
/*.n_tokens_per_seq =*/ 1,
};
static const llm_fused_op_probe llm_fused_op_gdn_ch_probe = {
/*.op =*/ LLM_FUSED_OP_GDN_CH,
/*.name =*/ "fused Gated Delta Net (chunked)",
/*.n_tokens_per_seq =*/ 16,
};
llama_context::llama_context(
const llama_model & model,
llama_context_params params) :
@@ -436,6 +461,69 @@ llama_context::~llama_context() {
ggml_opt_free(opt_ctx);
}
void llama_context::resolve_fused_ops(const llama_memory_context_i * mctx, uint32_t n_seqs) {
const char * func = __func__;
auto resolve = [&](const llm_fused_op_probe & probe, bool & enabled) {
if (!enabled) {
return;
}
const uint32_t n_tokens_probe = probe.n_tokens_per_seq*n_seqs;
auto * gf = graph_reserve(n_tokens_probe, n_seqs, n_tokens_probe, mctx, true);
if (!gf) {
throw std::runtime_error(std::string("failed to reserve graph for ") + probe.name + " check");
}
bool device_mismatch = false;
for (const auto & node : get_gf_res_reserve()->get_fused_nodes()) {
if (node.op != probe.op) {
continue;
}
GGML_ASSERT(node.il >= 0);
ggml_backend_t backend_fused = ggml_backend_sched_get_tensor_backend(sched.get(), node.tensor);
ggml_backend_dev_t device_fused = backend_fused ? ggml_backend_get_device(backend_fused) : nullptr;
// TODO: make this descriptor-specific; model.dev_layer() preserves the current behavior,
// but is still wrong for cases like --no-kv-offload.
ggml_backend_dev_t device_layer = model.dev_layer(node.il);
if (device_fused != device_layer) {
LLAMA_LOG_WARN("%s: layer %d is assigned to device %s but %s "
"is assigned to device %s (usually due to missing support)\n",
func, node.il,
device_layer ? ggml_backend_dev_name(device_layer) : "none",
probe.name,
device_fused ? ggml_backend_dev_name(device_fused) : "none");
device_mismatch = true;
break;
}
}
if (device_mismatch) {
enabled = false;
LLAMA_LOG_WARN("%s: %s not supported, set to disabled\n", func, probe.name);
} else {
enabled = true;
LLAMA_LOG_INFO("%s: %s enabled\n", func, probe.name);
}
};
if (cparams.auto_fa) {
resolve(llm_fused_op_flash_attn_probe, cparams.flash_attn);
cparams.auto_fa = false;
}
if (cparams.auto_fgdn) {
LLAMA_LOG_INFO("%s: resolving fused Gated Delta Net support:\n", func);
resolve(llm_fused_op_gdn_ar_probe, cparams.fused_gdn_ar);
resolve(llm_fused_op_gdn_ch_probe, cparams.fused_gdn_ch);
cparams.auto_fgdn = false;
}
}
void llama_context::sched_reserve() {
if (!sched_need_reserve) {
return;
@@ -475,128 +563,7 @@ void llama_context::sched_reserve() {
LLAMA_LOG_DEBUG("%s: worst-case: n_tokens = %d, n_seqs = %d, n_outputs = %d\n", __func__, n_tokens, n_seqs, n_outputs);
// resolve automatic Flash Attention use
if (cparams.auto_fa) {
auto * gf = graph_reserve(1, n_seqs, n_outputs, mctx.get(), true);
if (!gf) {
throw std::runtime_error("failed to reserve graph for Flash Attention check");
}
const size_t prefix_len = strlen(LLAMA_TENSOR_NAME_FATTN) + 1;
bool fa_device_mismatch = false;
for (int i = 0; i < ggml_graph_n_nodes(gf); i++) {
ggml_tensor * n = ggml_graph_node(gf, i);
if (n->op != GGML_OP_FLASH_ATTN_EXT) {
continue;
}
ggml_backend_dev_t device_fa = ggml_backend_get_device(ggml_backend_sched_get_tensor_backend(sched.get(), n));
// TODO: instead of the tensor names, use a map to keep track of which (FA) tensors belong to which layer
GGML_ASSERT(strncmp(n->name, LLAMA_TENSOR_NAME_FATTN "-", prefix_len) == 0);
const int il = std::stoi(n->name + prefix_len);
ggml_backend_dev_t device_kv = model.dev_layer(il);
if (device_fa != device_kv) {
LLAMA_LOG_WARN("%s: layer %d is assigned to device %s but the Flash Attention tensor "
"is assigned to device %s (usually due to missing support)\n",
__func__, il, ggml_backend_dev_name(device_kv), ggml_backend_dev_name(device_fa));
// FIXME: fa_device_mismatch logic is wrong for --no-kv-offload, but this is broken anyways
fa_device_mismatch = true;
break;
}
}
if (fa_device_mismatch) {
cparams.flash_attn = false;
LLAMA_LOG_WARN("%s: Flash Attention was auto, set to disabled\n", __func__);
} else {
cparams.flash_attn = true;
LLAMA_LOG_INFO("%s: Flash Attention was auto, set to enabled\n", __func__);
}
cparams.auto_fa = false;
}
if (cparams.auto_fgdn) {
LLAMA_LOG_INFO("%s: resolving fused Gated Delta Net support:\n", __func__);
if (cparams.fused_gdn_ar) {
auto * gf = graph_reserve(1, n_seqs, n_outputs, mctx.get(), true);
if (!gf) {
throw std::runtime_error("failed to reserve graph for fused Gated Delta Net check (autoregressive)");
}
const size_t prefix_len = strlen(LLAMA_TENSOR_NAME_FGDN_AR) + 1;
bool gdn_device_mismatch = false;
for (int i = 0; i < ggml_graph_n_nodes(gf); i++) {
ggml_tensor * n = ggml_graph_node(gf, i);
if (n->op != GGML_OP_GATED_DELTA_NET) {
continue;
}
ggml_backend_dev_t device_gdn = ggml_backend_get_device(ggml_backend_sched_get_tensor_backend(sched.get(), n));
GGML_ASSERT(strncmp(n->name, LLAMA_TENSOR_NAME_FGDN_AR "-", prefix_len) == 0);
const int il = std::stoi(n->name + prefix_len);
ggml_backend_dev_t device_kv = model.dev_layer(il);
if (device_gdn != device_kv) {
LLAMA_LOG_WARN("%s: layer %d is assigned to device %s but the fused Gated Delta Net tensor "
"is assigned to device %s (usually due to missing support)\n",
__func__, il, ggml_backend_dev_name(device_kv), ggml_backend_dev_name(device_gdn));
gdn_device_mismatch = true;
break;
}
}
if (gdn_device_mismatch) {
cparams.fused_gdn_ar = false;
LLAMA_LOG_WARN("%s: fused Gated Delta Net (autoregressive) not supported, set to disabled\n", __func__);
} else {
LLAMA_LOG_INFO("%s: fused Gated Delta Net (autoregressive) enabled\n", __func__);
}
}
if (cparams.fused_gdn_ch) {
// more than one token in the batch per sequence in order to take the chunked path
// note: n_outputs must match n_tokens for embedding models with mean/rank pooling,
// because build_pooling creates inp_mean with shape [n_tokens, n_seqs] and multiplies
// it with t_embd which is reduced to [n_outputs, ...] via out_ids. if n_outputs != n_tokens,
// the ggml_mul_mat assertion fails.
const uint32_t n_tokens_ch = 16*n_seqs;
auto * gf = graph_reserve(n_tokens_ch, n_seqs, n_tokens_ch, mctx.get(), true);
if (!gf) {
throw std::runtime_error("failed to reserve graph for fused Gated Delta Net check (chunked)");
}
const size_t prefix_len = strlen(LLAMA_TENSOR_NAME_FGDN_CH) + 1;
bool gdn_device_mismatch = false;
for (int i = 0; i < ggml_graph_n_nodes(gf); i++) {
ggml_tensor * n = ggml_graph_node(gf, i);
if (n->op != GGML_OP_GATED_DELTA_NET) {
continue;
}
ggml_backend_dev_t device_gdn = ggml_backend_get_device(ggml_backend_sched_get_tensor_backend(sched.get(), n));
GGML_ASSERT(strncmp(n->name, LLAMA_TENSOR_NAME_FGDN_CH "-", prefix_len) == 0);
const int il = std::stoi(n->name + prefix_len);
ggml_backend_dev_t device_kv = model.dev_layer(il);
if (device_gdn != device_kv) {
LLAMA_LOG_WARN("%s: layer %d is assigned to device %s but the fused Gated Delta Net tensor "
"is assigned to device %s (usually due to missing support)\n",
__func__, il, ggml_backend_dev_name(device_kv), ggml_backend_dev_name(device_gdn));
gdn_device_mismatch = true;
break;
}
}
if (gdn_device_mismatch) {
cparams.fused_gdn_ch = false;
LLAMA_LOG_WARN("%s: fused Gated Delta Net (chunked) not supported, set to disabled\n", __func__);
} else {
LLAMA_LOG_INFO("%s: fused Gated Delta Net (chunked) enabled\n", __func__);
}
}
cparams.auto_fgdn = false;
}
resolve_fused_ops(mctx.get(), n_seqs);
// reserve worst-case graph
int n_splits_pp = -1;
+4
View File
@@ -262,6 +262,10 @@ private:
llm_graph_cb graph_get_cb() const;
// disable auto fused ops (Flash Attention, Gated Delta Net) whose op lands on a device
// that differs from the layer it belongs to (usually due to missing backend support)
void resolve_fused_ops(const llama_memory_context_i * mctx, uint32_t n_seqs);
// TODO: read/write lora adapters and cvec
size_t state_write_data(llama_io_write_i & io);
size_t state_read_data (llama_io_read_i & io);
+8 -1
View File
@@ -1192,6 +1192,7 @@ void llm_graph_result::reset() {
params = {};
inputs.clear();
fused_nodes.clear();
buf_compute_meta.resize(ggml_tensor_overhead()*max_nodes + ggml_graph_overhead_custom(max_nodes, false));
@@ -1293,6 +1294,10 @@ llm_graph_input_i * llm_graph_result::add_input(llm_graph_input_ptr input) {
return inputs.back().get();
}
void llm_graph_result::add_fused_node(llm_graph_fused_node result) {
fused_nodes.push_back(result);
}
void llm_graph_result::set_params(const llm_graph_params & params) {
this->params = params;
}
@@ -1352,6 +1357,8 @@ void llm_graph_context::cb(ggml_tensor * cur, const char * name, int il) const {
}
}
ggml_tensor * llm_graph_context::build_cvec(
ggml_tensor * cur,
int il) const {
@@ -2402,7 +2409,7 @@ ggml_tensor * llm_graph_context::build_attn_mha(
cur = ggml_flash_attn_ext(ctx0, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias,
hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f);
cb(cur, LLAMA_TENSOR_NAME_FATTN, il);
res->add_fused_node({LLM_FUSED_OP_FLASH_ATTN, cur, il});
ggml_flash_attn_ext_add_sinks(cur, sinks);
ggml_flash_attn_ext_set_prec (cur, GGML_PREC_F32);
+17
View File
@@ -38,6 +38,12 @@ enum llm_graph_type {
LLM_GRAPH_TYPE_DECODER_MTP,
};
enum llm_fused_op {
LLM_FUSED_OP_FLASH_ATTN,
LLM_FUSED_OP_GDN_AR,
LLM_FUSED_OP_GDN_CH,
};
enum llm_ffn_op_type : int {
LLM_FFN_NONE = 0, // sentinel: unset; archs must assign before use
LLM_FFN_SILU,
@@ -775,6 +781,12 @@ struct llm_graph_params {
}
};
struct llm_graph_fused_node {
llm_fused_op op;
ggml_tensor * tensor;
int il;
};
class llm_graph_result {
public:
llm_graph_result(int64_t max_nodes);
@@ -808,6 +820,10 @@ public:
llm_graph_input_i * add_input(llm_graph_input_ptr input);
void add_fused_node(llm_graph_fused_node result);
const std::vector<llm_graph_fused_node> & get_fused_nodes() const { return fused_nodes; }
void set_params(const llm_graph_params & params);
// important graph nodes
@@ -826,6 +842,7 @@ public:
std::map<llama_seq_id, ggml_tensor *> t_sampled_probs;
std::vector<llm_graph_input_ptr> inputs;
std::vector<llm_graph_fused_node> fused_nodes;
ggml_context_ptr ctx_compute;
-4
View File
@@ -103,7 +103,3 @@ std::string llama_format_tensor_shape(const std::vector<int64_t> & ne);
std::string llama_format_tensor_shape(const struct ggml_tensor * t);
std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i);
#define LLAMA_TENSOR_NAME_FATTN "__fattn__"
#define LLAMA_TENSOR_NAME_FGDN_AR "__fgdn_ar__"
#define LLAMA_TENSOR_NAME_FGDN_CH "__fgdn_ch__"
+1 -1
View File
@@ -113,7 +113,7 @@ llama_memory_context_ptr llama_kv_cache_dsa::init_batch(
std::vector<llama_ubatch> ubatches;
while (true) {
auto ubatch = n_stream == 1 ? balloc.split_simple(n_ubatch) : balloc.split_equal(n_ubatch, true);
auto ubatch = n_stream == 1 ? balloc.split_simple(n_ubatch) : balloc.split_equal(n_ubatch, true, 0);
if (ubatch.n_tokens == 0) {
break;
+1 -1
View File
@@ -1110,7 +1110,7 @@ llama_memory_context_ptr llama_kv_cache_dsv4::init_batch(
if (has_coupled) {
ubatch = balloc.split_seq(n_ubatch);
} else {
ubatch = balloc.split_equal(n_ubatch, raw_per_seq || comp_per_seq);
ubatch = balloc.split_equal(n_ubatch, raw_per_seq || comp_per_seq, 0);
}
if (ubatch.n_tokens == 0) {
+1 -1
View File
@@ -206,7 +206,7 @@ llama_memory_context_ptr llama_kv_cache_iswa::init_batch(llama_batch_allocr & ba
std::vector<llama_ubatch> ubatches;
while (true) {
auto ubatch = balloc.split_equal(n_ubatch, !unified);
auto ubatch = balloc.split_equal(n_ubatch, !unified, 0);
if (ubatch.n_tokens == 0) {
break;
+1 -1
View File
@@ -706,7 +706,7 @@ llama_memory_context_ptr llama_kv_cache::init_batch(
std::vector<llama_ubatch> ubatches;
while (true) {
auto ubatch = n_stream == 1 ? balloc.split_simple(n_ubatch) : balloc.split_equal(n_ubatch, true);
auto ubatch = n_stream == 1 ? balloc.split_simple(n_ubatch) : balloc.split_equal(n_ubatch, true, 0);
if (ubatch.n_tokens == 0) {
break;
+9 -9
View File
@@ -77,15 +77,15 @@ llama_memory_context_ptr llama_memory_hybrid_iswa::init_batch(llama_batch_allocr
// if all tokens are output, split by sequence
ubatch = balloc.split_seq(n_ubatch);
} else {
if (mem_recr->n_rs_seq > 0) {
// [TAG_RECURRENT_ROLLBACK_SPLITS]
// TODO: recurrent state rollback does not support equal splits
ubatch = balloc.split_seq(n_ubatch);
} else {
// Use non-sequential split when KV cache is unified (needed for hellaswag/winogrande/multiple-choice)
const bool unified = (mem_attn->get_base()->get_n_stream() == 1);
ubatch = balloc.split_equal(n_ubatch, !unified);
}
// Use non-sequential split when KV cache is unified (needed for hellaswag/winogrande/multiple-choice)
const bool unified = (mem_attn->get_base()->get_n_stream() == 1);
// [TAG_RECURRENT_ROLLBACK_SPLITS]
// the trailing (1 + n_rs_seq) tokens of each seq must stay in the same ubatch
// so that the rollback snapshots remain valid
const uint32_t n_rs_seq = mem_recr->n_rs_seq;
ubatch = balloc.split_equal(n_ubatch, !unified, n_rs_seq > 0 ? n_rs_seq + 1 : 0);
}
if (ubatch.n_tokens == 0) {
+9 -9
View File
@@ -78,15 +78,15 @@ llama_memory_context_ptr llama_memory_hybrid::init_batch(llama_batch_allocr & ba
// if all tokens are output, split by sequence
ubatch = balloc.split_seq(n_ubatch);
} else {
if (mem_recr->n_rs_seq > 0) {
// [TAG_RECURRENT_ROLLBACK_SPLITS]
// TODO: recurrent state rollback does not support equal splits
ubatch = balloc.split_seq(n_ubatch);
} else {
// Use non-sequential split when KV cache is unified (needed for hellaswag/winogrande/multiple-choice)
const bool unified = (mem_attn->get_n_stream() == 1);
ubatch = balloc.split_equal(n_ubatch, !unified);
}
// Use non-sequential split when KV cache is unified (needed for hellaswag/winogrande/multiple-choice)
const bool unified = (mem_attn->get_n_stream() == 1);
// [TAG_RECURRENT_ROLLBACK_SPLITS]
// the trailing (1 + n_rs_seq) tokens of each seq must stay in the same ubatch
// so that the rollback snapshots remain valid
const uint32_t n_rs_seq = mem_recr->n_rs_seq;
ubatch = balloc.split_equal(n_ubatch, !unified, n_rs_seq > 0 ? n_rs_seq + 1 : 0);
}
if (ubatch.n_tokens == 0) {
+6 -9
View File
@@ -416,15 +416,12 @@ llama_memory_context_ptr llama_memory_recurrent::init_batch(llama_batch_allocr &
// if all tokens are output, split by sequence
ubatch = balloc.split_seq(n_ubatch);
} else {
if (n_rs_seq > 0) {
// [TAG_RECURRENT_ROLLBACK_SPLITS]
// TODO: recurrent state rollback does not support equal splits
ubatch = balloc.split_seq(n_ubatch);
} else {
// TODO: non-sequential equal split can be done if using unified KV cache
// for simplicity, we always use sequential equal split for now
ubatch = balloc.split_equal(n_ubatch, true);
}
// TODO: non-sequential equal split can be done if using unified KV cache
// for simplicity, we always use sequential equal split for now
// [TAG_RECURRENT_ROLLBACK_SPLITS]
// the trailing (1 + n_rs_seq) tokens of each seq must stay in the same ubatch
// so that the rollback snapshots remain valid
ubatch = balloc.split_equal(n_ubatch, true, n_rs_seq > 0 ? n_rs_seq + 1 : 0);
}
if (ubatch.n_tokens == 0) {
+19 -7
View File
@@ -887,9 +887,6 @@ struct llm_tokenizer_ugm : llm_tokenizer {
// blob containing XOR-compressed compact double array (XCDA) entries
uint32_t xcda_blob_size = *(const uint32_t *) &precompiled_charsmap[0];
charsmap_offset += sizeof(xcda_blob_size);
if (xcda_blob_size + charsmap_offset >= precompiled_charsmap.size()) {
throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
}
// Next xcda_blob_size bytes contain entries of XOR-compressed compact
// double array (XCDA). Each entry is bit-packed into a 32-bit integer.
