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llama.cpp
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mpi : add support for distributed inference via MPI (#2099) 

* MPI support, first cut

* fix warnings, update README

* fixes

* wrap includes

* PR comments

* Update CMakeLists.txt

* Add GH workflow, fix test

* Add info to README

* mpi : trying to move more MPI stuff into ggml-mpi (WIP) (#2099)

* mpi : add names for layer inputs + prep ggml_mpi_graph_compute()

* mpi : move all MPI logic into ggml-mpi

Not tested yet

* mpi : various fixes - communication now works but results are wrong

* mpi : fix output tensor after MPI compute (still not working)

* mpi : fix inference

* mpi : minor

* Add OpenMPI to GH action

* [mpi] continue-on-error: true

* mpi : fix after master merge

* [mpi] Link MPI C++ libraries to fix OpenMPI

* tests : fix new llama_backend API

* [mpi] use MPI_INT32_T

* mpi : factor out recv / send in functions and reuse

* mpi : extend API to allow usage with outer backends (e.g. Metal)

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Evan Miller 2023-07-10 11:49:56 -04:00 committed by GitHub
parent 1d16309969
commit 5656d10599
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
18 changed files with 460 additions and 35 deletions
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34
.github/workflows/build.yml vendored
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@ -104,6 +104,40 @@ jobs:
cd build
ctest --verbose --timeout 900
ubuntu-latest-cmake-mpi:
runs-on: ubuntu-latest
continue-on-error: true
strategy:
matrix:
mpi_library: [mpich, libopenmpi-dev]
steps:
- name: Clone
id: checkout
uses: actions/checkout@v1
- name: Dependencies
id: depends
run: |
sudo apt-get update
sudo apt-get install build-essential ${{ matrix.mpi_library }}
- name: Build
id: cmake_build
run: |
mkdir build
cd build
cmake -DLLAMA_MPI=ON ..
cmake --build . --config Release
- name: Test
id: cmake_test
run: |
cd build
ctest --verbose
macOS-latest-make:
runs-on: macos-latest

1
.gitignore vendored
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@ -20,6 +20,7 @@ build-static/
build-cublas/
build-opencl/
build-metal/
build-mpi/
build-no-accel/
build-sanitize-addr/
build-sanitize-thread/

24
CMakeLists.txt
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@ -75,6 +75,7 @@ option(LLAMA_CUDA_DMMV_F16 "llama: use 16 bit floats for dmmv
set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
option(LLAMA_METAL "llama: use Metal" OFF)
option(LLAMA_MPI "llama: use MPI" OFF)
option(LLAMA_K_QUANTS "llama: use k-quants" ON)
option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF)
@ -308,6 +309,28 @@ if (LLAMA_METAL)
)
endif()
if (LLAMA_MPI)
cmake_minimum_required(VERSION 3.10)
find_package(MPI)
if (MPI_C_FOUND)
message(STATUS "MPI found")
set(GGML_SOURCES_MPI ggml-mpi.c ggml-mpi.h)
add_compile_definitions(GGML_USE_MPI)
add_compile_definitions(${MPI_C_COMPILE_DEFINITIONS})
set(cxx_flags ${cxx_flags} -Wno-cast-qual)
set(c_flags ${c_flags} -Wno-cast-qual)
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${MPI_C_LIBRARIES})
set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${MPI_C_INCLUDE_DIRS})
# Even if you're only using the C header, C++ programs may bring in MPI
# C++ functions, so more linkage is needed
if (MPI_CXX_FOUND)
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${MPI_CXX_LIBRARIES})
endif()
else()
message(WARNING "MPI not found")
endif()
endif()
if (LLAMA_CLBLAST)
find_package(CLBlast)
if (CLBlast_FOUND)
@ -476,6 +499,7 @@ add_library(ggml OBJECT
${GGML_SOURCES_CUDA}
${GGML_SOURCES_OPENCL}
${GGML_SOURCES_METAL}
${GGML_SOURCES_MPI}
${GGML_SOURCES_EXTRA}
)