@@ -1205,7 +1202,15 @@ private:
throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
}
const char * prefix_replacement = &(tokenizer.prefix_replacements)[longest_prefix_offset];
return { prefix_replacement, strlen(prefix_replacement), longest_prefix_length };
size_t max_len = tokenizer.prefix_replacements_size - longest_prefix_offset;
size_t repl_len = 0;
while (repl_len < max_len && prefix_replacement[repl_len] != '\0') {
repl_len++;
}
if (repl_len == max_len) {
throw std::runtime_error("Unterminated string in precompiled charsmap!");
}
return { prefix_replacement, repl_len, longest_prefix_length };
}
// check if the input prefix contains a valid sequence of UTF-8 code units
@@ -2018,11 +2023,18 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
const size_t n_precompiled_charsmap = gguf_get_arr_n(ctx, precompiled_charsmap_keyidx);
const char * pc = (const char *) gguf_get_arr_data(ctx, precompiled_charsmap_keyidx);
precompiled_charsmap.assign(pc, pc + n_precompiled_charsmap);
if (precompiled_charsmap.size() < sizeof(uint32_t)) {
throw std::runtime_error("precompiled_charsmap too small for xcda_blob_size header!");
}
uint32_t * xcda_blob_size = (uint32_t *) &precompiled_charsmap[0];
#if defined(__BYTE_ORDER__) && defined(__ORDER_BIG_ENDIAN__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
*xcda_blob_size = __builtin_bswap32(*xcda_blob_size);
#endif
if (*xcda_blob_size + sizeof(uint32_t) >= precompiled_charsmap.size()) {
throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
}
#if defined(__BYTE_ORDER__) && defined(__ORDER_BIG_ENDIAN__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
// correct endianness of data in precompiled_charsmap binary blob
uint32_t * xcda_blob_size = (uint32_t *) &precompiled_charsmap[0];
*xcda_blob_size = __builtin_bswap32(*xcda_blob_size);
assert(*xcda_blob_size + sizeof(uint32_t) < n_precompiled_charsmap);
size_t xcda_array_size = *xcda_blob_size / sizeof(uint32_t);
uint32_t * xcda_array = (uint32_t *) &precompiled_charsmap[sizeof(uint32_t)];
for (size_t i = 0; i < xcda_array_size; ++i) {
+6 -6
View File
@@ -401,9 +401,9 @@ std::pair<ggml_tensor *, ggml_tensor *> llm_build_delta_net_base::build_delta_ne
// K=1: output carries the final state only. state s is 4D [S_v, S_v, H_v, n_seqs].
ggml_tensor * result = ggml_gated_delta_net(ctx0, q, k, v, g, b, s, /*K=*/1);
if (n_tokens == 1) {
cb(result, LLAMA_TENSOR_NAME_FGDN_AR, il);
res->add_fused_node({LLM_FUSED_OP_GDN_AR, result, il});
} else {
cb(result, LLAMA_TENSOR_NAME_FGDN_CH, il);
res->add_fused_node({LLM_FUSED_OP_GDN_CH, result, il});
}
ggml_tensor * output = ggml_view_4d(ctx0, result,
@@ -496,8 +496,8 @@ ggml_tensor * llm_build_delta_net_base::build_conv_state(
ggml_build_forward_expand(gf, ggml_cpy(ctx0, conv_state_last, conv_state_update));
} else {
// [TAG_RECURRENT_ROLLBACK_SPLITS]
// TODO: this logic incorrectly assumes that the last (n_rs_seq + 1) tokens of a sequence in a batch are
// inside the same ubatch. currently with `split_equal()` this is not correct
// this logic assumes that the last (n_rs_seq + 1) tokens of a sequence in a batch are inside
// the same ubatch, which `split_equal()` guarantees via its n_keep_tail argument
const int64_t K = (int64_t) cparams.n_rs_seq + 1;
@@ -566,9 +566,9 @@ ggml_tensor * llm_build_delta_net_base::build_recurrent_attn(
// state s is 4D [S_v, S_v, H_v, n_seqs]; K snapshot slots are written into the output.
ggml_tensor * gdn_out = ggml_gated_delta_net(ctx0, q, k, v, g, b, s, K);
if (n_seq_tokens > 1) {
cb(gdn_out, LLAMA_TENSOR_NAME_FGDN_CH, il);
res->add_fused_node({LLM_FUSED_OP_GDN_CH, gdn_out, il});
} else {
cb(gdn_out, LLAMA_TENSOR_NAME_FGDN_AR, il);
res->add_fused_node({LLM_FUSED_OP_GDN_AR, gdn_out, il});
}
const int64_t attn_score_elems = S_v * H_v * n_seq_tokens * n_seqs;
+4 -2
View File
@@ -2,11 +2,13 @@
set(TARGET llama-cli-impl)
add_library(${TARGET} cli.cpp)
add_library(${TARGET} cli.cpp
cli-client.cpp
cli-context.cpp)
set_target_properties(${TARGET} PROPERTIES WINDOWS_EXPORT_ALL_SYMBOLS ON)
target_include_directories(${TARGET} PUBLIC ${CMAKE_CURRENT_SOURCE_DIR} ../server)
target_link_libraries(${TARGET} PUBLIC server-context llama-common ${CMAKE_THREAD_LIBS_INIT})
target_link_libraries(${TARGET} PUBLIC llama-server-impl llama-common ${CMAKE_THREAD_LIBS_INIT})
if(LLAMA_TOOLS_INSTALL)
install(TARGETS ${TARGET} LIBRARY)
+130
View File
@@ -0,0 +1,130 @@
#include "cli-client.h"
#include "http.h"
#include <algorithm>
#include <chrono>
#include <thread>
// generation can stall for a long time during prompt processing, so the
// read timeout must be generous
static constexpr time_t CLI_HTTP_READ_TIMEOUT_SEC = 3600;
// upper bound for the accumulated response body kept for error reporting
static constexpr size_t CLI_HTTP_MAX_ERROR_BODY = 1024 * 1024;
// returns the path with the base url's path prefix prepended (if any)
static std::string join_path(const common_http_url & parts, const std::string & path) {
if (parts.path.empty() || parts.path == "/") {
return path;
}
std::string prefix = parts.path;
if (prefix.back() == '/') {
prefix.pop_back();
}
return prefix + path;
}
std::string cli_client::get(const std::string & path) {
auto [cli, parts] = common_http_client(server_base);
cli.set_read_timeout(CLI_HTTP_READ_TIMEOUT_SEC, 0);
auto path_with_model = path + (model.empty() ? "" : ("?model=" + model));
auto res = cli.Get(join_path(parts, path_with_model));
if (!res) {
throw std::runtime_error("failed to connect to " + server_base + ": " + httplib::to_string(res.error()));
}
if (res->status < 200 || res->status >= 300) {
throw std::runtime_error("GET " + path + " failed with status " + std::to_string(res->status) + ": " + res->body);
}
return res->body;
}
std::string cli_client::post(const std::string & path, const std::string & body) {
auto [cli, parts] = common_http_client(server_base);
cli.set_read_timeout(CLI_HTTP_READ_TIMEOUT_SEC, 0);
auto res = cli.Post(join_path(parts, path), body, "application/json");
if (!res) {
throw std::runtime_error("failed to connect to " + server_base + ": " + httplib::to_string(res.error()));
}
if (res->status < 200 || res->status >= 300) {
throw std::runtime_error("POST " + path + " failed with status " + std::to_string(res->status) + ": " + res->body);
}
return res->body;
}
std::string cli_client::post_sse(const std::string & path,
const std::string & body,
const std::function<bool()> & should_stop,
const std::function<void(const std::string &)> & on_data) {
auto [cli, parts] = common_http_client(server_base);
cli.set_read_timeout(CLI_HTTP_READ_TIMEOUT_SEC, 0);
std::string pending; // buffer for incomplete SSE lines
std::string raw_body; // accumulated body, used only for error reporting
auto receiver = [&](const char * data, size_t len) -> bool {
if (should_stop()) {
return false; // aborts the request
}
if (raw_body.size() < CLI_HTTP_MAX_ERROR_BODY) {
raw_body.append(data, std::min(len, CLI_HTTP_MAX_ERROR_BODY - raw_body.size()));
}
pending.append(data, len);
size_t pos;
while ((pos = pending.find('\n')) != std::string::npos) {
std::string line = pending.substr(0, pos);
pending.erase(0, pos + 1);
if (!line.empty() && line.back() == '\r') {
line.pop_back();
}
if (line.rfind("data: ", 0) != 0) {
continue;
}
std::string payload = line.substr(6);
if (payload == "[DONE]") {
continue;
}
on_data(payload);
}
return true;
};
httplib::Headers headers = {{"Accept", "text/event-stream"}};
auto res = cli.Post(join_path(parts, path), headers, body, "application/json", receiver);
if (!res) {
if (res.error() == httplib::Error::Canceled && should_stop()) {
return ""; // cancelled by the user
}
return "failed to connect to " + server_base + ": " + httplib::to_string(res.error());
}
if (res->status < 200 || res->status >= 300) {
if (!raw_body.empty()) {
return raw_body;
}
return "request failed with status " + std::to_string(res->status);
}
return "";
}
bool cli_client::wait_health(const std::function<bool()> & is_aborted) {
int connect_attempts = 0;
while (!is_aborted()) {
auto [cli, parts] = common_http_client(server_base);
cli.set_connection_timeout(1, 0);
auto res = cli.Get(join_path(parts, "/health"));
if (res) {
if (res->status == 200) {
return true;
}
// any other status means the server is up but not ready yet
// (e.g. 503 while the model is still loading)
} else if (++connect_attempts >= 10) {
last_error = "failed to connect to " + server_base + ": " + httplib::to_string(res.error());
return false;
}
std::this_thread::sleep_for(std::chrono::milliseconds(300));
}
last_error = "aborted while waiting for the server to become ready";
return false;
}
+33
View File
@@ -0,0 +1,33 @@
#pragma once
#include <functional>
#include <string>
// openai-like client for CLI
struct cli_client {
std::string server_base; // base url, for example "http://127.0.0.1:8080"
std::string last_error; // set when wait_health() fails
std::string model; // optional, set when the server has multiple models (router mode)
// simple GET request, returns the raw response body
// throws std::runtime_error on transport error or non-2xx status
std::string get(const std::string & path);
// simple POST request, returns the raw response body
// throws std::runtime_error on transport error or non-2xx status
std::string post(const std::string & path, const std::string & body);
// POST request with an SSE streaming response
// on_data is invoked per "data:" event with the raw event payload
// returns after the stream is finished (empty string on graceful exit)
// otherwise, the raw error response body
std::string post_sse(const std::string & path,
const std::string & body,
const std::function<bool()> & should_stop,
const std::function<void(const std::string &)> & on_data);
// poll /health until the server is ready to accept requests
// returns false if is_aborted returned true or the server is unreachable
bool wait_health(const std::function<bool()> & is_aborted);
};
+622
View File
@@ -0,0 +1,622 @@
#include "cli-context.h"
#include "cli-ui.h"
#include "arg.h"
#include "base64.hpp"
#include "log.h"
#include "console.h"
#define JSON_ASSERT GGML_ASSERT
#include <nlohmann/json.hpp>
#include <algorithm>
#include <cctype>
#include <filesystem>
#include <fstream>
#include <map>
#include <set>
using json = nlohmann::ordered_json;
struct cli_context_impl {
json messages = json::array();
json pending_media = json::array(); // staged multimodal content parts
};
cli_context::cli_context(const common_params & params) : params(params), impl(new cli_context_impl()) {}
cli_context::~cli_context() {
shutdown();
}
std::atomic<bool> & cli_context::interrupted() {
static std::atomic<bool> flag = false;
return flag;
}
static bool should_stop() {
return cli_context::interrupted().load();
}
static constexpr size_t FILE_GLOB_MAX_RESULTS = 100;
const char * LLAMA_ASCII_LOGO = R"(
)";
// number of values an arg consumes on the command line
static int arg_num_values(const common_arg & opt) {
if (opt.value_hint_2 != nullptr) {
return 2;
}
if (opt.value_hint != nullptr) {
return 1;
}
return 0;
}
static std::string format_error_message(const json & err) {
if (err.contains("error") && err.at("error").is_object()) {
const auto & e = err.at("error");
if (e.contains("message") && e.at("message").is_string()) {
return e.at("message").get<std::string>();
}
}
return err.dump();
}
// err is the raw response body of a failed request; it may or may not be JSON
static std::string format_error_message(const std::string & err) {
json parsed = json::parse(err, nullptr, false);
if (!parsed.is_discarded()) {
return format_error_message(parsed);
}
return err;
}
static std::string media_type_from_ext(const std::string & fname) {
std::string ext = std::filesystem::path(fname).extension().string();
std::transform(ext.begin(), ext.end(), ext.begin(), [](unsigned char c) { return std::tolower(c); });
if (ext == ".wav" || ext == ".mp3") {
return "audio";
}
if (ext == ".mp4" || ext == ".avi" || ext == ".mkv" || ext == ".mov" || ext == ".webm") {
return "video";
}
return "image";
}
bool cli_context::init() {
ui::init(params);
std::optional<ui::spinner> spinner;
bool use_external_server = !params.server_base.empty();
if (use_external_server) {
std::string base = params.server_base;
while (!base.empty() && base.back() == '/') {
base.pop_back();
}
client.server_base = base;
spinner.emplace("Connecting to server at " + base);
} else {
if (params.model.path.empty() && params.model.url.empty() &&
params.model.hf_repo.empty() && params.model.docker_repo.empty()) {
ui::show_error(
"no model specified",
"use -m <file.gguf> or -hf <user/repo> to run a local model,\n"
"or --server-base <url> to connect to a running llama-server"
);
return false;
}
spinner.emplace("\n\nLoading model...");
server.emplace();
if (!server->start(params)) {
ui::show_error("server start failed");
return false;
}
if (!server->wait_ready(should_stop)) {
if (!should_stop()) {
ui::show_error("the server exited before becoming ready");
}
return false;
}
client.server_base = server->address();
}
// for --server-base this is the main availability check; for a spawned
// server it is a cheap sanity check on top of the ready signal
auto is_aborted = [this]() {
return should_stop() || (server && !server->alive());
};
bool healthy = false;
try {
healthy = client.wait_health(is_aborted);
} catch (const std::exception & e) {
client.last_error = e.what();
}
if (!healthy) {
if (!should_stop()) {
ui::show_error(client.last_error);
}
return false;
}
if (use_external_server) {
spinner.reset();
if (!list_and_ask_models()) {
return false;
}
// restore the spinner for the next step
spinner.emplace("Waiting for server...");
}
fetch_server_props();
return true;
}
void cli_context::fetch_server_props() {
try {
json props = json::parse(client.get("/props"));
model_name = props.value("model_alias", "");
if (model_name.empty()) {
const std::string path = props.value("model_path", "");
if (!path.empty()) {
model_name = std::filesystem::path(path).filename().string();
}
}
model_ftype = props.value("model_ftype", "");
build_info = props.value("build_info", "");
if (props.contains("modalities") && props.at("modalities").is_object()) {
const auto & modalities = props.at("modalities");
has_vision = modalities.value("vision", false);
has_audio = modalities.value("audio", false);
has_video = modalities.value("video", false);
}
} catch (const std::exception & e) {
// /props can be disabled on remote servers; not fatal
LOG_DBG("failed to fetch /props: %s\n", e.what());
}
}
bool cli_context::list_and_ask_models() {
json resp = json::parse(client.get("/v1/models"));
if (!resp.contains("data") || !resp.at("data").is_array()) {
throw std::runtime_error("invalid response from /v1/models");
}
std::vector<std::string> models;
std::vector<std::string> models_display;
for (const auto & m : resp.at("data")) {
if (!m.contains("id") || !m.at("id").is_string()) {
continue;
}
std::string name = m.at("id").get<std::string>();
std::string display = name;
if (m.contains("aliases") && m.at("aliases").is_array()) {
std::vector<std::string> aliases;
for (const auto & a : m.at("aliases")) {
if (a.is_string()) {
aliases.push_back(a.get<std::string>());
}
}
if (!aliases.empty()) {
display += " (" + string_join(aliases, ", ") + ")";
}
}
models.push_back(name);
models_display.push_back(display);
}
// only one model: use it without asking
if (models.size() == 1) {
model_name = models[0];
client.model = model_name;
return true;
}
std::string message = "\nAvailable models:";
for (size_t i = 0; i < models_display.size(); ++i) {
message += "\n " + std::to_string(i + 1) + ". " + models_display[i];
}
message += "\n";
ui::show_message(message);
std::string selection;
while (selection.empty()) {
if (should_stop()) {
return false;
}
ui::user_turn user_turn;
selection = user_turn.read_input(false, "Select model by number: ");
if (selection.empty()) {
continue;
}
try {
size_t idx = std::stoul(selection);
if (idx > 0 && idx <= models.size()) {
model_name = models[idx - 1];
client.model = model_name;
ui::show_message("Selected model: " + model_name);
break;
}
} catch (...) {
// ignore
}
ui::show_error("Invalid selection. Please enter a valid number.");
selection.clear();
continue;
}
return true;
}
void cli_context::add_system_prompt() {
if (!params.system_prompt.empty()) {
impl->messages.push_back({
{"role", "system"},
{"content", params.system_prompt}
});
}
}
void cli_context::push_user_message(const std::string & text) {
json content;
if (impl->pending_media.empty()) {
content = text;
} else {
// multimodal message: media parts first, then the text
content = impl->pending_media;
content.push_back({
{"type", "text"},
{"text", text}
});
impl->pending_media = json::array();
}
impl->messages.push_back({
{"role", "user"},
{"content", content}
});
}
bool cli_context::stage_media_file(const std::string & fname, const std::string & type) {
std::ifstream file(fname, std::ios::binary);
if (!file) {
return false;
}
std::string data((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
std::string encoded = base64::encode(data);
if (type == "audio") {
std::string ext = std::filesystem::path(fname).extension().string();
std::transform(ext.begin(), ext.end(), ext.begin(), [](unsigned char c) { return std::tolower(c); });
impl->pending_media.push_back({
{"type", "input_audio"},
{"input_audio", {
{"data", encoded},
{"format", ext == ".mp3" ? "mp3" : "wav"}
}}
});
} else if (type == "video") {
impl->pending_media.push_back({
{"type", "input_video"},
{"input_video", {
{"data", encoded}
}}
});
} else {
// the server detects the actual image type from the data
impl->pending_media.push_back({
{"type", "image_url"},
{"image_url", {
{"url", "data:image/unknown;base64," + encoded}
}}
});
}
return true;
}
bool cli_context::generate_completion(std::string & assistant_content, cli_timings & timings) {
json body = {
{"messages", impl->messages},
{"stream", true},
// in order to get timings even when we cancel mid-way
{"timings_per_token", true},
};
if (!client.model.empty()) {
body["model"] = client.model;
}
bool stream_error = false;
ui::assistant_turn a;
std::string err = client.post_sse("/v1/chat/completions", body.dump(), should_stop, [&](const std::string & payload) {
json chunk = json::parse(payload, nullptr, false);
if (chunk.is_discarded()) {
return;
}
if (chunk.contains("error")) {
stream_error = true;
ui::show_error(format_error_message(chunk));
return;
}
if (chunk.contains("timings")) {
const auto & t = chunk.at("timings");
timings.prompt_per_second = t.value("prompt_per_second", 0.0);
timings.predicted_per_second = t.value("predicted_per_second", 0.0);
}
if (!chunk.contains("choices") || !chunk.at("choices").is_array() || chunk.at("choices").empty()) {
return;
}
const auto & choice = chunk.at("choices").at(0);
if (!choice.contains("delta")) {
return;
}
const auto & delta = choice.at("delta");
if (delta.contains("reasoning_content") && delta.at("reasoning_content").is_string()) {
const std::string text = delta.at("reasoning_content").get<std::string>();
if (!text.empty()) {
a.push(ui::ASSISTANT_DISPLAY_MODE_REASONING, text);
}
}
if (delta.contains("content") && delta.at("content").is_string()) {
const std::string text = delta.at("content").get<std::string>();
if (!text.empty()) {
assistant_content += text;
a.push(ui::ASSISTANT_DISPLAY_MODE_CONTENT, text);
}
}
});
cli_context::interrupted().store(false);
if (!err.empty()) {
ui::show_error(format_error_message(err));
return false;
}
return !stream_error;
}
int cli_context::run() {
add_system_prompt();
std::string modalities = "text";
if (has_vision) {
modalities += ", vision";
}
if (has_audio) {
modalities += ", audio";
}
if (has_video) {
modalities += ", video";
}
std::string banner;
banner += "\n";
banner += LLAMA_ASCII_LOGO;
banner += "\n";
banner += "build : " + build_info + "\n";
banner += "model : " + model_name + "\n";
if (!model_ftype.empty()) {
banner += "ftype : " + model_ftype + "\n";
}
banner += "modalities : " + modalities + "\n";
if (!params.system_prompt.empty()) {
banner += "using custom system prompt\n";
}
banner += "\n";
banner += "available commands:\n";
banner += " /exit or Ctrl+C stop or exit\n";
banner += " /regen regenerate the last response\n";
banner += " /clear clear the chat history\n";
banner += " /read <file> add a text file\n";
banner += " /glob <pattern> add text files using globbing pattern\n";
if (has_vision) {
banner += " /image <file> add an image file\n";
}
if (has_audio) {
banner += " /audio <file> add an audio file\n";
}
if (has_video) {
banner += " /video <file> add a video file\n";
}
banner += "\n";
ui::show_message(banner);
// interactive loop
std::string cur_msg;
auto add_text_file = [&](const std::string & fname) -> bool {
std::ifstream file(fname, std::ios::binary);
if (!file) {
ui::show_error(string_format("file does not exist or cannot be opened: '%s'", fname.c_str()));
return false;
}
std::string content((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
cur_msg += "--- File: ";
cur_msg += fname;
cur_msg += " ---\n";
cur_msg += content;
ui::show_message(string_format("Loaded text from '%s'", fname.c_str()));
return true;
};
while (true) {
std::string buffer;
{
ui::user_turn user_turn;
if (params.prompt.empty()) {
buffer = user_turn.read_input(params.multiline_input);
} else {
// process input prompt from args
for (auto & fname : params.image) {
if (!stage_media_file(fname, media_type_from_ext(fname))) {
ui::show_error(string_format("file does not exist or cannot be opened: '%s'", fname.c_str()));
break;
}
ui::show_message(string_format("Loaded media from '%s'", fname.c_str()));
}
buffer = params.prompt;
user_turn.echo(buffer);
params.prompt.clear(); // only use it once
}
}
if (should_stop()) {
cli_context::interrupted().store(false);
break;
}
// remove trailing newline
if (!buffer.empty() && buffer.back() == '\n') {
buffer.pop_back();
}
// skip empty messages
if (buffer.empty()) {
continue;
}
bool add_user_msg = true;
// process commands
if (string_starts_with(buffer, "/exit")) {
break;
} else if (string_starts_with(buffer, "/regen")) {
if (impl->messages.size() >= 2) {
size_t last_idx = impl->messages.size() - 1;
impl->messages.erase(last_idx);
add_user_msg = false;
} else {
ui::show_error("No message to regenerate.");
continue;
}
} else if (string_starts_with(buffer, "/clear")) {
impl->messages.clear();
add_system_prompt();
impl->pending_media = json::array();
ui::show_message("Chat history cleared.");
continue;
} else if (
(string_starts_with(buffer, "/image ") && has_vision) ||
(string_starts_with(buffer, "/audio ") && has_audio) ||
(string_starts_with(buffer, "/video ") && has_video)) {
std::string type = buffer.substr(1, 5);
// just in case (bad copy-paste for example), we strip all trailing/leading spaces
std::string fname = string_strip(buffer.substr(7));
if (!stage_media_file(fname, type)) {
ui::show_error(string_format("file does not exist or cannot be opened: '%s'", fname.c_str()));
continue;
}
ui::show_message(string_format("Loaded media from '%s'", fname.c_str()));
continue;
} else if (string_starts_with(buffer, "/read ")) {
std::string fname = string_strip(buffer.substr(6));
add_text_file(fname);
continue;
} else if (string_starts_with(buffer, "/glob ")) {
std::error_code ec;
size_t count = 0;