9
Makefile
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@ -147,6 +147,15 @@ ifndef LLAMA_NO_ACCELERATE
endif
endif # LLAMA_NO_ACCELERATE
ifdef LLAMA_MPI
CFLAGS += -DGGML_USE_MPI -Wno-cast-qual
CXXFLAGS += -DGGML_USE_MPI -Wno-cast-qual
OBJS += ggml-mpi.o
ggml-mpi.o: ggml-mpi.c ggml-mpi.h
$(CC) $(CFLAGS) -c $< -o $@
endif # LLAMA_MPI
ifdef LLAMA_OPENBLAS
CFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/openblas -I/usr/include/openblas
LDFLAGS += -lopenblas

39
README.md
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@ -268,6 +268,45 @@ Any value larger than 0 will offload the computation to the GPU. For example:
./main -m ./models/7B/ggml-model-q4_0.bin -n 128 -ngl 1
```
### MPI Build
MPI lets you distribute the computation over a cluster of machines. Because of the serial nature of LLM prediction, this won't yield any end-to-end speed-ups, but it will let you run larger models than would otherwise fit into RAM on a single machine.
First you will need MPI libraries installed on your system. The two most popular (only?) options are [MPICH](https://www.mpich.org) and [OpenMPI](https://www.open-mpi.org). Either can be installed with a package manager (`apt`, Homebrew, MacPorts, etc).
Next you will need to build the project with `LLAMA_MPI` set to true on all machines; if you're building with `make`, you will also need to specify an MPI-capable compiler (when building with CMake, this is configured automatically):
- Using `make`:
```bash
make CC=mpicc CXX=mpicxx LLAMA_MPI=1
```
- Using `CMake`:
```bash
cmake -S . -B build -DLLAMA_MPI=ON
```
Once the programs are built, download/convert the weights on all of the machines in your cluster. The paths to the weights and programs should be identical on all machines.
Next, ensure password-less SSH access to each machine from the primary host, and create a `hostfile` with a list of the hostnames and their relative "weights" (slots). If you want to use localhost for computation, use its local subnet IP address rather than the loopback address or "localhost".
Here is an example hostfile:
```
192.168.0.1:2
malvolio.local:1
```
The above will distribute the computation across 2 processes on the first host and 1 process on the second host. Each process will use roughly an equal amount of RAM. Try to keep these numbers small, as inter-process (intra-host) communication is expensive.
Finally, you're ready to run a computation using `mpirun`:
```bash
mpirun -hostfile hostfile -n 3 ./main -m ./models/7B/ggml-model-q4_0.bin -n 128
```
### BLAS Build
Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). BLAS doesn't affect the normal generation performance. There are currently three different implementations of it:

2
examples/embd-input/embd-input-lib.cpp
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@ -34,7 +34,7 @@ struct MyModel* create_mymodel(int argc, char ** argv) {
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
llama_init_backend(params.numa);
llama_backend_init(params.numa);
llama_model * model;
llama_context * ctx;

4
examples/embedding/embedding.cpp
View File

@ -35,7 +35,7 @@ int main(int argc, char ** argv) {
params.prompt = gpt_random_prompt(rng);
}
llama_init_backend(params.numa);
llama_backend_init(params.numa);
llama_model * model;
llama_context * ctx;
@ -93,5 +93,7 @@ int main(int argc, char ** argv) {
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
return 0;
}