auto curdir = std::filesystem::current_path();
std::string pattern = string_strip(buffer.substr(6));
std::filesystem::path rel_path;
auto startglob = pattern.find_first_of("![*?");
if (startglob != std::string::npos && startglob != 0) {
auto endpath = pattern.substr(0, startglob).find_last_of('/');
if (endpath != std::string::npos) {
std::string rel_pattern = pattern.substr(0, endpath);
#if !defined(_WIN32)
if (string_starts_with(rel_pattern, '~')) {
const char * home = std::getenv("HOME");
if (home && home[0]) {
rel_pattern = home + rel_pattern.substr(1);
}
}
#endif
rel_path = rel_pattern;
pattern.erase(0, endpath + 1);
curdir /= rel_path;
}
}
for (const auto & entry : std::filesystem::recursive_directory_iterator(curdir,
std::filesystem::directory_options::skip_permission_denied, ec)) {
if (!entry.is_regular_file()) {
continue;
}
std::string rel = std::filesystem::relative(entry.path(), curdir, ec).string();
if (ec) {
ec.clear();
continue;
}
std::replace(rel.begin(), rel.end(), '\\', '/');
if (!glob_match(pattern, rel)) {
continue;
}
if (!add_text_file((rel_path / rel).string())) {
continue;
}
if (++count >= FILE_GLOB_MAX_RESULTS) {
ui::show_error(string_format("Maximum number of globbed files allowed (%zu) reached.", FILE_GLOB_MAX_RESULTS));
break;
}
}
continue;
} else {
// not a command
cur_msg += buffer;
}
// generate response
if (add_user_msg) {
push_user_message(cur_msg);
cur_msg.clear();
}
cli_timings timings;
std::string assistant_content;
generate_completion(assistant_content, timings);
impl->messages.push_back({
{"role", "assistant"},
{"content", assistant_content}
});
if (params.show_timings) {
ui::show_info(string_format(
"\n[ Prompt: %.1f t/s | Generation: %.1f t/s ]",
timings.prompt_per_second,
timings.predicted_per_second
));
}
if (params.single_turn) {
break;
}
}
ui::show_message("\n\nExiting...");
return 0;
}
void cli_context::shutdown() {
if (server) {
server->stop();
server.reset();
}
}
+66
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@@ -0,0 +1,66 @@
#pragma once
#include "common.h"
#include "cli-client.h"
#include "cli-server.h"
#include <atomic>
#include <memory>
#include <optional>
#include <string>
struct cli_timings {
double prompt_per_second = 0.0;
double predicted_per_second = 0.0;
};
struct cli_context_impl;
struct cli_context {
common_params params;
cli_client client; // always initialized
std::optional<cli_server> server; // only set when no --server-base is given
// properties of the connected server
// will be populated by fetch_server_props()
std::string model_name;
std::string model_ftype;
std::string build_info;
bool has_vision = false;
bool has_audio = false;
bool has_video = false;
cli_context(const common_params & params);
~cli_context();
// connect to --server-base or spawn a local llama-server child;
// argc/argv are needed to forward the server-relevant args to the child
bool init();
// run the interactive chat loop, returns the process exit code
int run();
// stop the local server child (if any)
void shutdown();
// set by the SIGINT handler; cleared once the interrupt has been handled
static std::atomic<bool> & interrupted();
private:
bool generate_completion(std::string & assistant_content, cli_timings & timings);
void fetch_server_props();
void add_system_prompt();
void push_user_message(const std::string & text);
// check if server have multiple models (router mode)
// if yes, list them then ask; do nothing otherwise
bool list_and_ask_models();
// read a file and stage it as a multimodal content part; type is one of
// "image", "audio", "video"; returns false if the file cannot be read
bool stage_media_file(const std::string & fname, const std::string & type);
std::unique_ptr<cli_context_impl> impl;
};
+89
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@@ -0,0 +1,89 @@
#pragma once
#include <thread>
#include "http.h"
// llama_server will be available as a dynamic library symbol
int llama_server(common_params & params, int argc, char ** argv);
void llama_server_terminate();
struct cli_server {
std::thread th;
int port = -1;
std::atomic<bool> is_alive = false;
std::atomic<bool> is_stopping = false;
~cli_server() {
stop();
}
void stop() {
if (is_stopping.exchange(true)) {
return;
}
if (alive()) {
llama_server_terminate();
}
if (th.joinable()) {
th.join();
}
}
// spawn llama-server in a thread and interact with it via a random port
bool start(common_params & params) {
port = common_http_get_free_port();
if (port <= 0) {
fprintf(stderr, "failed to get a free port\n");
exit(1);
}
is_alive.store(true, std::memory_order_release);
common_params server_params = params; // copy
server_params.port = port;
th = std::thread([this, server_params]() mutable {
// argc / argv are only used in router mode, we can skip them for now
int res = llama_server(server_params, 0, nullptr);
if (res != 0) {
fprintf(stderr, "llama_server exited with code %d\n", res);
}
is_alive.store(false, std::memory_order_release);
});
return true;
}
std::string address() const {
return "http://127.0.0.1:" + std::to_string(port);
}
bool wait_ready(std::function<bool()> should_stop) {
if (!alive()) {
return false;
}
while (!should_stop()) {
auto [cli, parts] = common_http_client(address());
cli.set_connection_timeout(1, 0);
auto res = cli.Get("/health");
if (res) {
if (res->status == 200) {
return true;
}
// any other status means the server is up but not ready yet
// (e.g. 503 while the model is still loading)
}
if (!alive()) {
// in case server die permanently
return false;
}
std::this_thread::sleep_for(std::chrono::milliseconds(200));
}
return true;
}
bool alive() const {
return is_alive.load(std::memory_order_acquire);
}
};
+251
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@@ -0,0 +1,251 @@
#pragma once
#include "common.h"
#include "console.h"
#include <array>
#include <algorithm>
#include <cctype>
#include <filesystem>
#include <string_view>
// TODO?: Make this reusable, enums, docs
static const std::array<std::string_view, 8> cmds = {
"/audio ",
"/clear",
"/exit",
"/glob ",
"/image ",
"/read ",
"/regen",
"/video ",
};
static std::vector<std::pair<std::string, size_t>> auto_completion_callback(std::string_view line, size_t cursor_byte_pos) {
std::vector<std::pair<std::string, size_t>> matches;
std::string cmd;
if (line.length() > 1 && line.front() == '/' && !std::any_of(cmds.begin(), cmds.end(), [line](std::string_view prefix) {
return string_starts_with(line, prefix);
})) {
auto it = cmds.begin();
while ((it = std::find_if(it, cmds.end(), [line](std::string_view cmd_line) {
return string_starts_with(cmd_line, line);
})) != cmds.end()) {
matches.emplace_back(*it, it->length());
++it;
}
} else {
auto it = std::find_if(cmds.begin(), cmds.end(), [line](std::string_view prefix) {
return prefix.back() == ' ' && string_starts_with(line, prefix);
});
if (it != cmds.end()) {
cmd = *it;
}
}
if (!cmd.empty() && cmd != "/glob " && line.length() >= cmd.length() && cursor_byte_pos >= cmd.length()) {
const std::string path_prefix = std::string(line.substr(cmd.length(), cursor_byte_pos - cmd.length()));
const std::string path_postfix = std::string(line.substr(cursor_byte_pos));
auto cur_dir = std::filesystem::current_path();
std::string cur_dir_str = cur_dir.string();
std::string expanded_prefix = path_prefix;
#if !defined(_WIN32)
if (string_starts_with(path_prefix, '~')) {
const char * home = std::getenv("HOME");
if (home && home[0]) {
expanded_prefix = home + path_prefix.substr(1);
}
}
if (string_starts_with(expanded_prefix, '/')) {
#else
if (std::isalpha(static_cast<unsigned char>(expanded_prefix[0])) && expanded_prefix.find(':') == 1) {
#endif
cur_dir = std::filesystem::path(expanded_prefix).parent_path();
cur_dir_str.clear();
} else if (!path_prefix.empty()) {
cur_dir /= std::filesystem::path(path_prefix).parent_path();
}
std::error_code ec;
for (const auto & entry : std::filesystem::directory_iterator(cur_dir, ec)) {
if (ec) {
break;
}
if (!entry.exists(ec)) {
ec.clear();
continue;
}
const std::string path_full = entry.path().string();
std::string path_entry = !cur_dir_str.empty() && string_starts_with(path_full, cur_dir_str) ? path_full.substr(cur_dir_str.length() + 1) : path_full;
if (entry.is_directory(ec)) {
path_entry.push_back(std::filesystem::path::preferred_separator);
}
if (expanded_prefix.empty() || string_starts_with(path_entry, expanded_prefix)) {
const std::string updated_line = cmd + path_entry;
matches.emplace_back(updated_line + path_postfix, updated_line.length());
}
if (ec) {
ec.clear();
}
}
if (matches.empty()) {
const std::string updated_line = cmd + path_prefix;
matches.emplace_back(updated_line + path_postfix, updated_line.length());
}
// Add the longest common prefix
if (!expanded_prefix.empty() && matches.size() > 1) {
const std::string_view match0(matches[0].first);
const std::string_view match1(matches[1].first);
auto it = std::mismatch(match0.begin(), match0.end(), match1.begin(), match1.end());
size_t len = it.first - match0.begin();
for (size_t i = 2; i < matches.size(); ++i) {
const std::string_view matchi(matches[i].first);
auto cmp = std::mismatch(match0.begin(), match0.end(), matchi.begin(), matchi.end());
len = std::min(len, static_cast<size_t>(cmp.first - match0.begin()));
}
const std::string updated_line = std::string(match0.substr(0, len));
matches.emplace_back(updated_line + path_postfix, updated_line.length());
}
std::sort(matches.begin(), matches.end(), [](const auto & a, const auto & b) {
return a.first.compare(0, a.second, b.first, 0, b.second) < 0;
});
}
return matches;
}
// note: make this view implementation generic, so that we can move to TUI in the future if we want to
namespace ui {
static void init(const common_params & params) {
// TODO: avoid using atexit() here by making `console` a singleton
console::init(params.simple_io, params.use_color);
atexit([]() { console::cleanup(); });
console::set_completion_callback(auto_completion_callback);
}
struct spinner {
spinner(const std::string & message) {
if (!message.empty()) {
console::log("%s ", message.c_str());
}
console::spinner::start();
}
~spinner() {
console::spinner::stop();
}
};
struct user_turn {
user_turn() {
console::set_display(DISPLAY_TYPE_USER_INPUT);
}
~user_turn() {
console::set_display(DISPLAY_TYPE_RESET);
}
void echo(const std::string & buffer) {
if (buffer.size() > 500) {
console::log("\n> %s ... (truncated)\n", buffer.substr(0, 500).c_str());
} else {
console::log("\n> %s\n", buffer.c_str());
}
}
std::string read_input(bool multiline_input, const char * prompt = nullptr) {
if (prompt) {
console::log("%s", prompt);
} else {
console::log("\n> ");
}
std::string buffer;
std::string line;
bool another_line = true;
do {
another_line = console::readline(line, multiline_input);
buffer += line;
} while (another_line);
return buffer;
}
};
enum assistant_display_mode {
ASSISTANT_DISPLAY_MODE_REASONING,
ASSISTANT_DISPLAY_MODE_CONTENT,
};
struct assistant_turn {
assistant_display_mode mode = ASSISTANT_DISPLAY_MODE_CONTENT;
bool trailing_newline = true;
bool is_inside_reasoning = false;
assistant_turn() {
console::set_display(DISPLAY_TYPE_RESET);
}
~assistant_turn() {
console::set_display(DISPLAY_TYPE_RESET);
add_newline_if_needed();
}
void push(assistant_display_mode m, const std::string & buffer) {
if (m != mode) {
add_newline_if_needed();
switch (m) {
case ASSISTANT_DISPLAY_MODE_CONTENT:
{
if (is_inside_reasoning) {
console::log("[End thinking]\n\n");
is_inside_reasoning = false;
}
console::set_display(DISPLAY_TYPE_RESET);
} break;
case ASSISTANT_DISPLAY_MODE_REASONING:
{
console::set_display(DISPLAY_TYPE_REASONING);
is_inside_reasoning = true;
console::log("\n[Start thinking]\n\n");
} break;
}
}
mode = m;
if (buffer.empty()) {
return;
}
trailing_newline = buffer.back() == '\n';
console::log("%s", buffer.c_str());
console::flush();
}
void add_newline_if_needed() {
if (!trailing_newline) {
console::log("\n");
console::flush();
}
}
};
static void show_error(const std::string & title, const std::string & message = "") {
console::spinner::stop();
console::error("Error: %s\n", title.c_str());
if (!message.empty()) {
console::log("%s\n", message.c_str());
}
}
static void show_message(const std::string & message) {
console::log("%s\n", message.c_str());
}
static void show_info(const std::string & message) {
console::set_display(DISPLAY_TYPE_INFO);
console::log("%s\n", message.c_str());
console::set_display(DISPLAY_TYPE_RESET);
}
}
+9 -627
View File
@@ -1,20 +1,9 @@
#include "chat.h"
#include "common.h"
#include "arg.h"
#include "console.h"
#include "fit.h"
// #include "log.h"
#include "common.h"
#include "log.h"
#include "server-common.h"
#include "server-context.h"
#include "server-task.h"
#include "cli-context.h"
#include <array>
#include <atomic>
#include <algorithm>
#include <filesystem>
#include <fstream>
#include <thread>
#include <signal.h>
#if defined(_WIN32)
@@ -25,342 +14,19 @@
#include <windows.h>
#endif
const char * LLAMA_ASCII_LOGO = R"(
)";
static std::atomic<bool> g_is_interrupted = false;
static bool should_stop() {
return g_is_interrupted.load();
}
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
static void signal_handler(int) {
if (g_is_interrupted.load()) {
if (cli_context::interrupted().load()) {
// second Ctrl+C - exit immediately
// make sure to clear colors before exiting (not using LOG or console.cpp here to avoid deadlock)
fprintf(stdout, "\033[0m\n");
fflush(stdout);
std::exit(130);
}
g_is_interrupted.store(true);
cli_context::interrupted().store(true);
}
#endif
struct cli_context {
server_context ctx_server;
json messages = json::array();
std::vector<raw_buffer> input_files;
task_params defaults;
bool verbose_prompt;
// thread for showing "loading" animation
std::atomic<bool> loading_show;
cli_context(const common_params & params) {
defaults.sampling = params.sampling;
defaults.speculative = params.speculative;
defaults.n_keep = params.n_keep;
defaults.n_predict = params.n_predict;
defaults.antiprompt = params.antiprompt;
defaults.stream = true; // make sure we always use streaming mode
defaults.timings_per_token = true; // in order to get timings even when we cancel mid-way
// defaults.return_progress = true; // TODO: show progress
verbose_prompt = params.verbose_prompt;
}
std::string generate_completion(result_timings & out_timings) {
server_response_reader rd = ctx_server.get_response_reader();
auto chat_params = format_chat();
{
// TODO: reduce some copies here in the future
server_task task = server_task(SERVER_TASK_TYPE_COMPLETION);
task.id = rd.get_new_id();
task.index = 0;
task.params = defaults; // copy
task.cli_prompt = chat_params.prompt; // copy
task.cli_files = input_files; // copy
task.cli = true;
// chat template settings
task.params.chat_parser_params = common_chat_parser_params(chat_params);
task.params.chat_parser_params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
if (!chat_params.parser.empty()) {
task.params.chat_parser_params.parser.load(chat_params.parser);
}
// Copy the preserved tokens into the sampling params
const llama_vocab * vocab = llama_model_get_vocab(
llama_get_model(ctx_server.get_llama_context()));
for (const auto & token : chat_params.preserved_tokens) {
auto ids = common_tokenize(vocab, token, false, true);
if (ids.size() == 1) {
task.params.sampling.preserved_tokens.insert(ids[0]);
}
}
// reasoning budget sampler
if (!chat_params.thinking_end_tag.empty()) {
task.params.sampling.reasoning_budget_tokens = defaults.sampling.reasoning_budget_tokens;
task.params.sampling.generation_prompt = chat_params.generation_prompt;
if (!chat_params.thinking_start_tag.empty()) {
task.params.sampling.reasoning_budget_start =
common_tokenize(vocab, chat_params.thinking_start_tag, false, true);
}
task.params.sampling.reasoning_budget_end =
common_tokenize(vocab, chat_params.thinking_end_tag, false, true);
task.params.sampling.reasoning_budget_forced =
common_tokenize(vocab, defaults.sampling.reasoning_budget_message + chat_params.thinking_end_tag, false, true);
}
rd.post_task({std::move(task)});
}
if (verbose_prompt) {
console::set_display(DISPLAY_TYPE_PROMPT);
console::log("%s\n\n", chat_params.prompt.c_str());
console::set_display(DISPLAY_TYPE_RESET);
}
// wait for first result
console::spinner::start();
server_task_result_ptr result = rd.next(should_stop);
while (true) {
auto res_partial = dynamic_cast<server_task_result_cmpl_partial *>(result.get());
if (res_partial && res_partial->is_begin) {
// this is the "send 200 status to client" signal in streaming mode
// skip, do not stop the spinner
result = rd.next(should_stop);
} else {
console::spinner::stop();
break;
}
}
std::string curr_content;
bool is_thinking = false;
while (result) {
if (should_stop()) {
break;
}
if (result->is_error()) {
json err_data = result->to_json();
if (err_data.contains("message")) {
console::error("Error: %s\n", err_data["message"].get<std::string>().c_str());
} else {
console::error("Error: %s\n", err_data.dump().c_str());
}
return curr_content;
}
auto res_partial = dynamic_cast<server_task_result_cmpl_partial *>(result.get());
if (res_partial) {
out_timings = std::move(res_partial->timings);
for (const auto & diff : res_partial->oaicompat_msg_diffs) {
if (!diff.content_delta.empty()) {
if (is_thinking) {
console::log("\n[End thinking]\n\n");
console::set_display(DISPLAY_TYPE_RESET);
is_thinking = false;
}
curr_content += diff.content_delta;
console::log("%s", diff.content_delta.c_str());
console::flush();
}
if (!diff.reasoning_content_delta.empty()) {
console::set_display(DISPLAY_TYPE_REASONING);
if (!is_thinking) {
console::log("[Start thinking]\n");
}
is_thinking = true;
console::log("%s", diff.reasoning_content_delta.c_str());
console::flush();
}
}
}
auto res_final = dynamic_cast<server_task_result_cmpl_final *>(result.get());
if (res_final) {
out_timings = std::move(res_final->timings);
break;
}
result = rd.next(should_stop);
}
g_is_interrupted.store(false);
// server_response_reader automatically cancels pending tasks upon destruction
return curr_content;
}
// TODO: support remote files in the future (http, https, etc)
std::string load_input_file(const std::string & fname, bool is_media) {
std::ifstream file = fs_open_ifstream(fname, std::ios::binary);
if (!file) {
return "";
}
if (is_media) {
raw_buffer buf;
buf.assign((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
input_files.push_back(std::move(buf));
return get_media_marker();
} else {
std::string content((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
return content;
}
}
common_chat_params format_chat() {
auto meta = ctx_server.get_meta();
auto & chat_params = meta.chat_params;
auto caps = common_chat_templates_get_caps(chat_params.tmpls.get());
common_chat_templates_inputs inputs;
inputs.messages = common_chat_msgs_parse_oaicompat(messages);
inputs.tools = {}; // TODO
inputs.tool_choice = COMMON_CHAT_TOOL_CHOICE_NONE;
inputs.json_schema = ""; // TODO
inputs.grammar = ""; // TODO
inputs.use_jinja = chat_params.use_jinja;
inputs.parallel_tool_calls = caps["supports_parallel_tool_calls"];
inputs.add_generation_prompt = true;
inputs.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
inputs.force_pure_content = chat_params.force_pure_content;
inputs.enable_thinking = chat_params.enable_thinking ? common_chat_templates_support_enable_thinking(chat_params.tmpls.get()) : false;
// Apply chat template to the list of messages
return common_chat_templates_apply(chat_params.tmpls.get(), inputs);
}
};
// TODO?: Make this reusable, enums, docs
static const std::array<std::string_view, 8> cmds = {
"/audio ",
"/clear",
"/exit",
"/glob ",
"/image ",
"/read ",
"/regen",
"/video ",
};
static std::vector<std::pair<std::string, size_t>> auto_completion_callback(std::string_view line, size_t cursor_byte_pos) {
std::vector<std::pair<std::string, size_t>> matches;
std::string cmd;
if (line.length() > 1 && line.front() == '/' && !std::any_of(cmds.begin(), cmds.end(), [line](std::string_view prefix) {
return string_starts_with(line, prefix);
})) {
auto it = cmds.begin();
while ((it = std::find_if(it, cmds.end(), [line](std::string_view cmd_line) {
return string_starts_with(cmd_line, line);
})) != cmds.end()) {
matches.emplace_back(*it, it->length());
++it;
}
} else {
auto it = std::find_if(cmds.begin(), cmds.end(), [line](std::string_view prefix) {
return prefix.back() == ' ' && string_starts_with(line, prefix);
});
if (it != cmds.end()) {
cmd = *it;
}
}
if (!cmd.empty() && cmd != "/glob " && line.length() >= cmd.length() && cursor_byte_pos >= cmd.length()) {
const std::string path_prefix = std::string(line.substr(cmd.length(), cursor_byte_pos - cmd.length()));
const std::string path_postfix = std::string(line.substr(cursor_byte_pos));
auto cur_dir = std::filesystem::current_path();
std::string cur_dir_str = cur_dir.string();
std::string expanded_prefix = path_prefix;
#if !defined(_WIN32)
if (string_starts_with(path_prefix, '~')) {
const char * home = std::getenv("HOME");
if (home && home[0]) {
expanded_prefix = home + path_prefix.substr(1);
}
}
if (string_starts_with(expanded_prefix, '/')) {
#else
if (std::isalpha(expanded_prefix[0]) && expanded_prefix.find(':') == 1) {
#endif
cur_dir = std::filesystem::path(expanded_prefix).parent_path();
cur_dir_str.clear();
} else if (!path_prefix.empty()) {
cur_dir /= std::filesystem::path(path_prefix).parent_path();
}
std::error_code ec;
for (const auto & entry : std::filesystem::directory_iterator(cur_dir, ec)) {
if (ec) {
break;
}
if (!entry.exists(ec)) {
ec.clear();
continue;
}
const std::string path_full = entry.path().string();
std::string path_entry = !cur_dir_str.empty() && string_starts_with(path_full, cur_dir_str) ? path_full.substr(cur_dir_str.length() + 1) : path_full;
if (entry.is_directory(ec)) {
path_entry.push_back(std::filesystem::path::preferred_separator);
}
if (expanded_prefix.empty() || string_starts_with(path_entry, expanded_prefix)) {
const std::string updated_line = cmd + path_entry;
matches.emplace_back(updated_line + path_postfix, updated_line.length());
}
if (ec) {
ec.clear();
}
}
if (matches.empty()) {
const std::string updated_line = cmd + path_prefix;
matches.emplace_back(updated_line + path_postfix, updated_line.length());
}
// Add the longest common prefix
if (!expanded_prefix.empty() && matches.size() > 1) {
const std::string_view match0(matches[0].first);
const std::string_view match1(matches[1].first);
auto it = std::mismatch(match0.begin(), match0.end(), match1.begin(), match1.end());
size_t len = it.first - match0.begin();
for (size_t i = 2; i < matches.size(); ++i) {
const std::string_view matchi(matches[i].first);
auto cmp = std::mismatch(match0.begin(), match0.end(), matchi.begin(), matchi.end());
len = std::min(len, static_cast<size_t>(cmp.first - match0.begin()));
}
const std::string updated_line = std::string(match0.substr(0, len));
matches.emplace_back(updated_line + path_postfix, updated_line.length());
}
std::sort(matches.begin(), matches.end(), [](const auto & a, const auto & b) {
return a.first.compare(0, a.second, b.first, 0, b.second) < 0;
});
}
return matches;
}
static constexpr size_t FILE_GLOB_MAX_RESULTS = 100;
// satisfies -Wmissing-declarations
int llama_cli(int argc, char ** argv);
@@ -375,25 +41,6 @@ int llama_cli(int argc, char ** argv) {
return 1;
}
// TODO: maybe support it later?