4
examples/main/main.cpp
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@ -105,7 +105,7 @@ int main(int argc, char ** argv) {
params.prompt = gpt_random_prompt(rng);
}
llama_init_backend(params.numa);
llama_backend_init(params.numa);
llama_model * model;
llama_context * ctx;
@ -671,5 +671,7 @@ int main(int argc, char ** argv) {
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
return 0;
}

4
examples/perplexity/perplexity.cpp
View File

@ -147,7 +147,7 @@ int main(int argc, char ** argv) {
params.prompt = gpt_random_prompt(rng);
}
llama_init_backend(params.numa);
llama_backend_init(params.numa);
llama_model * model;
llama_context * ctx;
@ -172,5 +172,7 @@ int main(int argc, char ** argv) {
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
return 0;
}

4
examples/quantize/quantize.cpp
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@ -180,7 +180,7 @@ int main(int argc, char ** argv) {
usage(argv[0]);
}
llama_init_backend(false);
llama_backend_init(false);
// parse command line arguments
const std::string fname_inp = argv[arg_idx];
@ -257,5 +257,7 @@ int main(int argc, char ** argv) {
printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0);
}
llama_backend_free();
return 0;
}

4
examples/server/server.cpp
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@ -1079,7 +1079,7 @@ int main(int argc, char **argv)
params.model_alias = params.model;
}
llama_init_backend(params.numa);
llama_backend_init(params.numa);
LOG_INFO("build info", {{"build", BUILD_NUMBER},
{"commit", BUILD_COMMIT}});
@ -1309,5 +1309,7 @@ int main(int argc, char **argv)
return 1;
}
llama_backend_free();
return 0;
}

4
examples/simple/simple.cpp
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@ -66,7 +66,7 @@ int main(int argc, char ** argv)
// Init LLM :
//---------------------------------
llama_init_backend(params.numa);
llama_backend_init(params.numa);
llama_model * model;
llama_context * ctx;
@ -173,6 +173,8 @@ int main(int argc, char ** argv)
llama_free( ctx );
llama_free_model( model );
llama_backend_free();
return 0;
}

1
ggml-metal.m
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@ -450,6 +450,7 @@ void ggml_metal_graph_compute(
//}
switch (dst->op) {
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_TRANSPOSE:

216
ggml-mpi.c Normal file
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@ -0,0 +1,216 @@
#include "ggml-mpi.h"
#include "ggml.h"
#include <mpi.h>
#include <stdio.h>
#include <stdlib.h>
#define MIN(a, b) ((a) < (b) ? (a) : (b))
#define UNUSED GGML_UNUSED
struct ggml_mpi_context {
int rank;
int size;
};
void ggml_mpi_backend_init(void) {
MPI_Init(NULL, NULL);
}
void ggml_mpi_backend_free(void) {
MPI_Finalize();
}
struct ggml_mpi_context * ggml_mpi_init(void) {
struct ggml_mpi_context * ctx = calloc(1, sizeof(struct ggml_mpi_context));
MPI_Comm_rank(MPI_COMM_WORLD, &ctx->rank);
MPI_Comm_size(MPI_COMM_WORLD, &ctx->size);
return ctx;
}
void ggml_mpi_free(struct ggml_mpi_context * ctx) {
free(ctx);
}
int ggml_mpi_rank(struct ggml_mpi_context * ctx) {
return ctx->rank;
}
void ggml_mpi_eval_init(
struct ggml_mpi_context * ctx_mpi,
int * n_tokens,
int * n_past,
int * n_threads) {
UNUSED(ctx_mpi);
// synchronize the worker node parameters with the root node
MPI_Barrier(MPI_COMM_WORLD);
MPI_Bcast(n_tokens, 1, MPI_INT, 0, MPI_COMM_WORLD);
MPI_Bcast(n_past, 1, MPI_INT, 0, MPI_COMM_WORLD);
MPI_Bcast(n_threads, 1, MPI_INT, 0, MPI_COMM_WORLD);