if (params.conversation_mode == COMMON_CONVERSATION_MODE_DISABLED) {
console::error("--no-conversation is not supported by llama-cli\n");
console::error("please use llama-completion instead\n");
}
// struct that contains llama context and inference
cli_context ctx_cli(params);
llama_backend_init();
llama_numa_init(params.numa);
// TODO: avoid using atexit() here by making `console` a singleton
console::init(params.simple_io, params.use_color);
atexit([]() { console::cleanup(); });
console::set_display(DISPLAY_TYPE_RESET);
console::set_completion_callback(auto_completion_callback);
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
struct sigaction sigint_action;
sigint_action.sa_handler = signal_handler;
@@ -408,276 +55,11 @@ int llama_cli(int argc, char ** argv) {
SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
#endif
console::log("\nLoading model... "); // followed by loading animation
console::spinner::start();
if (!ctx_cli.ctx_server.load_model(params)) {
console::spinner::stop();
console::error("\nFailed to load the model\n");
cli_context ctx_cli(params);
if (!ctx_cli.init()) {
return 1;
}
ctx_cli.defaults.sampling = params.sampling;
console::spinner::stop();
console::log("\n");
std::thread inference_thread([&ctx_cli]() {
ctx_cli.ctx_server.start_loop();
});
auto inf = ctx_cli.ctx_server.get_meta();
std::string modalities = "text";
if (inf.has_inp_image) {
modalities += ", vision";
}
if (inf.has_inp_audio) {
modalities += ", audio";
}
auto add_system_prompt = [&]() {
if (!params.system_prompt.empty()) {
ctx_cli.messages.push_back({
{"role", "system"},
{"content", params.system_prompt}
});
}
};
add_system_prompt();
console::log("\n");
console::log("%s\n", LLAMA_ASCII_LOGO);
console::log("build : %s\n", inf.build_info.c_str());
console::log("model : %s\n", inf.model_name.c_str());
if (!inf.model_ftype.empty()) {
console::log("ftype : %s\n", inf.model_ftype.c_str());
}
console::log("modalities : %s\n", modalities.c_str());
if (!params.system_prompt.empty()) {
console::log("using custom system prompt\n");
}
console::log("\n");
console::log("available commands:\n");
console::log(" /exit or Ctrl+C stop or exit\n");
console::log(" /regen regenerate the last response\n");
console::log(" /clear clear the chat history\n");
console::log(" /read <file> add a text file\n");
console::log(" /glob <pattern> add text files using globbing pattern\n");
if (inf.has_inp_image) {
console::log(" /image <file> add an image file\n");
}
if (inf.has_inp_audio) {
console::log(" /audio <file> add an audio file\n");
}
if (inf.has_inp_video) {
console::log(" /video <file> add a video file\n");
}
console::log("\n");
// interactive loop
std::string cur_msg;
auto add_text_file = [&](const std::string & fname) -> bool {
std::string marker = ctx_cli.load_input_file(fname, false);
if (marker.empty()) {
console::error("file does not exist or cannot be opened: '%s'\n", fname.c_str());
return false;
}
if (inf.fim_sep_token != LLAMA_TOKEN_NULL) {
cur_msg += common_token_to_piece(ctx_cli.ctx_server.get_llama_context(), inf.fim_sep_token, true);
cur_msg += fname;
cur_msg.push_back('\n');
} else {
cur_msg += "--- File: ";
cur_msg += fname;
cur_msg += " ---\n";
}
cur_msg += marker;
console::log("Loaded text from '%s'\n", fname.c_str());
return true;
};
while (true) {
std::string buffer;
console::set_display(DISPLAY_TYPE_USER_INPUT);
if (params.prompt.empty()) {
console::log("\n> ");
std::string line;
bool another_line = true;
do {
another_line = console::readline(line, params.multiline_input);
buffer += line;
} while (another_line);
} else {
// process input prompt from args
for (auto & fname : params.image) {
std::string marker = ctx_cli.load_input_file(fname, true);
if (marker.empty()) {
console::error("file does not exist or cannot be opened: '%s'\n", fname.c_str());
break;
}
console::log("Loaded media from '%s'\n", fname.c_str());
cur_msg += marker;
}
buffer = params.prompt;
if (buffer.size() > 500) {
console::log("\n> %s ... (truncated)\n", buffer.substr(0, 500).c_str());
} else {
console::log("\n> %s\n", buffer.c_str());
}
params.prompt.clear(); // only use it once
}
console::set_display(DISPLAY_TYPE_RESET);
console::log("\n");
if (should_stop()) {
g_is_interrupted.store(false);
break;
}
// remove trailing newline
if (!buffer.empty() &&buffer.back() == '\n') {
buffer.pop_back();
}
// skip empty messages
if (buffer.empty()) {
continue;
}
bool add_user_msg = true;
// process commands
if (string_starts_with(buffer, "/exit")) {
break;
} else if (string_starts_with(buffer, "/regen")) {
if (ctx_cli.messages.size() >= 2) {
size_t last_idx = ctx_cli.messages.size() - 1;
ctx_cli.messages.erase(last_idx);
add_user_msg = false;
} else {
console::error("No message to regenerate.\n");
continue;
}
} else if (string_starts_with(buffer, "/clear")) {
ctx_cli.messages.clear();
add_system_prompt();
ctx_cli.input_files.clear();
console::log("Chat history cleared.\n");
continue;
} else if (
(string_starts_with(buffer, "/image ") && inf.has_inp_image) ||
(string_starts_with(buffer, "/audio ") && inf.has_inp_audio) ||
(string_starts_with(buffer, "/video ") && inf.has_inp_video)) {
// just in case (bad copy-paste for example), we strip all trailing/leading spaces
std::string fname = string_strip(buffer.substr(7));
std::string marker = ctx_cli.load_input_file(fname, true);
if (marker.empty()) {
console::error("file does not exist or cannot be opened: '%s'\n", fname.c_str());
continue;
}
cur_msg += marker;
console::log("Loaded media from '%s'\n", fname.c_str());
continue;
} else if (string_starts_with(buffer, "/read ")) {
std::string fname = string_strip(buffer.substr(6));
add_text_file(fname);
continue;
} else if (string_starts_with(buffer, "/glob ")) {
std::error_code ec;
size_t count = 0;
auto curdir = std::filesystem::current_path();
std::string pattern = string_strip(buffer.substr(6));
std::filesystem::path rel_path;
auto startglob = pattern.find_first_of("![*?");
if (startglob != std::string::npos && startglob != 0) {
auto endpath = pattern.substr(0, startglob).find_last_of('/');
if (endpath != std::string::npos) {
std::string rel_pattern = pattern.substr(0, endpath);
#if !defined(_WIN32)
if (string_starts_with(rel_pattern, '~')) {
const char * home = std::getenv("HOME");
if (home && home[0]) {
rel_pattern = home + rel_pattern.substr(1);
}
}
#endif
rel_path = rel_pattern;
pattern.erase(0, endpath + 1);
curdir /= rel_path;
}
}
for (const auto & entry : std::filesystem::recursive_directory_iterator(curdir,
std::filesystem::directory_options::skip_permission_denied, ec)) {
if (!entry.is_regular_file()) {
continue;
}
std::string rel = std::filesystem::relative(entry.path(), curdir, ec).string();
if (ec) {
ec.clear();
continue;
}
std::replace(rel.begin(), rel.end(), '\\', '/');
if (!glob_match(pattern, rel)) {
continue;
}
if (!add_text_file((rel_path / rel).string())) {
continue;
}
if (++count >= FILE_GLOB_MAX_RESULTS) {
console::error("Maximum number of globbed files allowed (%zu) reached.\n", FILE_GLOB_MAX_RESULTS);
break;
}
}
continue;
} else {
// not a command
cur_msg += buffer;
}
// generate response
if (add_user_msg) {
ctx_cli.messages.push_back({
{"role", "user"},
{"content", cur_msg}
});
cur_msg.clear();
}
result_timings timings;
std::string assistant_content = ctx_cli.generate_completion(timings);
ctx_cli.messages.push_back({
{"role", "assistant"},
{"content", assistant_content}
});
console::log("\n");
if (params.show_timings) {
console::set_display(DISPLAY_TYPE_INFO);
console::log("\n");
console::log("[ Prompt: %.1f t/s | Generation: %.1f t/s ]\n", timings.prompt_per_second, timings.predicted_per_second);
console::set_display(DISPLAY_TYPE_RESET);
}
if (params.single_turn) {
break;
}
}
console::set_display(DISPLAY_TYPE_RESET);
console::log("\nExiting...\n");
ctx_cli.ctx_server.terminate();
inference_thread.join();
// bump the log level to display timings
common_log_set_verbosity_thold(LOG_LEVEL_INFO);
common_memory_breakdown_print(ctx_cli.ctx_server.get_llama_context());
return 0;
return ctx_cli.run();
}
+6 -4
View File
@@ -57,7 +57,7 @@ The core architecture consists of the following components:
- `server_tokens`: Unified representation of token sequences (supports both text and multimodal tokens); used by `server_task` and `server_slot`.
- `server_prompt_checkpoint`: For recurrent (e.g., RWKV) and SWA models, stores snapshots of KV cache state. Enables reuse when subsequent requests share the same prompt prefix, saving redundant computation.
- `server_models`: Standalone component for managing multiple backend instances (used in router mode). It is completely independent of `server_context`.
- `stream_session_manager`: Process wide owner of resumable SSE stream sessions (`g_stream_sessions`), keyed by conversation id. Backs the replay buffer that lets a client reattach to a generation after an HTTP disconnect. See the "Resumable streaming" section below.
- `stream_session_manager`: process wide owner of resumable SSE stream sessions, keyed by conversation id. A file-static singleton inside `server-stream.cpp`, driven through `server_stream_session_manager_start/stop`. Backs the replay buffer that lets a client reattach to a generation after an HTTP disconnect. See the "Resumable streaming" section below.
```mermaid
graph TD
@@ -127,10 +127,12 @@ It is opt in via the `X-Conversation-Id` header on `POST /v1/chat/completions`.
The feature lives entirely in `server-stream.{h,cpp}` and rests on three types:
- `stream_session`: a bounded ring buffer (4 MiB cap, oldest bytes drop first) plus a condvar. `append` pushes raw SSE bytes, `read_from` drains from any offset and blocks for live bytes or finalize, `finalize` wakes readers, `cancel` stops the producer. One conv maps to at most one live session.
- `stream_session_manager` (`g_stream_sessions`): owns all sessions keyed by conv id, enforces the one conv one session invariant via `create_or_replace`, and runs a GC thread that drops completed sessions past their TTL.
- `stream_session_manager`: a file-static singleton (`g_stream_sessions`) inside `server-stream.cpp`, owns all sessions keyed by conv id, enforces the one conv one session invariant via `create_or_replace`, and runs a GC thread that drops completed sessions past their TTL. Exposed to main only through `server_stream_session_manager_start/stop`.
- `stream_pipe_producer` / `stream_pipe_consumer`: the write and read ends. The producer owns the session lifetime and finalizes it on destruction; the consumer is read only and never finalizes, so a reader detaching cannot kill a running generation.
Producer side: `server_res_generator` attaches a producer pipe when the header is present. The HTTP content provider mirrors every chunk into the ring before writing it to the socket. While a pipe is attached, `stream_aware_should_stop` ignores peer disconnect, so a dropped socket does not stop generation: only an explicit `DELETE` does. When the peer leaves early, `on_complete` calls `close()`, which drains the rest of the generation into the ring on the http worker.
The implementation is hidden in `server-stream.cpp` (pimpl). The header exposes only the route handler factories, `server_stream_session_attach_pipe`, `server_stream_aware_should_stop`, `server_stream_conv_id_from_headers` and the GC lifecycle; the session, manager and consumer types stay in the `.cpp`.
Producer side: `server_res_generator` attaches a producer pipe when the header is present. The HTTP content provider mirrors every chunk into the ring before writing it to the socket. While a pipe is attached, `server_stream_aware_should_stop` ignores peer disconnect, so a dropped socket does not stop generation: only an explicit `DELETE` does. When the peer leaves early, `on_complete` calls `close()`, which drains the rest of the generation into the ring on the http worker.
Lifetime safety: the producer pipe holds a shared `alive` flag also captured by the session cancel hook. `~server_res_generator` calls `cleanup()` to clear that hook while the reader is still alive, so a `cancel` arriving during teardown can never call `stop()` on a freed response. This ordering is the most fragile part of the feature: finalizing or destroying the producer before `cleanup()` runs reintroduces a use after free.
@@ -144,7 +146,7 @@ Routes:
Router mode binds the same paths to proxy handlers. A `conv_id -> child` map (`conv_models`), populated when a POST is routed, resolves the owning child in one lookup with no polling. The lookup groups ids per child; GET and DELETE proxy straight to the owner. This loopback REST hop is expected to move to a websocket IPC later, swapping only the transport.
Lifecycle: `g_stream_sessions.start_gc()` runs in main after common init, `stop_gc()` runs first in `clean_up()` and finalizes every live session so no reader hangs. Reader blocking and the post drop drain both run on httplib worker threads, which block on a condvar rather than spin.
Lifecycle: `server_stream_session_manager_start()` runs in main after common init, `server_stream_session_manager_stop()` runs first in `clean_up()` and finalizes every live session so no reader hangs. Reader blocking and the post drop drain both run on httplib worker threads, which block on a condvar rather than spin.
| Constant | Value | Role |
| --- | --- | --- |
+1 -1
View File
@@ -228,7 +228,7 @@ For the full list of features, please refer to [server's changelog](https://gith
| `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.10, 0.0 = disabled) |
| `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) |
| `--sleep-idle-seconds SECONDS` | number of seconds of idleness after which the server will sleep (default: -1; -1 = disabled) |
| `--log-prompts-dir PATH` | Log prompts to directory (only used for debugging, default: disabled) |
| `--log-prompts-dir PATH` | Log prompts to directory (auto-created if not present; only used for debugging, default: disabled) |
| `--spec-draft-hf, -hfd, -hfrd, --hf-repo-draft <user>/<model>[:quant]` | Same as --hf-repo, but for the draft model (default: unused)<br/>(env: LLAMA_ARG_SPEC_DRAFT_HF_REPO) |
| `--spec-draft-threads, -td, --threads-draft N` | number of threads to use during generation (default: same as --threads) |
| `--spec-draft-threads-batch, -tbd, --threads-batch-draft N` | number of threads to use during batch and prompt processing (default: same as --threads-draft) |
+3 -2
View File
@@ -4188,7 +4188,7 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
}
};
auto effective_should_stop = stream_aware_should_stop(res_this, req.should_stop);
auto effective_should_stop = server_stream_aware_should_stop(res_this, req.should_stop);
try {
if (effective_should_stop()) {
@@ -4284,7 +4284,7 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
// attach a producer pipe to the response when X-Conversation-Id is present.
// the pipe mirrors SSE chunks into the ring buffer and wires up the cancel hook.
stream_session_attach_pipe(*res, req.headers);
server_stream_session_attach_pipe(*res, req.headers);
return res;
}
@@ -4521,6 +4521,7 @@ void server_routes::init_routes() {
{ "default_generation_settings", default_generation_settings_for_props },
{ "total_slots", params.n_parallel },
{ "model_alias", meta->model_name },
{ "model_ftype", meta->model_ftype },
{ "model_path", meta->model_path },
{ "modalities", json {
{"vision", meta->has_inp_image},
+5 -71
View File
@@ -7,6 +7,7 @@
#include "build-info.h"
#include "preset.h"
#include "download.h"
#include "http.h"
#include <cpp-httplib/httplib.h> // TODO: remove this once we use HTTP client from download.h
#include <optional>
@@ -28,14 +29,7 @@
#include <sstream>
#include <cstring>
#ifdef _WIN32
#include <winsock2.h>
#include <windows.h>
#else
#include <sys/socket.h>
#include <netinet/in.h>
#include <arpa/inet.h>
#include <unistd.h>
#ifndef _WIN32
extern char **environ;
#endif
@@ -716,66 +710,6 @@ std::optional<server_model_meta> server_models::get_meta(const std::string & nam
return std::nullopt;
}
static int get_free_port() {
#ifdef _WIN32
WSADATA wsaData;
if (WSAStartup(MAKEWORD(2, 2), &wsaData) != 0) {
return -1;
}
typedef SOCKET native_socket_t;
#define INVALID_SOCKET_VAL INVALID_SOCKET
#define CLOSE_SOCKET(s) closesocket(s)
#else
typedef int native_socket_t;
#define INVALID_SOCKET_VAL -1
#define CLOSE_SOCKET(s) close(s)
#endif
native_socket_t sock = socket(AF_INET, SOCK_STREAM, 0);
if (sock == INVALID_SOCKET_VAL) {
#ifdef _WIN32
WSACleanup();
#endif
return -1;
}
struct sockaddr_in serv_addr;
std::memset(&serv_addr, 0, sizeof(serv_addr));
serv_addr.sin_family = AF_INET;
serv_addr.sin_addr.s_addr = htonl(INADDR_ANY);
serv_addr.sin_port = htons(0);
if (bind(sock, (struct sockaddr*)&serv_addr, sizeof(serv_addr)) != 0) {
CLOSE_SOCKET(sock);
#ifdef _WIN32
WSACleanup();
#endif
return -1;
}
#ifdef _WIN32
int namelen = sizeof(serv_addr);
#else
socklen_t namelen = sizeof(serv_addr);
#endif
if (getsockname(sock, (struct sockaddr*)&serv_addr, &namelen) != 0) {
CLOSE_SOCKET(sock);
#ifdef _WIN32
WSACleanup();
#endif
return -1;
}
int port = ntohs(serv_addr.sin_port);
CLOSE_SOCKET(sock);
#ifdef _WIN32
WSACleanup();
#endif
return port;
}
// helper to convert vector<string> to char **
// pointers are only valid as long as the original vector is valid
static std::vector<char *> to_char_ptr_array(const std::vector<std::string> & vec) {
@@ -879,7 +813,7 @@ void server_models::load(const std::string & name, const load_options & opts) {
// prepare new instance info
instance_t inst;
inst.meta = meta;
inst.meta.port = get_free_port();
inst.meta.port = common_http_get_free_port();
inst.meta.status = SERVER_MODEL_STATUS_LOADING;
inst.meta.loaded_info = json{};
inst.meta.last_used = ggml_time_ms();
@@ -1681,7 +1615,7 @@ void server_models_routes::init_routes() {
}
// remember which child serves this conversation so the stream routes can route straight
// to it without polling, keyed on the exact conv id from the header
std::string conv_id = stream_conv_id_from_headers(req.headers);
std::string conv_id = server_stream_conv_id_from_headers(req.headers);
if (!conv_id.empty()) {
models.conv_models.remember(conv_id, name);
}
@@ -1896,7 +1830,7 @@ void server_models_routes::init_routes() {
if (!from.empty()) {
child_path += "?from=" + from;
}
SRV_INF("proxying stream resume to model %s on port %d, path=%s\n",
SRV_TRC("proxying stream resume to model %s on port %d, path=%s\n",
owner->name.c_str(), owner->port, child_path.c_str());
auto proxy = std::make_unique<server_http_proxy>(
"GET",
+147 -41
View File
@@ -6,6 +6,12 @@
#include <chrono>
#include <memory>
#include <utility>
#include <shared_mutex>
enum class stream_read_status {
OK,
OFFSET_LOST,
};
namespace {
constexpr int64_t STREAM_SESSION_TTL_SECONDS = 300;
@@ -13,7 +19,6 @@ constexpr size_t STREAM_SESSION_MAX_BYTES = 4 * 1024 * 1024;
constexpr int64_t STREAM_SESSION_GC_INTERVAL_SECONDS = 60;
constexpr int64_t STREAM_READ_WAKE_INTERVAL_MS = 200;
// returns unix time in seconds
int64_t now_seconds() {
return std::chrono::duration_cast<std::chrono::seconds>(
std::chrono::system_clock::now().time_since_epoch()
@@ -21,6 +26,91 @@ int64_t now_seconds() {
}
}
// owns all live sessions keyed by conversation_id, one conv = at most one live session.