}
static int ggml_graph_get_node_idx(struct ggml_cgraph * gf, const char * name) {
struct ggml_tensor * t = ggml_graph_get_tensor(gf, name);
if (t == NULL) {
fprintf(stderr, "%s: tensor %s not found\n", __func__, name);
return -1;
}
for (int i = 0; i < gf->n_nodes; i++) {
if (gf->nodes[i] == t) {
return i;
}
}
fprintf(stderr, "%s: tensor %s not found in graph (should not happen)\n", __func__, name);
return -1;
}
static void ggml_mpi_tensor_send(struct ggml_tensor * t, int mpi_rank_dst) {
MPI_Datatype mpi_type;
switch (t->type) {
case GGML_TYPE_I32: mpi_type = MPI_INT32_T; break;
case GGML_TYPE_F32: mpi_type = MPI_FLOAT; break;
default: GGML_ASSERT(false && "not implemented");
}
const int retval = MPI_Send(t->data, ggml_nelements(t), mpi_type, mpi_rank_dst, 0, MPI_COMM_WORLD);
GGML_ASSERT(retval == MPI_SUCCESS);
}
static void ggml_mpi_tensor_recv(struct ggml_tensor * t, int mpi_rank_src) {
MPI_Datatype mpi_type;
switch (t->type) {
case GGML_TYPE_I32: mpi_type = MPI_INT32_T; break;
case GGML_TYPE_F32: mpi_type = MPI_FLOAT; break;
default: GGML_ASSERT(false && "not implemented");
}
MPI_Status status; UNUSED(status);
const int retval = MPI_Recv(t->data, ggml_nelements(t), mpi_type, mpi_rank_src, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
GGML_ASSERT(retval == MPI_SUCCESS);
}
// TODO: there are many improvements that can be done to this implementation
void ggml_mpi_graph_compute_pre(
struct ggml_mpi_context * ctx_mpi,
struct ggml_cgraph * gf,
int n_layers) {
const int mpi_rank = ctx_mpi->rank;
const int mpi_size = ctx_mpi->size;
struct ggml_tensor * inp_tokens = ggml_graph_get_tensor(gf, "inp_tokens");
if (inp_tokens == NULL) {
fprintf(stderr, "%s: tensor 'inp_tokens' not found\n", __func__);
return;
}
struct ggml_tensor * inp0 = ggml_graph_get_tensor(gf, "layer_inp_0");
if (inp0 == NULL) {
fprintf(stderr, "%s: tensor 'inp0' not found\n", __func__);
return;
}
GGML_ASSERT(inp0 == gf->nodes[0]);
// distribute the compute graph into slices across the MPI nodes
//
// the main node (0) processes the last layers + the remainder of the compute graph
// and is responsible to pass the input tokens to the first node (1)
//
// node 1: [( 0) * n_per_node, ( 1) * n_per_node)
// node 2: [( 1) * n_per_node, ( 2) * n_per_node)
// ...
// node n-1: [(n-2) * n_per_node, (n-1) * n_per_node)
// node 0: [(n-1) * n_per_node, n_nodes)
//
if (mpi_rank > 0) {
if (mpi_rank == 1) {
// the first node (1) receives the input tokens from the main node (0)
ggml_mpi_tensor_recv(inp_tokens, 0);
} else {
// recv input data for each node into the "inp0" tensor (i.e. the first node in the compute graph)
ggml_mpi_tensor_recv(inp0, mpi_rank - 1);
}
} else if (mpi_size > 1) {
// node 0 sends the input tokens to node 1
ggml_mpi_tensor_send(inp_tokens, 1);
// recv the output data from the last node
ggml_mpi_tensor_recv(inp0, mpi_size - 1);
}
{
const int n_per_node = (n_layers + (mpi_size - 1)) / mpi_size;
const int mpi_idx = mpi_rank > 0 ? mpi_rank - 1 : mpi_size - 1;
const int il0 = (mpi_idx + 0) * n_per_node;
const int il1 = MIN(n_layers, (mpi_idx + 1) * n_per_node);
char name_l0[GGML_MAX_NAME];
char name_l1[GGML_MAX_NAME];
snprintf(name_l0, sizeof(name_l0), "layer_inp_%d", il0);
snprintf(name_l1, sizeof(name_l1), "layer_inp_%d", il1);
const int idx_l0 = ggml_graph_get_node_idx(gf, name_l0);
const int idx_l1 = mpi_rank > 0 ? ggml_graph_get_node_idx(gf, name_l1) + 1 : gf->n_nodes;