// a periodic GC evicts expired ones
class stream_session_manager {
public:
stream_session_manager();
~stream_session_manager();
stream_session_manager(const stream_session_manager &) = delete;
stream_session_manager & operator=(const stream_session_manager &) = delete;
// install a new session, evicting and cancelling any previous one. conversation_id must be non empty
stream_session_ptr create_or_replace(const std::string & conversation_id);
stream_session_ptr get(const std::string & conversation_id);
std::vector<stream_session_ptr> list_all() const;
void evict(const std::string & conversation_id);
void evict_and_cancel(const std::string & conversation_id);
void start_gc();
void stop_gc();
private:
void gc_loop();
mutable std::shared_mutex map_mu;
std::unordered_map<std::string, stream_session_ptr> sessions; // key: conversation_id
std::thread gc_thread;
bool running;
std::mutex gc_wake_mu;
std::condition_variable gc_wake_cv;
};
// process wide manager, lifecycle controlled by llama-server main() via start_gc/stop_gc
static stream_session_manager g_stream_sessions;
void server_stream_session_manager_start() {
g_stream_sessions.start_gc();
}
void server_stream_session_manager_stop() {
g_stream_sessions.stop_gc();
}
struct stream_session {
std::string conversation_id;
int64_t started_ts; // unix seconds at construction
stream_session(std::string conversation_id_, size_t max_bytes_);
stream_session(const stream_session &) = delete;
stream_session & operator=(const stream_session &) = delete;
bool append(const char * data, size_t len);
void finalize();
// drain from offset into sink, blocking for more bytes or finalize. OFFSET_LOST if offset
// fell below the dropped prefix
stream_read_status read_from(size_t offset,
const std::function<bool(const char *, size_t)> & sink,
const std::function<bool()> & should_stop);
bool is_done() const;
bool is_cancelled() const;
size_t total_size() const; // bytes that ever entered the session
size_t dropped_prefix() const; // bytes evicted from the front due to cap
int64_t completed_at() const; // 0 while alive, unix seconds after finalize
void set_stop_producer(std::function<void()> fn);
void cancel();
private:
mutable std::mutex mu;
std::condition_variable cv;
std::vector<char> buffer;
size_t prefix_dropped;
size_t cap_bytes;
bool done;
std::atomic<bool> cancelled; // polled lock-free by the should_stop closure, no mu
int64_t completed_ts;
std::function<void()> stop_producer;
};
stream_session::stream_session(std::string conversation_id_, size_t max_bytes_)
: conversation_id(std::move(conversation_id_))
, started_ts(now_seconds())
@@ -38,7 +128,7 @@ bool stream_session::append(const char * data, size_t len) {
}
{
std::lock_guard<std::mutex> lock(mu);
if (done.load(std::memory_order_relaxed)) {
if (done) {
return false;
}
if (len >= cap_bytes) {
@@ -62,11 +152,14 @@ bool stream_session::append(const char * data, size_t len) {
}
void stream_session::finalize() {
bool was_done = done.exchange(true, std::memory_order_acq_rel);
if (was_done) {
return;
{
std::lock_guard<std::mutex> lock(mu);
if (done) {
return;
}
done = true;
completed_ts = now_seconds();
}
completed_ts.store(now_seconds(), std::memory_order_release);
cv.notify_all();
}
@@ -96,7 +189,7 @@ stream_read_status stream_session::read_from(size_t offset,
lock.lock();
continue;
}
if (done.load(std::memory_order_acquire)) {
if (done) {
return stream_read_status::OK;
}
// wait for new bytes, finalize, or a periodic wake to re check should_stop
@@ -105,7 +198,8 @@ stream_read_status stream_session::read_from(size_t offset,
}
bool stream_session::is_done() const {
return done.load(std::memory_order_acquire);
std::lock_guard<std::mutex> lock(mu);
return done;
}
size_t stream_session::total_size() const {
@@ -119,7 +213,8 @@ size_t stream_session::dropped_prefix() const {
}
int64_t stream_session::completed_at() const {
return completed_ts.load(std::memory_order_acquire);
std::lock_guard<std::mutex> lock(mu);
return completed_ts;
}
void stream_session::set_stop_producer(std::function<void()> fn) {
@@ -128,7 +223,7 @@ void stream_session::set_stop_producer(std::function<void()> fn) {
}
void stream_session::cancel() {
// flip cancelled first so the producer-side stream_aware_should_stop can break out of the
// flip cancelled first so the producer-side server_stream_aware_should_stop can break out of the
// recv() wait even if remove_waiting_task_ids does not notify the condvar (the cancel task
// posted by rd.stop() will eventually notify, but we do not want to depend on that timing)
cancelled.store(true, std::memory_order_release);
@@ -237,18 +332,24 @@ void stream_session_manager::evict_and_cancel(const std::string & conversation_i
}
void stream_session_manager::start_gc() {
if (running.exchange(true)) {
return;
{
std::lock_guard<std::mutex> lock(gc_wake_mu);
if (running) {
return;
}
running = true;
}
gc_thread = std::thread([this] { gc_loop(); });
}
void stream_session_manager::stop_gc() {
bool was_running = running.exchange(false);
bool was_running;
{
std::lock_guard<std::mutex> lock(gc_wake_mu);
was_running = running;
running = false;
}
if (was_running) {
{
std::lock_guard<std::mutex> lock(gc_wake_mu);
}
gc_wake_cv.notify_all();
if (gc_thread.joinable()) {
gc_thread.join();
@@ -270,15 +371,15 @@ void stream_session_manager::stop_gc() {
}
void stream_session_manager::gc_loop() {
while (running.load(std::memory_order_acquire)) {
while (true) {
{
std::unique_lock<std::mutex> lock(gc_wake_mu);
gc_wake_cv.wait_for(lock,
std::chrono::seconds(STREAM_SESSION_GC_INTERVAL_SECONDS),
[this] { return !running.load(std::memory_order_acquire); });
}
if (!running.load(std::memory_order_acquire)) {
return;
[this] { return !running; });
if (!running) {
return;
}
}
int64_t cutoff = now_seconds() - STREAM_SESSION_TTL_SECONDS;
std::vector<stream_session_ptr> to_drop;
@@ -301,10 +402,19 @@ void stream_session_manager::gc_loop() {
}
}
// process wide manager, lifecycle controlled by llama-server main() via start_gc/stop_gc
stream_session_manager g_stream_sessions;
// stream_pipe
// stream_pipe ---------------------------------------------------------------------------------
// consumer end: read-only replay of the ring buffer, the destructor does not finalize the session
struct stream_pipe_consumer : stream_pipe {
stream_read_status read(size_t & offset,
const std::function<bool(const char *, size_t)> & sink,
const std::function<bool()> & should_stop);
static std::shared_ptr<stream_pipe_consumer> create(stream_session_ptr session);
private:
explicit stream_pipe_consumer(stream_session_ptr session);
};
stream_pipe::stream_pipe(stream_session_ptr session)
: session_(std::move(session)) {
@@ -408,12 +518,10 @@ static server_http_res_ptr make_error_response(int status, const std::string & m
return res;
}
server_http_context::handler_t make_stream_get_handler() {
server_http_context::handler_t server_stream_make_get_handler() {
return [](const server_http_req & req) -> server_http_res_ptr {
// GET /v1/stream/<conv_id>?from=N replays the SSE bytes already buffered for the
// session, blocks for more bytes when the session is still running, returns when
// the session is finalized. the body is streamed back as text/event-stream so the
// browser EventSource can attach to it like a fresh request
// GET /v1/stream/<conv_id>?from=N replays buffered SSE bytes then blocks for live
// bytes until the session finalizes, streamed as text/event-stream for EventSource
std::string conv_id = req.get_param("conv_id");
if (conv_id.empty()) {
return make_error_response(400, "Missing conversation id in path", ERROR_TYPE_INVALID_REQUEST);
@@ -459,11 +567,10 @@ server_http_context::handler_t make_stream_get_handler() {
};
}
server_http_context::handler_t make_streams_lookup_handler() {
server_http_context::handler_t server_stream_make_lookup_handler() {
return [](const server_http_req & req) -> server_http_res_ptr {
// POST /v1/streams/lookup with body {"conversation_ids": ["X", "Y", ...]} returns the
// matching sessions, only for ids the caller already knows. each id matches the exact key
// and any "<id>::<model>" variant, so one lookup covers every per model session for a conv
// POST /v1/streams/lookup returns the matching sessions, only for ids the caller already
// knows. each id matches the exact key and any "<id>::<model>" per model variant
std::vector<std::string> requested;
try {
json body = json::parse(req.body);
@@ -518,11 +625,10 @@ server_http_context::handler_t make_streams_lookup_handler() {
};
}
server_http_context::handler_t make_stream_delete_handler() {
server_http_context::handler_t server_stream_make_delete_handler() {
return [](const server_http_req & req) -> server_http_res_ptr {
// DELETE /v1/stream/<conv_id> is the explicit user Stop, cancels the producer hook
// wired by handle_completions_impl and evicts the buffer. idempotent, a session that
// already finalized or was never created returns 204 either way
// DELETE /v1/stream/<conv_id> is the explicit user Stop, cancels the producer and evicts
// the buffer. idempotent, returns 204 even if the session was already gone
std::string conv_id = req.get_param("conv_id");
if (conv_id.empty()) {
return make_error_response(400, "Missing conversation id in path", ERROR_TYPE_INVALID_REQUEST);
@@ -536,7 +642,7 @@ server_http_context::handler_t make_stream_delete_handler() {
};
}
std::string stream_conv_id_from_headers(const std::map<std::string, std::string> & headers) {
std::string server_stream_conv_id_from_headers(const std::map<std::string, std::string> & headers) {
// case-insensitive scan for x-conversation-id
static constexpr char target[] = "x-conversation-id";
static constexpr size_t target_len = sizeof(target) - 1;
@@ -555,8 +661,8 @@ std::string stream_conv_id_from_headers(const std::map<std::string, std::string>
return std::string();
}
void stream_session_attach_pipe(server_http_res & res, const std::map<std::string, std::string> & headers) {
std::string conversation_id = stream_conv_id_from_headers(headers);
void server_stream_session_attach_pipe(server_http_res & res, const std::map<std::string, std::string> & headers) {
std::string conversation_id = server_stream_conv_id_from_headers(headers);
SRV_TRC("conv_id=%s (empty=%d)\n", conversation_id.c_str(), conversation_id.empty() ? 1 : 0);
if (conversation_id.empty()) {
return;
@@ -565,7 +671,7 @@ void stream_session_attach_pipe(server_http_res & res, const std::map<std::strin
res.spipe = stream_pipe_producer::create(session, res);
}
std::function<bool()> stream_aware_should_stop(server_http_res * res, std::function<bool()> fallback) {
std::function<bool()> server_stream_aware_should_stop(server_http_res * res, std::function<bool()> fallback) {
return [res, fallback = std::move(fallback)]() -> bool {
if (res->spipe) {
return res->spipe->is_cancelled();
+15 -136
View File
@@ -3,81 +3,23 @@
#include "server-http.h"
#include <atomic>
#include <condition_variable>
#include <cstddef>
#include <cstdint>
#include <functional>
#include <memory>
#include <mutex>
#include <shared_mutex>
#include <string>
#include <thread>
#include <unordered_map>
#include <vector>
enum class stream_read_status {
OK,
OFFSET_LOST,
};
// streaming buffer for one generation, survives HTTP disconnect. the producer appends SSE bytes,
// readers drain from any offset via read_from. keyed by conversation_id, one conv = one live session
// streaming buffer for one generation, survives HTTP disconnect. the producer appends raw SSE
// bytes, readers drain from any offset via read_from and block until more bytes or finalize.
// keyed by conversation_id: one conv = at most one live session
struct stream_session {
std::string conversation_id;
int64_t started_ts; // unix seconds at construction, used by /v1/streams listing
stream_session(std::string conversation_id_, size_t max_bytes_);
stream_session(const stream_session &) = delete;
stream_session & operator=(const stream_session &) = delete;
// append raw bytes, drops from the front if the cap is reached.
// returns false if the session is already finalized
bool append(const char * data, size_t len);
// mark the session as complete, wakes all pending readers
void finalize();
// drain bytes from offset, calling sink for each chunk. blocks until more
// bytes arrive or finalize is called. returns OK on clean exit, OFFSET_LOST
// if offset falls below the dropped prefix
stream_read_status read_from(size_t offset,
const std::function<bool(const char *, size_t)> & sink,
const std::function<bool()> & should_stop);
bool is_done() const;
bool is_cancelled() const;
size_t total_size() const; // bytes that ever entered the session
size_t dropped_prefix() const; // bytes evicted from the front due to cap
int64_t completed_at() const; // 0 while alive, unix seconds after finalize
// attach the producer stop hook used to cancel its reader, pass an empty function to detach
void set_stop_producer(std::function<void()> fn);
// signal the producer to abort its inference asap via the stop hook, idempotent
void cancel();
private:
mutable std::mutex mu;
std::condition_variable cv;
std::vector<char> buffer;
size_t prefix_dropped;
size_t cap_bytes;
std::atomic<bool> done;
std::atomic<bool> cancelled;
std::atomic<int64_t> completed_ts;
std::function<void()> stop_producer; // protected by mu
};
struct stream_session;
using stream_session_ptr = std::shared_ptr<stream_session>;
// one end of a stream_session pipe. the base holds the session and the shared query, the
// producer and consumer ends derive from it. virtual dtor so each end runs its own teardown:
// base of the producer/consumer pipe ends. virtual dtor so each runs its own teardown:
// the producer finalizes the session, the consumer leaves it untouched
struct stream_pipe {
virtual ~stream_pipe() = default;
// true if the session was cancelled (e.g. via DELETE /v1/stream/<conv_id>)
bool is_cancelled() const;
protected:
@@ -95,7 +37,6 @@ protected:
struct stream_pipe_producer : stream_pipe {
~stream_pipe_producer() override;
// append raw bytes to the session's ring buffer, returns false if already finalized
bool write(const char * data, size_t len);
// mark the natural end on the wire so a later close() is a no-op
@@ -121,83 +62,21 @@ private:
server_http_res * res_ = nullptr;
};
// consumer end: read-only replay of the ring buffer, the destructor does not finalize the session
struct stream_pipe_consumer : stream_pipe {
// drain bytes from offset, calling sink for each available chunk. blocks until more data
// arrives or the session finalizes. should_stop is polled, returns OFFSET_LOST if offset
// fell below the dropped prefix
stream_read_status read(size_t & offset,
const std::function<bool(const char *, size_t)> & sink,
const std::function<bool()> & should_stop);
void server_stream_session_manager_start();
void server_stream_session_manager_stop();
static std::shared_ptr<stream_pipe_consumer> create(stream_session_ptr session);
// route handler factories wired under /v1/stream/* by server.cpp
server_http_context::handler_t server_stream_make_get_handler();
server_http_context::handler_t server_stream_make_lookup_handler();
server_http_context::handler_t server_stream_make_delete_handler();
private:
explicit stream_pipe_consumer(stream_session_ptr session);
};
// extract the X-Conversation-Id header value (case-insensitive), empty when absent
std::string server_stream_conv_id_from_headers(const std::map<std::string, std::string> & headers);
// owns all live sessions, runs a periodic GC to evict expired ones.
// the map is keyed by conversation_id, so the invariant "one conv = at most one
// live session" is enforced at the type level
class stream_session_manager {
public:
stream_session_manager();
~stream_session_manager();
stream_session_manager(const stream_session_manager &) = delete;
stream_session_manager & operator=(const stream_session_manager &) = delete;
// install a new session for this conversation, evicting and cancelling any previous one.
// the conversation_id must be non empty, the caller is responsible for that check.
// returns the new session
stream_session_ptr create_or_replace(const std::string & conversation_id);
// lookup, returns null if unknown or already evicted
stream_session_ptr get(const std::string & conversation_id);
// list every live or recently completed session, used by GET /v1/streams without filter
std::vector<stream_session_ptr> list_all() const;
// remove from the map and finalize, wakes any pending readers
void evict(const std::string & conversation_id);
// signal the producer to cancel asap then evict, used by the explicit user Stop path
void evict_and_cancel(const std::string & conversation_id);
void start_gc();
void stop_gc();
private:
void gc_loop();
mutable std::shared_mutex map_mu;
std::unordered_map<std::string, stream_session_ptr> sessions; // key: conversation_id
std::thread gc_thread;
std::atomic<bool> running;
std::mutex gc_wake_mu;
std::condition_variable gc_wake_cv;
};
// process wide manager, linked by both llama-server and llama-cli. llama-server main() drives
// start_gc/stop_gc, llama-cli leaves it idle. the dtor calls stop_gc() unconditionally so exit
// is safe whether or not the GC thread ran
extern stream_session_manager g_stream_sessions;
// route handler factories operating on g_stream_sessions, wired under /v1/stream/* by server.cpp.
// keeps the resumable stream surface confined to server-stream
server_http_context::handler_t make_stream_get_handler();
server_http_context::handler_t make_streams_lookup_handler();
server_http_context::handler_t make_stream_delete_handler();
// extract the X-Conversation-Id header value (case-insensitive), empty when absent. exposed so
// the router can track which child serves a forwarded POST
std::string stream_conv_id_from_headers(const std::map<std::string, std::string> & headers);
// on an X-Conversation-Id header, create or replace the session and attach a producer pipe to
// res. no-op when absent, called from the server_res_generator constructor
void stream_session_attach_pipe(server_http_res & res, const std::map<std::string, std::string> & headers);
// on an X-Conversation-Id header, create or replace the session and attach a producer pipe to res
void server_stream_session_attach_pipe(server_http_res & res, const std::map<std::string, std::string> & headers);
// should_stop closure that ignores peer disconnect when a pipe is attached, so only an explicit
// DELETE stops the producer and generation keeps flowing into the ring buffer. without a pipe it
// delegates to fallback, the legacy non-resumable flow
std::function<bool()> stream_aware_should_stop(server_http_res * res, std::function<bool()> fallback);
std::function<bool()> server_stream_aware_should_stop(server_http_res * res, std::function<bool()> fallback);
+54 -31
View File
@@ -36,6 +36,19 @@ static inline void signal_handler(int signal) {
shutdown_handler(signal);
}
// satisfies -Wmissing-declarations (used by llama command)
int llama_server(int argc, char ** argv);
// to be used via CLI (argc / argv are used by router mode only)
int llama_server(common_params & params, int argc, char ** argv);
void llama_server_terminate();
void llama_server_terminate() {
if (shutdown_handler) {
shutdown_handler(0);
}
}
// wrapper function that handles exceptions and logs errors
// this is to make sure handler_t never throws exceptions; instead, it returns an error response
static server_http_context::handler_t ex_wrapper(server_http_context::handler_t func) {
@@ -72,9 +85,6 @@ static server_http_context::handler_t ex_wrapper(server_http_context::handler_t
};
}
// satisfies -Wmissing-declarations
int llama_server(int argc, char ** argv);
int llama_server(int argc, char ** argv) {
std::setlocale(LC_NUMERIC, "C");
@@ -85,7 +95,7 @@ int llama_server(int argc, char ** argv) {
// start the stream session manager GC right after common init, before any HTTP route can
// touch it. lifecycle is symmetric, stop_gc() runs in clean_up() before backend free
g_stream_sessions.start_gc();
server_stream_session_manager_start();
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SERVER)) {
return 1;
@@ -94,16 +104,26 @@ int llama_server(int argc, char ** argv) {
llama_backend_init();
llama_numa_init(params.numa);
return llama_server(params, argc, argv);
}
int llama_server(common_params & params, int argc, char ** argv) {
bool is_run_by_cli = (argv == nullptr);
common_models_handler models_handler;
try {
models_handler = common_models_handler_init(params, LLAMA_EXAMPLE_SERVER);
if (common_models_handler_is_preset_repo(models_handler)) {
// apply the preset and start the server in router mode
common_models_handler_apply(models_handler, params);
// note: router mode also accepts -hf remote-preset, so we need to check that first
if (!is_run_by_cli && !params.model.hf_repo.empty()) {
try {
models_handler = common_models_handler_init(params, LLAMA_EXAMPLE_SERVER);
if (common_models_handler_is_preset_repo(models_handler)) {
// apply the preset and start the server in router mode
common_models_handler_apply(models_handler, params);
}
} catch (const std::exception & e) {
SRV_ERR("failed to fetch model metadata: %s\n", e.what());
return 1;
}
} catch (const std::exception & e) {
SRV_ERR("failed to fetch model metadata: %s\n", e.what());
return 1;
}
// router server never loads a model and must not touch the GPU
@@ -245,8 +265,8 @@ int llama_server(int argc, char ** argv) {
ctx_http.post("/slots/:id_slot", ex_wrapper(routes.post_slots));
// resumable streaming, the conversation_id is the session identity end to end. router and
// child wire different handlers under the same paths: a child binds the local g_stream_sessions
// backed factories, the router binds proxies that resolve the owning child through the
// child wire different handlers under the same paths: a child binds the local session
// factories, the router binds proxies that resolve the owning child through the
// conv_id -> model map
server_http_context::handler_t stream_get_h;
server_http_context::handler_t streams_lookup_h;
@@ -256,9 +276,9 @@ int llama_server(int argc, char ** argv) {
streams_lookup_h = models_routes->router_streams_lookup;
stream_delete_h = models_routes->router_stream_delete;
} else {
stream_get_h = make_stream_get_handler();
streams_lookup_h = make_streams_lookup_handler();
stream_delete_h = make_stream_delete_handler();
stream_get_h = server_stream_make_get_handler();
streams_lookup_h = server_stream_make_lookup_handler();
stream_delete_h = server_stream_make_delete_handler();
}
ctx_http.get ("/v1/stream/:conv_id", ex_wrapper(stream_get_h));
// POST /v1/streams/lookup with body {"conversation_ids": [...]}. you can only ask for ids
@@ -321,8 +341,9 @@ int llama_server(int argc, char ** argv) {
if (child.is_child() && child.get_mode() == SERVER_CHILD_MODE_DOWNLOAD) {
return child.run_download(params);
} else if (!is_router_server) {
} else if (!is_router_server && !is_run_by_cli) {
// single-model mode (NOT spawned by router)
// if this is invoked by CLI, model downloading should be already handled
try {
common_models_handler_apply(models_handler, params);
} catch (const std::exception & e) {
@@ -343,7 +364,7 @@ int llama_server(int argc, char ** argv) {
clean_up = [&models_routes]() {
SRV_INF("%s: cleaning up before exit...\n", __func__);
// stop the session GC first, it finalizes live sessions and wakes pending readers
g_stream_sessions.stop_gc();
server_stream_session_manager_stop();
if (models_routes.has_value()) {
models_routes->stopping.store(true); // maybe redundant, but just to be safe
models_routes->models.unload_all();
@@ -371,7 +392,7 @@ int llama_server(int argc, char ** argv) {
clean_up = [&ctx_http, &ctx_server]() {
SRV_INF("%s: cleaning up before exit...\n", __func__);
// stop the session GC first, it finalizes live sessions and wakes pending readers
g_stream_sessions.stop_gc();
server_stream_session_manager_stop();
ctx_http.stop();
ctx_server.terminate();
llama_backend_free();
@@ -411,20 +432,22 @@ int llama_server(int argc, char ** argv) {
};
}
// TODO: refactor in common/console
// register signal handler if not running by CLI
if (!is_run_by_cli) {
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
struct sigaction sigint_action;
sigint_action.sa_handler = signal_handler;
sigemptyset (&sigint_action.sa_mask);
sigint_action.sa_flags = 0;
sigaction(SIGINT, &sigint_action, NULL);
sigaction(SIGTERM, &sigint_action, NULL);
struct sigaction sigint_action;
sigint_action.sa_handler = signal_handler;
sigemptyset (&sigint_action.sa_mask);
sigint_action.sa_flags = 0;
sigaction(SIGINT, &sigint_action, NULL);
sigaction(SIGTERM, &sigint_action, NULL);
#elif defined (_WIN32)
auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false;
};
SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false;
};
SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
#endif
}
SRV_INF("listening on %s\n", ctx_http.listening_address.c_str());
+4 -4
View File
@@ -11,7 +11,7 @@
"@chromatic-com/storybook": "5.0.0",
"@eslint/compat": "1.4.1",
"@eslint/js": "9.39.2",
"@internationalized/date": "3.10.1",
"@internationalized/date": "3.12.2",
"@lucide/svelte": "0.515.0",
"@modelcontextprotocol/sdk": "1.26.0",
"@playwright/test": "1.56.1",
@@ -2981,9 +2981,9 @@
}
},
"node_modules/@internationalized/date": {
"version": "3.10.1",
"resolved": "https://registry.npmjs.org/@internationalized/date/-/date-3.10.1.tgz",
"integrity": "sha512-oJrXtQiAXLvT9clCf1K4kxp3eKsQhIaZqxEyowkBcsvZDdZkbWrVmnGknxs5flTD0VGsxrxKgBCZty1EzoiMzA==",
"version": "3.12.2",
"resolved": "https://registry.npmjs.org/@internationalized/date/-/date-3.12.2.tgz",
"integrity": "sha512-FY1Y+H64NDs+HAF6omlnWxm3mEpfgaCSWtL5l551ZZfImA+kGjPFgrnJrGjH6lfmLL0g8Z/mBu1R3kufeCp6Jw==",
"dev": true,
"license": "Apache-2.0",
"dependencies": {
+1 -1
View File
@@ -30,7 +30,7 @@
"@chromatic-com/storybook": "5.0.0",
"@eslint/compat": "1.4.1",
"@eslint/js": "9.39.2",
"@internationalized/date": "3.10.1",
"@internationalized/date": "3.12.2",
"@lucide/svelte": "0.515.0",
"@modelcontextprotocol/sdk": "1.26.0",
"@playwright/test": "1.56.1",
@@ -11,7 +11,8 @@
} from '$lib/constants';
import {
ChatFormActionAddToolsSubmenu,
ChatFormActionAddMcpServersSubmenu
ChatFormActionAddMcpServersSubmenu,
ChatFormActionAddReasoningSubmenu
} from '$lib/components/app';
import { useAttachmentMenu } from '$lib/hooks/use-attachment-menu.svelte';
@@ -92,7 +93,11 @@
</Tooltip.Content>
</Tooltip.Root>
<DropdownMenu.Content align="start" class="w-48">
<DropdownMenu.Content align="start" class="w-52">
<ChatFormActionAddReasoningSubmenu />
<DropdownMenu.Separator />
<DropdownMenu.Sub>
<DropdownMenu.SubTrigger class="flex cursor-pointer items-center gap-2">
<File class="h-4 w-4" />
@@ -2,7 +2,7 @@
import { Lightbulb, LightbulbOff, Check, Info } from '@lucide/svelte';
import * as DropdownMenu from '$lib/components/ui/dropdown-menu';
import * as Tooltip from '$lib/components/ui/tooltip';
import { ReasoningEffort, MessageRole } from '$lib/enums';
import { ReasoningEffort } from '$lib/enums';
import { REASONING_EFFORT_TOKENS } from '$lib/constants/reasoning-effort-tokens';
import { REASONING_EFFORT_LEVELS } from '$lib/constants/reasoning-effort';
import type { ReasoningEffortLevel } from '$lib/types';
@@ -18,31 +18,23 @@
import { isRouterMode } from '$lib/stores/server.svelte';
import type { DatabaseMessage } from '$lib/types/database';
let thinkingEnabled = $derived(conversationsStore.getThinkingEnabled());
let currentEffort = $derived(conversationsStore.getReasoningEffort());
let isOff = $derived(!thinkingEnabled);
let tooltipText = $derived(thinkingEnabled ? `${currentEffort} Reasoning` : 'Disabled Reasoning');
let subOpen = $state(false);
// Get conversation model from message history
let conversationModel = $derived(
chatStore.getConversationModel(activeMessages() as DatabaseMessage[])
);
// Fallback: if model props aren't available, check if any assistant messages
// for this model in the active conversation have reasoning content.
let modelSupportsThinkingFromMessages = $derived.by(() => {
const modelId = isRouterMode() ? modelsStore.selectedModelName || conversationModel : null;
if (!modelId) return false;
const messages = conversationsStore.activeMessages;
return messages.some(
(m: DatabaseMessage) =>
m.role === MessageRole.ASSISTANT && m.model === modelId && !!m.reasoningContent
(m) => m.role === 'assistant' && m.model === modelId && !!m.reasoningContent
);
});
// Check if model supports thinking. Primary: chat template from /props.
// Fallback: message history (reasoning content in assistant messages).
let modelSupportsThinking = $derived.by(() => {
loadedModelIds();
propsCacheVersion();
@@ -52,15 +44,15 @@
return checkModelSupportsThinking(modelId ?? '') || modelSupportsThinkingFromMessages;
}
// In non-router mode, use the built-in supportsThinking
return supportsThinking() || modelSupportsThinkingFromMessages;
});
// Check if current item is selected
let thinkingEnabled = $derived(conversationsStore.getThinkingEnabled());
let currentEffort = $derived(conversationsStore.getReasoningEffort());
let isOff = $derived(!thinkingEnabled);
function isSelected(item: ReasoningEffortLevel): boolean {
if (item.isOff) {
return isOff;
}
if (item.isOff) return isOff;
return thinkingEnabled && currentEffort === item.value;
}
@@ -76,39 +68,30 @@
</script>
{#if modelSupportsThinking}
<DropdownMenu.Root bind:open={subOpen}>
<Tooltip.Root>
<Tooltip.Trigger>
<DropdownMenu.Trigger
class={[
'flex h-6 w-6 cursor-pointer items-center justify-center rounded-full p-0 transition-colors focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2',
thinkingEnabled ? 'bg-amber-400/10 hover:bg-amber-400/20' : 'bg-muted'
]}
aria-label={`${tooltipText}. Click to configure.`}
>
{#if thinkingEnabled}
<Lightbulb class="h-3 w-3 text-amber-400" />
{:else}
<LightbulbOff class="h-3 w-3 text-muted-foreground" />
{/if}
</DropdownMenu.Trigger>
</Tooltip.Trigger>
<DropdownMenu.Sub bind:open={subOpen}>
<DropdownMenu.SubTrigger class="flex cursor-pointer items-center gap-2">
{#if thinkingEnabled}
<Lightbulb class="h-4 w-4 shrink-0 text-amber-400" />
{:else}
<LightbulbOff class="h-4 w-4 shrink-0 text-muted-foreground" />
{/if}
<Tooltip.Content>
<p class="capitalize">{tooltipText}</p>
</Tooltip.Content>
</Tooltip.Root>
<span class="text-sm inline-flex gap-2 {!thinkingEnabled ? 'text-muted-foreground' : ''}">
Reasoning
<DropdownMenu.Content
align="start"
class="w-60 rounded-xl bg-popover p-3 text-popover-foreground shadow-md outline-none"
<span class="capitalize text-muted-foreground">
{thinkingEnabled ? currentEffort : 'off'}
</span>
</span>
</DropdownMenu.SubTrigger>
<DropdownMenu.SubContent
class="w-60 bg-popover p-1.5 text-popover-foreground shadow-md outline-none"
>
<div class="mb-2 px-2.5 text-sm font-medium">Reasoning effort</div>
{#each REASONING_EFFORT_LEVELS as level (level.value)}
<button
type="button"
class="flex w-full cursor-pointer items-center gap-2 rounded-lg px-2.5 py-2 text-left text-sm transition-colors hover:bg-accent"
class="flex w-full cursor-pointer items-center gap-3 rounded-md px-2 py-1.75 text-left text-sm transition-colors hover:bg-accent"
class:bg-accent={isSelected(level)}
onclick={() => handleSelection(level)}
>
@@ -140,6 +123,6 @@
{/if}
</button>
{/each}
</DropdownMenu.Content>
</DropdownMenu.Root>
</DropdownMenu.SubContent>
</DropdownMenu.Sub>
{/if}
@@ -7,14 +7,20 @@
ChatFormActionModels,
ChatFormActionRecord,
ChatFormActionSubmit,
ChatFormReasoningToggle
ChatFormContextGauge
} from '$lib/components/app';
import { FileTypeCategory } from '$lib/enums';
import { FileTypeCategory, MessageRole } from '$lib/enums';
import { mcpStore } from '$lib/stores/mcp.svelte';
import { config } from '$lib/stores/settings.svelte';
import { conversationsStore } from '$lib/stores/conversations.svelte';
import { activeMessages, conversationsStore } from '$lib/stores/conversations.svelte';
import {
activeProcessingState,
isChatStreaming,
isLoading as chatIsLoading
} from '$lib/stores/chat.svelte';
import { getFileTypeCategory } from '$lib/utils';
import { goto } from '$app/navigation';
import { page } from '$app/state';
import { ROUTES } from '$lib/constants/routes';
interface Props {
@@ -93,6 +99,36 @@
let activeMessage = $derived(
conversationsStore.activeMessages[conversationsStore.activeMessages.length - 1]
);
let hasProcessedTokens = $derived.by(() => {
if (!page.params.id) return false;
const messages = activeMessages() as DatabaseMessage[];
let totalHistoricalTokens = 0;
for (const m of messages) {
if (m.role !== MessageRole.ASSISTANT) continue;
const timings = m.timings;
if (!timings) continue;
const agenticLlm = timings.agentic?.llm;
if (agenticLlm?.prompt_n != null || agenticLlm?.predicted_n != null) {
totalHistoricalTokens += (agenticLlm?.prompt_n ?? 0) + (agenticLlm?.predicted_n ?? 0);
} else {
totalHistoricalTokens += (timings.prompt_n ?? 0) + (timings.predicted_n ?? 0);
}
}
if (totalHistoricalTokens > 0) return true;
if (!chatIsLoading() && !isChatStreaming()) return false;
const processingState = activeProcessingState();
if (!processingState) return false;
const livePromptTokens = Math.max(
processingState.promptTokens ?? 0,
processingState.promptProgress?.processed ?? 0
);
const liveOutputTokens = processingState.outputTokensUsed ?? 0;
return livePromptTokens > 0 || liveOutputTokens > 0;
});
</script>
<div
@@ -100,7 +136,7 @@
style="container-type: inline-size"
>
{#if showAddButton}
<div class="mr-auto flex items-center gap-3">
<div class="mr-auto flex items-center gap-2">
<ChatFormActionsAdd
{disabled}
{hasAudioModality}
@@ -117,8 +153,10 @@
</div>
{/if}
<div class="flex items-center gap-2">
<ChatFormReasoningToggle />
<div class="flex items-center gap-1.5">
{#if hasProcessedTokens}
<ChatFormContextGauge />
{/if}
{#if showModelSelector}
<ChatFormActionModels
@@ -6,6 +6,7 @@
import { REASONING_EFFORT_TOKENS } from '$lib/constants/reasoning-effort-tokens';
import { REASONING_EFFORT_LEVELS } from '$lib/constants/reasoning-effort';
import type { ReasoningEffortLevel } from '$lib/types';
import { DIALOG_SUBMENU_CONTENT } from '$lib/constants/css-classes';
import {
modelsStore,
checkModelSupportsThinking,
@@ -71,9 +72,7 @@
{#if modelSupportsThinking}
<DropdownMenu.Sub bind:open={subOpen}>
<DropdownMenu.SubTrigger
class="flex cursor-pointer items-center gap-2 rounded-md px-2.5 py-1.5 text-sm transition-colors outline-none hover:bg-accent focus:bg-accent"
>
<DropdownMenu.SubTrigger class="flex cursor-pointer items-center gap-2">
{#if thinkingEnabled}
<Lightbulb class="h-4 w-4 shrink-0 text-amber-400" />
{:else}
@@ -89,23 +88,15 @@
{/if}
</DropdownMenu.SubTrigger>
<DropdownMenu.SubContent
class="w-60 rounded-xl bg-popover p-3 text-popover-foreground shadow-md outline-none data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:zoom-out-95 data-[state=open]:animate-in data-[state=open]:fade-in-0 data-[state=open]:zoom-in-95"
>
<DropdownMenu.SubContent class={DIALOG_SUBMENU_CONTENT}>
{#each REASONING_EFFORT_LEVELS as level (level.value)}
<button
type="button"
class="flex w-full cursor-pointer items-center gap-2 rounded-lg px-2.5 py-2 text-left text-sm transition-colors hover:bg-accent"
class="flex w-full cursor-pointer items-center gap-2"
class:bg-accent={isSelected(level)}
onclick={() => handleSelection(level)}
>
{#if isSelected(level)}
<Check class="h-4 w-4 shrink-0 text-foreground" />
{:else}
<div class="h-4 w-4 shrink-0"></div>
{/if}
<span class="flex-1">{level.label}</span>
<span class="flex-1 text-left">{level.label}</span>
{#if !level.isOff}
<span class="text-[11px] text-muted-foreground opacity-60">
@@ -125,6 +116,10 @@
</Tooltip.Content>
</Tooltip.Root>
{/if}
{#if isSelected(level)}
<Check class="h-4 w-4 shrink-0 text-foreground" />
{/if}
</button>
{/each}
</DropdownMenu.SubContent>
@@ -0,0 +1,108 @@
<script lang="ts">
import { untrack } from 'svelte';
import * as HoverCard from '$lib/components/ui/hover-card';
import { activeConversation, activeMessages } from '$lib/stores/conversations.svelte';
import { chatStore, isChatStreaming, isLoading } from '$lib/stores/chat.svelte';
import { formatParameters } from '$lib/utils/formatters';
import { useContextGauge } from '$lib/hooks/use-context-gauge.svelte';
import ContextGaugeDial from './ContextGaugeDial.svelte';
import ContextGaugeDetails from './ContextGaugeDetails.svelte';
import ContextGaugeLoadModel from './ContextGaugeLoadModel.svelte';
import { colorLevelBgClass, colorLevelTextClass } from './context-gauge';
const gauge = useContextGauge();
$effect(() => {
const conv = activeConversation();
untrack(() => chatStore.setActiveProcessingConversation(conv?.id ?? null));
});
$effect(() => {
const conv = activeConversation();
const messages = activeMessages() as DatabaseMessage[];
if (!conv) return;
if (isLoading() || isChatStreaming()) return;
if (messages.length === 0) {
untrack(() => chatStore.clearProcessingState(conv.id));
return;
}
untrack(() => chatStore.restoreProcessingStateFromMessages(messages, conv.id));
});
$effect(() => {
gauge.startMonitoring();
});
const showProgressBar = $derived(
gauge.contextTotal !== null &&
gauge.contextTotal > 0 &&
(gauge.activeModelId !== null || gauge.isActiveModelLoaded)
);
</script>
<HoverCard.Root>
<HoverCard.Trigger class="flex h-5 w-5 cursor-default items-center justify-center">
<ContextGaugeDial percent={gauge.contextPercent} level={gauge.colorLevel} />
</HoverCard.Trigger>
<HoverCard.Content
side="bottom"
class="z-50 w-64 rounded-lg border border-border/50 bg-popover p-3 text-popover-foreground shadow-lg"
>
<div class="flex flex-col gap-2">
<div class="flex items-center gap-2">
<span class="font-medium">Context</span>
<span class="text-muted-foreground">·</span>
<span class="font-mono text-muted-foreground">
{formatParameters(gauge.contextUsed)}
/ {gauge.contextTotal !== null ? formatParameters(gauge.contextTotal) : '-'}
</span>
</div>
{#if gauge.activeModelId !== null && !gauge.isActiveModelLoaded}
<ContextGaugeLoadModel
modelId={gauge.activeModelId}
isLoading={gauge.isActiveModelLoading}
onLoad={gauge.loadModel}
/>
{:else if showProgressBar}
<div class="h-1.5 w-full overflow-hidden rounded-full bg-muted">
<div