if (idx_l0 < 0 || idx_l1 < 0) {
fprintf(stderr, "%s: layer input nodes not found\n", __func__);
return;
}
// attach the input data to all nodes that need it
// TODO: not great - should be able to do this without modifying the compute graph (see next TODO below)
for (int i = idx_l0; i < idx_l1; i++) {
if (gf->nodes[i]->src0 == gf->nodes[idx_l0]) {
gf->nodes[i]->src0 = inp0;
}
if (gf->nodes[i]->src1 == gf->nodes[idx_l0]) {
gf->nodes[i]->src1 = inp0;
}
}
// TODO: instead of rearranging the nodes, we should be able to execute a subset of the compute graph
for (int i = 1; i < idx_l1 - idx_l0; i++) {
gf->nodes[i] = gf->nodes[idx_l0 + i];
gf->grads[i] = gf->grads[idx_l0 + i];
}
// the first node performs the "get_rows" operation, the rest of the nodes get the data from the previous node
if (mpi_idx != 0) {
gf->nodes[0]->op = GGML_OP_NONE;
}
gf->n_nodes = idx_l1 - idx_l0;
//fprintf(stderr, "%s: node %d: processing %d nodes [%d, %d)\n", __func__, mpi_rank, gf->n_nodes, il0, il1);
}
}
void ggml_mpi_graph_compute_post(
struct ggml_mpi_context * ctx_mpi,
struct ggml_cgraph * gf,
int n_layers) {
UNUSED(n_layers);
const int mpi_rank = ctx_mpi->rank;
const int mpi_size = ctx_mpi->size;
// send the output data to the next node
if (mpi_rank > 0) {
ggml_mpi_tensor_send(gf->nodes[gf->n_nodes - 1], (mpi_rank + 1) % mpi_size);
}
}

39
ggml-mpi.h Normal file
View File

@ -0,0 +1,39 @@
#pragma once
struct ggml_context;
struct ggml_tensor;
struct ggml_cgraph;
#ifdef __cplusplus
extern "C" {
#endif
struct ggml_mpi_context;
void ggml_mpi_backend_init(void);
void ggml_mpi_backend_free(void);
struct ggml_mpi_context * ggml_mpi_init(void);
void ggml_mpi_free(struct ggml_mpi_context * ctx);
int ggml_mpi_rank(struct ggml_mpi_context * ctx);
void ggml_mpi_eval_init(
struct ggml_mpi_context * ctx_mpi,
int * n_tokens,
int * n_past,
int * n_threads);
void ggml_mpi_graph_compute_pre(
struct ggml_mpi_context * ctx_mpi,
struct ggml_cgraph * gf,
int n_layers);
void ggml_mpi_graph_compute_post(
struct ggml_mpi_context * ctx_mpi,
struct ggml_cgraph * gf,
int n_layers);
#ifdef __cplusplus
}
#endif

98
llama.cpp
View File

@ -19,6 +19,9 @@
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#ifdef GGML_USE_MPI
#include "ggml-mpi.h"
#endif
#ifdef GGML_USE_K_QUANTS
#ifndef QK_K
#ifdef GGML_QKK_64
@ -352,6 +355,10 @@ struct llama_context {
ggml_metal_context * ctx_metal = NULL;
#endif
#ifdef GGML_USE_MPI
ggml_mpi_context * ctx_mpi = NULL;
#endif
int buf_last = 0;
size_t buf_max_size[LLAMA_MAX_SCRATCH_BUFFERS] = { 0 };
@ -870,7 +877,7 @@ bool llama_mlock_supported() {
return llama_mlock::SUPPORTED;
}
void llama_init_backend(bool numa) {
void llama_backend_init(bool numa) {
ggml_time_init();
// needed to initialize f16 tables
@ -883,6 +890,16 @@ void llama_init_backend(bool numa) {
if (numa) {
ggml_numa_init();
}
#ifdef GGML_USE_MPI
ggml_mpi_backend_init();
#endif
}
void llama_backend_free() {
#ifdef GGML_USE_MPI
ggml_mpi_backend_free();
#endif
}
int64_t llama_time_us() {
@ -1284,13 +1301,17 @@ static bool llama_eval_internal(
llama_context & lctx,
const llama_token * tokens,
const float * embd,
const int n_tokens,
const int n_past,
int n_tokens,
int n_past,
int n_threads,
const char * cgraph_fname) {
LLAMA_ASSERT((!tokens && embd) || (tokens && !embd));
#ifdef GGML_USE_MPI
ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads);
#endif
const int64_t t_start_us = ggml_time_us();