class="h-full rounded-full transition-all duration-300 {colorLevelBgClass(
gauge.colorLevel
)}"
style="width: {gauge.contextPercent}%"
></div>
</div>
<div class="flex justify-between text-xs text-muted-foreground">
<span>
<span class={colorLevelTextClass(gauge.colorLevel)}>{gauge.contextPercent}%</span> used
</span>
<span>
{formatParameters((gauge.contextTotal ?? 0) - gauge.contextUsed)} remaining
</span>
</div>
{:else}
<div class="text-xs text-muted-foreground">No context info available</div>
{/if}
{#if gauge.hasAnyUsage}
<ContextGaugeDetails
currentRead={gauge.currentRead}
currentFresh={gauge.currentFresh}
currentCache={gauge.currentCache}
currentOutput={gauge.currentOutput}
kvTotal={gauge.kvTotal}
cumulativeRead={gauge.cumulativeRead}
cumulativeOutput={gauge.cumulativeOutput}
cumulativeCacheTotal={gauge.cumulativeCacheTotal}
averageTokensPerSecond={gauge.averageTokensPerSecond}
transientDetails={gauge.transientDetails}
/>
{/if}
</div>
</HoverCard.Content>
</HoverCard.Root>
@@ -0,0 +1,20 @@
<script lang="ts">
interface Props {
label: string;
value: string;
subtitle?: string;
}
let { label, value, subtitle }: Props = $props();
</script>
<div class="grid gap-1.5">
<div class="flex items-baseline justify-between">
<span class="text-muted-foreground">{label}</span>
<span class="font-mono text-muted-foreground">{value}</span>
</div>
{#if subtitle}
<div class="text-[10px] leading-tight text-muted-foreground/70">{subtitle}</div>
{/if}
</div>
@@ -0,0 +1,122 @@
<script lang="ts">
import { ChevronDown } from '@lucide/svelte';
import * as Collapsible from '$lib/components/ui/collapsible';
import { STATS_UNITS } from '$lib/constants';
import ContextGaugeDetailRow from './ContextGaugeDetailRow.svelte';
interface Props {
currentRead: number;
currentFresh: number;
currentCache: number;
currentOutput: number;
kvTotal: number;
cumulativeRead: number;
cumulativeOutput: number;
cumulativeCacheTotal: number;
averageTokensPerSecond: number | null;
transientDetails: string[];
}
let {
currentRead,
currentFresh,
currentCache,
currentOutput,
kvTotal,
cumulativeRead,
cumulativeOutput,
cumulativeCacheTotal,
averageTokensPerSecond,
transientDetails
}: Props = $props();
let open = $state(false);
const hasCumulative = $derived(cumulativeRead > 0 || cumulativeOutput > 0);
const hasCurrent = $derived(currentRead > 0 || currentOutput > 0);
</script>
<Collapsible.Root bind:open class="mt-3 border-t border-border/50 pt-4">
<Collapsible.Trigger
class="flex w-full cursor-pointer items-center gap-1 text-xs text-muted-foreground hover:text-foreground"
>
<span>Token usage details</span>
<ChevronDown class={'ml-auto h-3 w-3 transition-transform' + (open ? ' rotate-180' : '')} />
</Collapsible.Trigger>
<Collapsible.Content class="flex flex-col gap-4 text-xs pt-4">
{#if hasCumulative}
<div>
<h3 class="text-[11px] font-medium uppercase tracking-wide text-muted-foreground/70 mb-2">
Across all turns
</h3>
<div class="flex flex-col gap-2">
{#if cumulativeRead > 0}
<ContextGaugeDetailRow
label="Prompt tokens evaluated"
value={`${cumulativeRead.toLocaleString()} tok`}
subtitle={cumulativeCacheTotal > 0
? `${cumulativeCacheTotal.toLocaleString()} reused from KV cache`
: undefined}
/>
{/if}
{#if cumulativeOutput > 0}
<ContextGaugeDetailRow
label="Tokens generated"
value={`${cumulativeOutput.toLocaleString()} tok`}
/>
{/if}
</div>
</div>
{/if}
{#if hasCurrent}
<div>
<h3 class="text-[11px] font-medium uppercase tracking-wide text-muted-foreground/70 mb-2">
This turn · KV cache
</h3>
<div class="flex flex-col gap-2">
{#if currentRead > 0}
<ContextGaugeDetailRow
label="Prompt"
value={`${currentRead.toLocaleString()} tok`}
subtitle={currentCache > 0
? `${currentFresh.toLocaleString()} fresh + ${currentCache.toLocaleString()} cached`
: undefined}
/>
{/if}
{#if currentOutput > 0}
<ContextGaugeDetailRow
label="Generated"
value={`${currentOutput.toLocaleString()} tok`}
/>
{/if}
<div class="pt-1 mt-0.5 border-t border-border/30">
<div class="flex justify-between">
<span class="text-muted-foreground">KV cache total</span>
<span class="font-mono font-medium">{kvTotal.toLocaleString()} tok</span>
</div>
</div>
</div>
</div>
{/if}
{#if averageTokensPerSecond !== null}
<div class="pt-1.5 mt-1 border-t border-border/30">
<ContextGaugeDetailRow
label="Avg speed"
value={`${averageTokensPerSecond.toFixed(1)}${STATS_UNITS.TOKENS_PER_SECOND}`}
/>
</div>
{/if}
{#each transientDetails as detail (detail)}
<div class="font-mono text-muted-foreground">{detail}</div>
{/each}
</Collapsible.Content>
</Collapsible.Root>
@@ -0,0 +1,43 @@
<script lang="ts">
import type { ColorLevel } from './context-gauge';
import { colorLevelTextClass } from './context-gauge';
interface Props {
percent: number | null;
level: ColorLevel;
size?: 'sm' | 'md';
}
let { percent, level, size = 'sm' }: Props = $props();
const RADIUS = 11;
const CIRCUMFERENCE = 2 * Math.PI * RADIUS;
const strokeLevelClass = $derived(colorLevelTextClass(level));
const dimensions = $derived(size === 'md' ? 'h-6 w-6' : 'h-5 w-5');
const strokeWidth = $derived(size === 'md' ? 4 : 3);
</script>
<svg viewBox="0 0 32 32" fill="none" class={dimensions}>
<circle
cx="16"
cy="16"
r={RADIUS}
stroke="currentColor"
stroke-opacity="0.1"
stroke-width={strokeWidth}
/>
<circle
cx="16"
cy="16"
r={RADIUS}
class="transition-colors duration-300 {strokeLevelClass}"
stroke="currentColor"
stroke-width={strokeWidth}
stroke-linecap="round"
stroke-dasharray={CIRCUMFERENCE}
stroke-dashoffset={percent !== null ? CIRCUMFERENCE * (1 - percent / 100) : CIRCUMFERENCE}
transform="rotate(-90 16 16)"
/>
</svg>
@@ -0,0 +1,24 @@
<script lang="ts">
import { Loader2 } from '@lucide/svelte';
import { Button } from '$lib/components/ui/button';
interface Props {
modelId: string | null;
isLoading: boolean;
onLoad: () => void;
}
let { modelId, isLoading, onLoad }: Props = $props();
</script>
{#if modelId !== null && !isLoading}
<div class="flex flex-col gap-2 border-t border-border/50 pt-2 text-xs text-muted-foreground">
<span>Available context size is only visible once the model is loaded.</span>
<Button size="sm" variant="secondary" class="self-start" onclick={onLoad}>Load model</Button>
</div>
{:else if isLoading}
<div class="flex items-center gap-2 border-t border-border/50 pt-2 text-xs text-muted-foreground">
<Loader2 class="h-3.5 w-3.5 animate-spin" />
<span>Loading model...</span>
</div>
{/if}
@@ -0,0 +1,37 @@
export type ColorLevel = 'ok' | 'warning' | 'critical' | 'neutral';
const WARNING_THRESHOLD = 80;
const CRITICAL_THRESHOLD = 95;
export function colorLevelFromPercent(percent: number | null): ColorLevel {
if (percent === null) return 'neutral';
if (percent >= CRITICAL_THRESHOLD) return 'critical';
if (percent >= WARNING_THRESHOLD) return 'warning';
return 'ok';
}
export function colorLevelTextClass(level: ColorLevel): string {
switch (level) {
case 'critical':
return 'text-red-400';
case 'warning':
return 'text-amber-400';
case 'ok':
return 'text-muted-foreground';
default:
return 'text-muted-foreground';
}
}
export function colorLevelBgClass(level: ColorLevel): string {
switch (level) {
case 'critical':
return 'bg-red-500';
case 'warning':
return 'bg-amber-500';
case 'ok':
return 'bg-green-500';
default:
return 'bg-muted';
}
}
@@ -24,6 +24,8 @@
message: DatabaseMessage;
toolMessages?: DatabaseMessage[];
isLastAssistantMessage?: boolean;
isLastUserMessage?: boolean;
nextAssistantMessage?: DatabaseMessage | null;
siblingInfo?: ChatMessageSiblingInfo | null;
}
@@ -32,6 +34,8 @@
message,
toolMessages = [],
isLastAssistantMessage = false,
isLastUserMessage = false,
nextAssistantMessage = null,
siblingInfo = null
}: Props = $props();
@@ -359,7 +363,9 @@
<ChatMessageUser
class={className}
{deletionInfo}
{isLastUserMessage}
{message}
{nextAssistantMessage}
onConfirmDelete={handleConfirmDelete}
onCopy={handleCopy}
onDelete={handleDelete}
@@ -11,11 +11,10 @@
import { useProcessingState } from '$lib/hooks/use-processing-state.svelte';
import { isLoading, isChatStreaming } from '$lib/stores/chat.svelte';
import { copyToClipboard, deriveAgenticSections, modelLoadProgressText } from '$lib/utils';
import { AgenticSectionType } from '$lib/enums';
import { AgenticSectionType, ChatMessageStatisticsMode } from '$lib/enums';
import { REASONING_TAGS } from '$lib/constants/agentic';
import { tick } from 'svelte';
import { fade } from 'svelte/transition';
import { MessageRole, ChatMessageStatsView } from '$lib/enums';
import { MessageRole } from '$lib/enums';
import { config } from '$lib/stores/settings.svelte';
import { isRouterMode } from '$lib/stores/server.svelte';
import { modelsStore } from '$lib/stores/models.svelte';
@@ -122,62 +121,6 @@
return parts.join('\n\n\n');
});
let activeStatsView = $state<ChatMessageStatsView>(ChatMessageStatsView.GENERATION);
let statsContainerEl: HTMLDivElement | undefined = $state();
function getScrollParent(el: HTMLElement): HTMLElement | null {
let parent = el.parentElement;
while (parent) {
const style = getComputedStyle(parent);
if (/(auto|scroll)/.test(style.overflowY)) {
return parent;
}
parent = parent.parentElement;
}
return null;
}
async function handleStatsViewChange(view: ChatMessageStatsView) {
const el = statsContainerEl;
if (!el) {
activeStatsView = view;
return;
}
const scrollParent = getScrollParent(el);
if (!scrollParent) {
activeStatsView = view;
return;
}
const yBefore = el.getBoundingClientRect().top;
activeStatsView = view;
await tick();
const delta = el.getBoundingClientRect().top - yBefore;
if (delta !== 0) {
scrollParent.scrollTop += delta;
}
// Correct any drift after browser paint
requestAnimationFrame(() => {
const drift = el.getBoundingClientRect().top - yBefore;
if (Math.abs(drift) > 1) {
scrollParent.scrollTop += drift;
}
});
}
let highlightAgenticTurns = $derived(
isAgentic &&
(currentConfig.alwaysShowAgenticTurns || activeStatsView === ChatMessageStatsView.SUMMARY)
);
let displayedModel = $derived(message.model ?? null);
// model being switched to while it loads, so the selector bar tracks it
@@ -291,7 +234,6 @@
{toolMessages}
isStreaming={isChatStreaming()}
{isLastAssistantMessage}
highlightTurns={highlightAgenticTurns}
/>
{/if}
{:else}
@@ -315,10 +257,7 @@
<div class="info my-6 grid gap-4 tabular-nums">
{#if displayedModel}
<div
bind:this={statsContainerEl}
class="inline-flex flex-wrap items-start gap-2 text-xs text-muted-foreground"
>
<div class="inline-flex flex-wrap items-start gap-2 text-xs text-muted-foreground">
{#if isRouter}
<ModelsSelectorDropdown
currentModel={pendingModel ?? displayedModel}
@@ -347,28 +286,25 @@
{#if currentConfig.showMessageStats && message.timings && message.timings.predicted_n && message.timings.predicted_ms}
{@const agentic = message.timings.agentic}
<ChatMessageStatistics
mode={ChatMessageStatisticsMode.GENERATION}
promptTokens={agentic ? agentic.llm.prompt_n : message.timings.prompt_n}
promptMs={agentic ? agentic.llm.prompt_ms : message.timings.prompt_ms}
predictedTokens={agentic ? agentic.llm.predicted_n : message.timings.predicted_n}
predictedMs={agentic ? agentic.llm.predicted_ms : message.timings.predicted_ms}
agenticTimings={agentic}
onActiveViewChange={handleStatsViewChange}
/>
{:else if isLoading() && currentConfig.showMessageStats}
{@const liveStats = processingState.getLiveProcessingStats()}
{@const genStats = processingState.getLiveGenerationStats()}
{@const promptProgress = processingState.processingState?.promptProgress}
{@const isStillProcessingPrompt =
promptProgress && promptProgress.processed < promptProgress.total}
{#if liveStats || genStats}
{#if genStats}
<ChatMessageStatistics
mode={ChatMessageStatisticsMode.GENERATION}
isLive
isProcessingPrompt={!!isStillProcessingPrompt}
promptTokens={liveStats?.tokensProcessed}
promptMs={liveStats?.timeMs}
predictedTokens={genStats?.tokensGenerated}
predictedMs={genStats?.timeMs}
predictedTokens={genStats.tokensGenerated}
predictedMs={genStats.timeMs}
/>
{/if}
{/if}
@@ -2,10 +2,14 @@
import {
ChatMessageActionIcons,
ChatMessageEditForm,
ChatMessageStatistics,
ChatMessageUserBubble
} from '$lib/components/app/chat';
import { getMessageEditContext } from '$lib/contexts';
import { MessageRole } from '$lib/enums';
import { useProcessingState } from '$lib/hooks/use-processing-state.svelte';
import { isLoading } from '$lib/stores/chat.svelte';
import { MessageRole, ChatMessageStatisticsMode } from '$lib/enums';
import { config } from '$lib/stores/settings.svelte';
interface Props {
class?: string;
@@ -17,6 +21,8 @@
assistantMessages: number;
messageTypes: string[];
} | null;
isLastUserMessage?: boolean;
nextAssistantMessage?: DatabaseMessage | null;
showDeleteDialog: boolean;
onEdit: () => void;
onDelete: () => void;
@@ -32,6 +38,8 @@
message,
siblingInfo = null,
deletionInfo,
isLastUserMessage = false,
nextAssistantMessage = null,
showDeleteDialog,
onEdit,
onDelete,
@@ -44,6 +52,37 @@
// Get contexts
const editCtx = getMessageEditContext();
const processingState = useProcessingState();
const currentConfig = $derived(config());
const isActivelyProcessing = $derived(isLastUserMessage && isLoading());
// For agentic turns, prefer the cumulative agentic.llm totals over per-call timings.
let storedReadingStats = $derived.by(() => {
const timings = nextAssistantMessage?.timings;
if (!timings?.prompt_n || !timings?.prompt_ms) return null;
const agentic = timings.agentic;
return {
promptTokens: agentic ? agentic.llm.prompt_n : timings.prompt_n,
promptMs: agentic ? agentic.llm.prompt_ms : timings.prompt_ms
};
});
let showStoredReadingStats = $derived(
Boolean(currentConfig.showMessageStats) && storedReadingStats !== null
);
let showLiveReadingStats = $derived(
Boolean(currentConfig.showMessageStats) && isActivelyProcessing && storedReadingStats === null
);
$effect(() => {
if (showLiveReadingStats) {
processingState.startMonitoring();
}
});
</script>
<div
@@ -60,6 +99,37 @@
renderMarkdown={true}
/>
{#if showStoredReadingStats}
<!-- Reading stats sourced from the assistant message that followed this turn -->
<div class="info my-2 grid w-full justify-items-end gap-4 tabular-nums">
<div
class="inline-flex flex-wrap items-start justify-end gap-2 text-xs text-muted-foreground"
>
<ChatMessageStatistics
mode={ChatMessageStatisticsMode.READING}
promptTokens={storedReadingStats!.promptTokens}
promptMs={storedReadingStats!.promptMs}
/>
</div>
</div>
{:else if showLiveReadingStats}
{@const liveStats = processingState.getLiveProcessingStats()}
{#if liveStats}
<div class="info my-2 grid w-full justify-items-end gap-4 tabular-nums">
<div
class="inline-flex flex-wrap items-start justify-end gap-2 text-xs text-muted-foreground"
>
<ChatMessageStatistics
mode={ChatMessageStatisticsMode.READING}
isLive
promptTokens={liveStats.tokensProcessed}
promptMs={liveStats.timeMs}
/>
</div>
</div>
{/if}
{/if}
{#if message.timestamp}
<div class="max-w-[80%]">
<ChatMessageActionIcons
@@ -2,7 +2,6 @@
import { ActionIcon, ChatMessageEditForm, ChatMessageUserBubble } from '$lib/components/app';
import { fadeInView } from '$lib/actions/fade-in-view.svelte';
import { ArrowUp, Edit, Trash2 } from '@lucide/svelte';
import { getProcessingInfoContext } from '$lib/contexts';
import { useMessageEditContext } from '$lib/hooks/use-message-edit-context.svelte';
interface Props {
@@ -23,9 +22,6 @@
onDelete
}: Props = $props();
const processingInfoCtx = getProcessingInfoContext();
let showProcessingInfo = $derived(processingInfoCtx.showProcessingInfo);
const editCtx = useMessageEditContext({
getContent: () => content,
getExtras: () => extras,
@@ -36,9 +32,7 @@
<div
use:fadeInView
aria-label="Pending user message"
class="group flex flex-col items-end gap-3 transition-opacity hover:opacity-80 md:gap-2 {className} sticky {showProcessingInfo
? 'bottom-44'
: 'bottom-32'}"
class="group flex flex-col items-end gap-3 transition-opacity hover:opacity-80 md:gap-2 {className} sticky bottom-32"
role="group"
>
{#if editCtx.isEditing}
@@ -41,15 +41,13 @@
toolMessages?: DatabaseMessage[];
isStreaming?: boolean;
isLastAssistantMessage?: boolean;
highlightTurns?: boolean;
}
let {
message,
toolMessages = [],
isStreaming = false,
isLastAssistantMessage = false,
highlightTurns = false
isLastAssistantMessage = false
}: Props = $props();
let expandedStates: Record<number, boolean> = $state({});
@@ -57,6 +55,7 @@
const showToolCallInProgress = $derived(config().showToolCallInProgress as boolean);
const showThoughtInProgress = $derived(config().showThoughtInProgress as boolean);
const renderThinkingAsMarkdown = $derived(config().renderThinkingAsMarkdown as boolean);
const showMessageStats = $derived(config().showMessageStats as boolean);
const hasReasoningError = $derived(
isLastAssistantMessage ? !!agenticLastError(message.convId) : false
@@ -354,16 +353,17 @@
{/snippet}
<div class="agentic-content">
{#if highlightTurns && turnGroups.length > 1}
{#if turnGroups.length > 1}
{#each turnGroups as turn, turnIndex (turnIndex)}
{@const turnStats = message?.timings?.agentic?.perTurn?.[turnIndex]}
<div class="agentic-turn my-2 hover:bg-muted/80 dark:hover:bg-muted/30">
<span class="agentic-turn-label">Turn {turnIndex + 1}</span>
<div class="agentic-turn group/turn grid gap-3 mb-4">
{#each turn.sections as section, sIdx (turn.flatIndices[sIdx])}
{@render renderSection(section, turn.flatIndices[sIdx])}
{/each}
{#if turnStats}
<div class="turn-stats">
{#if turnStats && showMessageStats}
<div class="turn-stats transition-opacity duration-150">
<ChatMessageStatistics
promptTokens={turnStats.llm.prompt_n}
promptMs={turnStats.llm.prompt_ms}
@@ -402,39 +402,21 @@
.agentic-content {
display: flex;
flex-direction: column;
gap: 0.5rem;
width: 100%;
max-width: 48rem;
gap: 1rem;
}
.agentic-content > :global(*),
.agentic-turn > :global(*) {
min-width: 0;
}
.agentic-text {
width: 100%;
}
.agentic-turn {
position: relative;
border: 1.5px dashed var(--muted-foreground);
border-radius: 0.75rem;
padding: 1rem;
transition: background 0.1s;
}
.agentic-turn-label {
position: absolute;
top: -1rem;
left: 0.75rem;
padding: 0 0.375rem;
background: var(--background);
font-size: 0.7rem;
font-weight: 500;
color: var(--muted-foreground);
text-transform: uppercase;
letter-spacing: 0.05em;
}
.turn-stats {
margin-top: 0.75rem;
padding-top: 0.5rem;
border-top: 1px solid hsl(var(--muted) / 0.5);
}
</style>
@@ -2,7 +2,7 @@
import { Clock, Gauge, WholeWord, BookOpenText, Sparkles, Wrench, Layers } from '@lucide/svelte';
import { ChatMessageStatisticsBadge } from '$lib/components/app';
import * as Tooltip from '$lib/components/ui/tooltip';
import { ChatMessageStatsView } from '$lib/enums';
import { ChatMessageStatsView, ChatMessageStatisticsMode } from '$lib/enums';
import type { ChatMessageAgenticTimings } from '$lib/types/chat';
import { formatPerformanceTime } from '$lib/utils';
import { MS_PER_SECOND, DEFAULT_PERFORMANCE_TIME } from '$lib/constants';
@@ -19,6 +19,7 @@
agenticTimings?: ChatMessageAgenticTimings;
onActiveViewChange?: (view: ChatMessageStatsView) => void;
hideSummary?: boolean;
mode?: ChatMessageStatisticsMode;
}
let {
@@ -31,19 +32,30 @@
initialView = ChatMessageStatsView.GENERATION,
agenticTimings,
onActiveViewChange,
hideSummary = false
hideSummary = false,
mode = ChatMessageStatisticsMode.SWITCHABLE
}: Props = $props();
let activeView: ChatMessageStatsView = $derived(initialView);
let isSwitchable = $derived(mode === ChatMessageStatisticsMode.SWITCHABLE);
let activeView: ChatMessageStatsView = $derived(
mode === ChatMessageStatisticsMode.READING
? ChatMessageStatsView.READING
: mode === ChatMessageStatisticsMode.GENERATION
? ChatMessageStatsView.GENERATION
: initialView
);
let hasAutoSwitchedToGeneration = $state(false);
$effect(() => {
onActiveViewChange?.(activeView);
if (isSwitchable) {
onActiveViewChange?.(activeView);
}
});
// In live mode: auto-switch to GENERATION tab when prompt processing completes
$effect(() => {
if (isLive) {
if (isLive && isSwitchable) {
// Auto-switch to generation tab only when prompt processing is done (once)
if (
!hasAutoSwitchedToGeneration &&
@@ -91,8 +103,7 @@
formattedPromptTime !== undefined
);
// In live mode, generation tab is disabled until we have generation stats
let isGenerationDisabled = $derived(isLive && !hasGenerationStats);
let isGenerationDisabled = $derived(isLive && isSwitchable && !hasGenerationStats);
let hasAgenticStats = $derived(agenticTimings !== undefined && agenticTimings.toolCallsCount > 0);
@@ -153,44 +164,44 @@
{/snippet}
<div class="inline-flex items-center text-xs text-muted-foreground">
<div class="inline-flex items-center rounded-sm bg-muted-foreground/15 p-0.5">
{#if hasPromptStats || isLive}
{@render viewButton({
view: ChatMessageStatsView.READING,
icon: BookOpenText,
label: 'Reading',
tooltipText: 'Reading (prompt processing)'
})}
{/if}
{@render viewButton({
view: ChatMessageStatsView.GENERATION,
icon: Sparkles,
label: 'Generation',
tooltipText: isGenerationDisabled
? 'Generation (waiting for tokens...)'
: 'Generation (token output)',
disabled: isGenerationDisabled
})}
{#if hasAgenticStats}
{@render viewButton({
view: ChatMessageStatsView.TOOLS,
icon: Wrench,
label: 'Tools',
tooltipText: 'Tool calls'
})}
{#if !hideSummary}
{#if isSwitchable}
<div class="inline-flex items-center rounded-sm bg-muted-foreground/15 p-0.5">
{#if hasPromptStats || isLive}
{@render viewButton({
view: ChatMessageStatsView.SUMMARY,
icon: Layers,
label: 'Summary',
tooltipText: 'Agentic summary'
view: ChatMessageStatsView.READING,
icon: BookOpenText,
label: 'Reading',
tooltipText: 'Processing'
})}
{/if}
{/if}
</div>
{@render viewButton({
view: ChatMessageStatsView.GENERATION,
icon: Sparkles,
label: 'Generation',
tooltipText: isGenerationDisabled ? 'Waiting for tokens...' : 'Generation',
disabled: isGenerationDisabled
})}
{#if hasAgenticStats}
{@render viewButton({
view: ChatMessageStatsView.TOOLS,
icon: Wrench,
label: 'Tools',
tooltipText: 'Tool calls'
})}
{#if !hideSummary}
{@render viewButton({
view: ChatMessageStatsView.SUMMARY,
icon: Layers,
label: 'Summary',
tooltipText: 'Agentic summary'
})}
{/if}
{/if}
</div>
{/if}
<div class="flex items-center gap-1 px-2">
{#if activeView === ChatMessageStatsView.GENERATION && hasGenerationStats}
@@ -256,7 +267,7 @@
value={formattedAgenticTotalTime}
tooltipLabel="Total time (LLM + tools)"
/>
{:else if hasPromptStats}
{:else if hasPromptStats && (mode === ChatMessageStatisticsMode.READING || isSwitchable)}
<ChatMessageStatisticsBadge
class="bg-transparent"
icon={WholeWord}
@@ -186,6 +186,8 @@
message: DatabaseMessage;
toolMessages: DatabaseMessage[];
isLastAssistantMessage: boolean;
isLastUserMessage: boolean;
nextAssistantMessage: DatabaseMessage | null;
siblingInfo: ChatMessageSiblingInfo;
}> = [];
@@ -236,18 +238,36 @@
message: msg,
toolMessages,
isLastAssistantMessage: false,
isLastUserMessage: false,
nextAssistantMessage: null,
siblingInfo
});
}
// Mark the last assistant message
let lastAssistantIdx = -1;
for (let i = result.length - 1; i >= 0; i--) {
if (result[i].message.role === MessageRole.ASSISTANT) {
result[i].isLastAssistantMessage = true;
lastAssistantIdx = i;
break;
}
}
if (lastAssistantIdx > 0 && result[lastAssistantIdx - 1].message.role === MessageRole.USER) {
result[lastAssistantIdx - 1].isLastUserMessage = true;
}
for (let i = 0; i < result.length; i++) {
if (result[i].message.role !== MessageRole.USER) continue;
for (let j = i + 1; j < result.length; j++) {
if (result[j].message.role === MessageRole.ASSISTANT) {
result[i].nextAssistantMessage = result[j].message;
break;
}
}
}
return result;
});
</script>
@@ -257,12 +277,14 @@
{isVisible ? 'opacity-100' : 'opacity-0'}
{previousRouteId === '/(chat)/chat/[id]' ? '' : 'delay-300'}"
>
{#each displayMessages as { message, toolMessages, isLastAssistantMessage, siblingInfo } (message.id)}
{#each displayMessages as { message, toolMessages, isLastAssistantMessage, isLastUserMessage, nextAssistantMessage, siblingInfo } (message.id)}
<ChatMessage
class="mx-auto mt-12 w-full max-w-3xl"
{message}
{toolMessages}
{isLastAssistantMessage}
{isLastUserMessage}
{nextAssistantMessage}
{siblingInfo}
/>
{/each}
@@ -4,12 +4,10 @@
ChatScreenForm,
ChatMessages,
ChatScreenDragOverlay,
ChatScreenProcessingInfo,
ChatScreenStreamResumeStatus,
ServerLoadingSplash,
ChatScreenServerError
} from '$lib/components/app';
import { setProcessingInfoContext } from '$lib/contexts';
import { createAutoScrollController } from '$lib/hooks/use-auto-scroll.svelte';
import { useChatScreenActiveModel } from '$lib/hooks/use-chat-screen-active-model.svelte';
import { useChatScreenDragAndDrop } from '$lib/hooks/use-chat-screen-drag-and-drop.svelte';
@@ -23,8 +21,7 @@
errorDialog,
isLoading,
isChatStreaming,
isEditing,
activeProcessingState
isEditing
} from '$lib/stores/chat.svelte';
import {
conversationsStore,
@@ -42,12 +39,6 @@
let { showCenteredEmpty = false } = $props();
setProcessingInfoContext({
get showProcessingInfo() {
return showProcessingInfo;
}
});
let disableAutoScroll = $derived(Boolean(config().disableAutoScroll) || isMobile.current);
let isMobileUserScrolledUp = $state(false);
let mobileScrollDownHint = $state(false);
@@ -63,11 +54,6 @@
let isServerLoading = $derived(serverLoading());
let hasPropsError = $derived(!!serverError());
let isCurrentConversationLoading = $derived(isLoading() || isChatStreaming());
let showProcessingInfo = $derived(
isCurrentConversationLoading ||
(config().keepStatsVisible && !!page.params.id) ||
activeProcessingState() !== null
);
let chatFormBottomPosition = $derived.by(() => {
if (!isMobile.current) return '1rem';
if (device.isStandalone) return '1.5rem';
@@ -298,10 +284,6 @@
}}
/>
{/if}
{#if showProcessingInfo}
<ChatScreenProcessingInfo />
{/if}
</div>
<ChatScreenForm
@@ -1,127 +0,0 @@
<script lang="ts">
import { untrack } from 'svelte';
import { PROCESSING_INFO_TIMEOUT } from '$lib/constants';
import { useProcessingState } from '$lib/hooks/use-processing-state.svelte';
import { chatStore, isLoading, isChatStreaming } from '$lib/stores/chat.svelte';
import { activeMessages, activeConversation } from '$lib/stores/conversations.svelte';
import { config } from '$lib/stores/settings.svelte';
const processingState = useProcessingState();
let isCurrentConversationLoading = $derived(isLoading());
let isStreaming = $derived(isChatStreaming());
let processingDetails = $derived(processingState.getTechnicalDetails());
let processingVisible = $derived(processingDetails.length > 0);
let { onVisibilityChange }: { onVisibilityChange?: (visible: boolean) => void } = $props();
$effect(() => {
onVisibilityChange?.(processingVisible);
});
$effect(() => {
const conversation = activeConversation();
untrack(() => chatStore.setActiveProcessingConversation(conversation?.id ?? null));
});
$effect(() => {
const keepStatsVisible = config().keepStatsVisible;
const shouldMonitor = keepStatsVisible || isCurrentConversationLoading || isStreaming;
if (shouldMonitor) {
processingState.startMonitoring();
}
if (!isCurrentConversationLoading && !isStreaming && !keepStatsVisible) {
const timeout = setTimeout(() => {
if (!config().keepStatsVisible && !isChatStreaming()) {
processingState.stopMonitoring();
}
}, PROCESSING_INFO_TIMEOUT);
return () => clearTimeout(timeout);
}
});
$effect(() => {
const conversation = activeConversation();
const messages = activeMessages() as DatabaseMessage[];
const keepStatsVisible = config().keepStatsVisible;
if (keepStatsVisible && conversation) {
if (messages.length === 0) {
untrack(() => chatStore.clearProcessingState(conversation.id));
return;
}
if (!isCurrentConversationLoading && !isStreaming) {
untrack(() => chatStore.restoreProcessingStateFromMessages(messages, conversation.id));
}
}
});
</script>
<div
class={[
'chat-processing-info-container pointer-events-none relative w-full hidden md:block',
processingVisible && 'visible'
]}
>
<div class="chat-processing-info-content absolute bottom-4 left-1/2 -translate-x-1/2">
{#each processingDetails as detail (detail)}
<span class="chat-processing-info-detail pointer-events-auto backdrop-blur-sm">{detail}</span>
{/each}
</div>
</div>
<style>
.chat-processing-info-container {
position: sticky;
top: 0;
z-index: 10;
padding: 0 1rem 0.75rem;
opacity: 0;
transform: translateY(50%);
transition:
opacity 300ms ease-out,
transform 300ms ease-out;
}
.chat-processing-info-container.visible {
opacity: 1;
transform: translateY(0);
}
.chat-processing-info-content {
display: flex;
flex-wrap: wrap;
align-items: center;
gap: 1rem;
justify-content: center;
max-width: 48rem;
margin: 0 auto;
}
.chat-processing-info-detail {
color: var(--muted-foreground);
font-size: 0.75rem;
padding: 0.25rem 0.75rem;
border-radius: 0.375rem;
font-family:
ui-monospace, SFMono-Regular, 'SF Mono', Consolas, 'Liberation Mono', Menlo, monospace;
white-space: nowrap;
}
@media (max-width: 768px) {
.chat-processing-info-content {
gap: 0.5rem;
}
.chat-processing-info-detail {
font-size: 0.7rem;
padding: 0.2rem 0.5rem;
}
}
</style>
+9 -12
View File
@@ -241,13 +241,18 @@ export { default as ChatFormActionAddToolsSubmenu } from './ChatForm/ChatFormAct
export { default as ChatFormActionAddMcpServersSubmenu } from './ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddMcpServersSubmenu.svelte';
/**
* **ChatFormReasoningToggle** - Thinking toggle button with effort dropdown
* Dropdown submenu for selecting reasoning effort level.
*
* A toggle button with lightbulb icon that indicates thinking status.
* Shows the reasoning effort dropdown when clicked.
* Shows a "Reasoning" sub-menu item with a lightbulb icon indicating
* thinking status, and a nested list of effort levels.
* Only visible when the current model supports thinking.
*/
export { default as ChatFormReasoningToggle } from './ChatForm/ChatFormActions/ChatFormReasoningToggle.svelte';
export { default as ChatFormActionAddReasoningSubmenu } from './ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddReasoningSubmenu.svelte';
/**
* Compact context-usage gauge with per-turn and cumulative breakdown in the tooltip.
*/
export { default as ChatFormContextGauge } from './ChatForm/ChatFormContextGauge/ChatFormContextGauge.svelte';
/**
* Hidden file input element for programmatic file selection.
@@ -669,14 +674,6 @@ export { default as ChatScreenDragOverlay } from './ChatScreen/ChatScreenDragOve
*/
export { default as ChatScreenForm } from './ChatScreen/ChatScreenForm.svelte';
/**
* Processing info display during generation. Shows real-time statistics:
* tokens per second, prompt/completion token counts, and elapsed time.
* Data sourced from slotsService polling during active generation.
* Only visible when `isCurrentConversationLoading` is true.
*/
export { default as ChatScreenProcessingInfo } from './ChatScreen/ChatScreenProcessingInfo.svelte';
/**
* Server error alert displayed when the server is unreachable.
* Shows the error message with a retry button.
@@ -76,7 +76,7 @@
open = value;
onToggle?.();
}}
class={className}
class="{className} my-0!"
>
<Card class="gap-0 border-muted bg-muted/30 py-0">
<Collapsible.Trigger class="flex w-full cursor-pointer items-start justify-between gap-2 p-3">
@@ -72,8 +72,8 @@
</script>
<div
class="code-preview-wrapper rounded-lg border border-border bg-muted {className}"
style="max-height: {maxHeight}; max-width: {maxWidth};"
class="code-preview-wrapper min-w-0 max-w-full overflow-x-auto rounded-lg border border-border bg-muted {className}"
style="max-height: {maxHeight}; {maxWidth ? `max-width: ${maxWidth};` : ''}"
>
<!-- Needs to be formatted as single line for proper rendering -->
<pre class="m-0"><code class="hljs text-sm leading-relaxed">{@html highlightedHtml}</code></pre>
@@ -4,9 +4,10 @@
import * as Dialog from '$lib/components/ui/dialog';
import { fly } from 'svelte/transition';
import { McpServerCardCompact, McpServerForm } from '$lib/components/app/mcp';
import { RECOMMENDED_MCP_SERVERS } from '$lib/constants';
import { RECOMMENDED_MCP_SERVERS, SETTINGS_KEYS } from '$lib/constants';
import { conversationsStore } from '$lib/stores/conversations.svelte';
import { mcpStore } from '$lib/stores/mcp.svelte';
import { settingsStore } from '$lib/stores/settings.svelte';
import { uuid } from '$lib/utils';
import { MCP_SERVERS_ADDED_TO_CHAT_LOCALSTORAGE_KEY, MCP_SERVER_ID_PREFIX } from '$lib/constants';
import type { MCPServerSettingsEntry } from '$lib/types';
@@ -24,6 +25,22 @@
);
let addedServers = $state<MCPServerSettingsEntry[]>([]);
let didAddAny = $state(false);
let selectedRecommendedCount = $derived.by(
() => RECOMMENDED_MCP_SERVERS.filter((server) => selected[server.id]).length
);
let footerLabel = $derived.by(() => {
const recommended = selectedRecommendedCount;
const custom = addedServers.length;
const total = recommended + custom;
if (total === 0) return 'Continue';
if (recommended === 0) return custom === 1 ? 'Add server' : `Add ${custom} servers`;
if (custom === 0) return recommended === 1 ? 'Add server' : `Add ${recommended} servers`;
return `Add ${recommended} servers and ${custom} custom`;
});
let showAddForm = $state(false);
let newServerUrl = $state('');
@@ -44,9 +61,14 @@
showAddForm = false;
newServerUrl = '';
newServerHeaders = '';
addedServers = [];
if (!didAddAny) {
settingsStore.updateConfig(SETTINGS_KEYS.MCP_SERVERS, []);
}
localStorage.setItem(MCP_SERVERS_ADDED_TO_CHAT_LOCALSTORAGE_KEY, 'true');
addedServers = [];
didAddAny = false;
}
open = value;
onOpenChange?.(value);
@@ -59,6 +81,7 @@
}
function enableSelected() {
didAddAny = true;
localStorage.setItem(MCP_SERVERS_ADDED_TO_CHAT_LOCALSTORAGE_KEY, 'true');
for (const server of RECOMMENDED_MCP_SERVERS) {
@@ -83,6 +106,8 @@
function saveNewServer() {
if (newServerUrlError) return;
didAddAny = true;
const newServerId = uuid() ?? `${MCP_SERVER_ID_PREFIX}-${Date.now()}`;
localStorage.setItem(MCP_SERVERS_ADDED_TO_CHAT_LOCALSTORAGE_KEY, 'true');
@@ -174,7 +199,12 @@
<Dialog.Footer>
<Button variant="secondary" size="sm" onclick={() => handleOpenChange(false)}>Not now</Button>
<Button variant="default" size="sm" onclick={enableSelected}>Add selected</Button>
<Button
variant="default"
size="sm"
onclick={enableSelected}
disabled={footerLabel === 'Continue'}>{footerLabel}</Button
>
</Dialog.Footer>
</Dialog.Content>
</Dialog.Root>
@@ -39,13 +39,17 @@
{@const faviconUrl = group.serverId ? mcpStore.getServerFavicon(group.serverId) : null}
<span class="inline-flex min-w-0 items-center gap-1.5 font-medium">
<McpServerIdentity
iconClass="h-4 w-4"
iconRounded="rounded-sm"
showVersion={false}
displayName={group.label}
{faviconUrl}
/>
{#if group.source === 'mcp'}
<McpServerIdentity
iconClass="h-4 w-4"
iconRounded="rounded-sm"
showVersion={false}
displayName={group.label}
{faviconUrl}
/>
{:else}
<TruncatedText text={group.label} class="font-medium" />
{/if}
</span>
<span class="ml-auto shrink-0 text-xs text-muted-foreground">
@@ -0,0 +1,31 @@
<script lang="ts">
import { LinkPreview as HoverCardPrimitive } from 'bits-ui';
import { cn, type WithoutChildrenOrChild } from '$lib/components/ui/utils.js';
import HoverCardPortal from './hover-card-portal.svelte';
import type { ComponentProps } from 'svelte';
let {
ref = $bindable(null),
class: className,
align = 'center',
sideOffset = 4,
portalProps,
...restProps
}: HoverCardPrimitive.ContentProps & {
portalProps?: WithoutChildrenOrChild<ComponentProps<typeof HoverCardPortal>>;
} = $props();
</script>
<HoverCardPortal {...portalProps}>
<HoverCardPrimitive.Content
bind:ref
data-slot="hover-card-content"
{align}
{sideOffset}
class={cn(
'data-open:animate-in data-closed:animate-out data-closed:fade-out-0 data-open:fade-in-0 data-closed:zoom-out-95 data-open:zoom-in-95 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2 ring-foreground/10 bg-popover text-popover-foreground w-64 rounded-lg p-2.5 text-sm shadow-md ring-1 duration-100 z-50 origin-(--transform-origin) outline-hidden',
className
)}
{...restProps}
/>
</HoverCardPortal>
@@ -0,0 +1,7 @@
<script lang="ts">
import { LinkPreview as HoverCardPrimitive } from 'bits-ui';
let { ...restProps }: HoverCardPrimitive.PortalProps = $props();
</script>
<HoverCardPrimitive.Portal {...restProps} />
@@ -0,0 +1,7 @@
<script lang="ts">
import { LinkPreview as HoverCardPrimitive } from 'bits-ui';
let { ref = $bindable(null), ...restProps }: HoverCardPrimitive.TriggerProps = $props();
</script>
<HoverCardPrimitive.Trigger bind:ref data-slot="hover-card-trigger" {...restProps} />
@@ -0,0 +1,7 @@
<script lang="ts">
import { LinkPreview as HoverCardPrimitive } from 'bits-ui';
let { open = $bindable(false), ...restProps }: HoverCardPrimitive.RootProps = $props();
</script>
<HoverCardPrimitive.Root bind:open {...restProps} />
@@ -0,0 +1,15 @@
import Root from './hover-card.svelte';
import Content from './hover-card-content.svelte';
import Trigger from './hover-card-trigger.svelte';
import Portal from './hover-card-portal.svelte';
export {
Root,
Content,
Trigger,
Portal,
Root as HoverCard,
Content as HoverCardContent,
Trigger as HoverCardTrigger,
Portal as HoverCardPortal
};
@@ -1,4 +1,3 @@
export const CONTEXT_KEY_MESSAGE_EDIT = 'chat-message-edit';
export const CONTEXT_KEY_CHAT_ACTIONS = 'chat-actions';
export const CONTEXT_KEY_CHAT_SETTINGS_CONFIG = 'chat-settings-config';
export const CONTEXT_KEY_PROCESSING_INFO = 'processing-info';

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