const int N = n_tokens;
@ -1331,11 +1352,16 @@ static bool llama_eval_internal(
struct ggml_tensor * inpL;
if (tokens) {
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
ggml_set_name(embd, "embd");
memcpy(embd->data, tokens, N*ggml_element_size(embd));
inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
memcpy(inp_tokens->data, tokens, N*ggml_element_size(inp_tokens));
ggml_set_name(inp_tokens, "inp_tokens");
inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
} else {
#ifdef GGML_USE_MPI
GGML_ASSERT(false && "not implemented");
#endif
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N);
memcpy(inpL->data, embd, N * n_embd * ggml_element_size(inpL));
}
@ -1353,18 +1379,20 @@ static bool llama_eval_internal(
offload_func_t offload_func_v = llama_nop;
#ifdef GGML_USE_CUBLAS
if (n_gpu_layers > n_layer) {
offload_func_nr = ggml_cuda_assign_buffers;
}
if (n_gpu_layers > n_layer + 1) {
offload_func_v = ggml_cuda_assign_buffers;
}
if (n_gpu_layers > n_layer + 2) {
offload_func_kq = ggml_cuda_assign_buffers;
}
if (n_gpu_layers > n_layer) {
offload_func_nr = ggml_cuda_assign_buffers;
}
if (n_gpu_layers > n_layer + 1) {
offload_func_v = ggml_cuda_assign_buffers;
}
if (n_gpu_layers > n_layer + 2) {
offload_func_kq = ggml_cuda_assign_buffers;
}
#endif // GGML_USE_CUBLAS
for (int il = 0; il < n_layer; ++il) {
ggml_format_name(inpL, "layer_inp_%d", il);
offload_func_t offload_func = llama_nop;
#ifdef GGML_USE_CUBLAS
@ -1571,7 +1599,6 @@ static bool llama_eval_internal(
// input for next layer
inpL = cur;
}
lctx.use_buf(ctx0, 0);
@ -1579,7 +1606,6 @@ static bool llama_eval_internal(
// used at the end to optionally extract the embeddings
struct ggml_tensor * embeddings = NULL;
// norm
{
cur = ggml_rms_norm(ctx0, inpL);
@ -1594,7 +1620,6 @@ static bool llama_eval_internal(
embeddings = cur;
}
// lm_head
cur = ggml_mul_mat(ctx0, model.output, cur);
ggml_set_name(cur, "result_output");
@ -1607,6 +1632,10 @@ static bool llama_eval_internal(
// run the computation
ggml_build_forward_expand(&gf, cur);
#if GGML_USE_MPI
ggml_mpi_graph_compute_pre(lctx.ctx_mpi, &gf, n_layer);
#endif
#ifdef GGML_USE_METAL
if (lctx.ctx_metal && N == 1) {
ggml_metal_set_n_cb (lctx.ctx_metal, n_threads);
@ -1635,6 +1664,15 @@ static bool llama_eval_internal(
ggml_graph_compute_helper(lctx.work_buffer, &gf, n_threads);
#endif
#if GGML_USE_MPI
ggml_mpi_graph_compute_post(lctx.ctx_mpi, &gf, n_layer);
#endif
// update kv token count
lctx.kv_self.n = n_past + N;
struct ggml_tensor * res = gf.nodes[gf.n_nodes - 1];
if (cgraph_fname) {
ggml_graph_export(&gf, cgraph_fname);
}
@ -1650,23 +1688,17 @@ static bool llama_eval_internal(
// ggml_graph_dump_dot(&gf, NULL, "llama.dot");
//}
//embd_w.resize(n_vocab*N);
//memcpy(embd_w.data(), ggml_get_data(cur), sizeof(float)*n_vocab*N);
// update kv token count
lctx.kv_self.n = n_past + N;
// extract logits
{
auto & logits_out = lctx.logits;
if (lctx.logits_all) {
logits_out.resize(n_vocab * N);
memcpy(logits_out.data(), (float *) ggml_get_data(cur), sizeof(float)*n_vocab*N);
memcpy(logits_out.data(), (float *) ggml_get_data(res), sizeof(float)*n_vocab*N);
} else {
// return result for just the last token
logits_out.resize(n_vocab);
memcpy(logits_out.data(), (float *) ggml_get_data(cur) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
memcpy(logits_out.data(), (float *) ggml_get_data(res) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
}
}
@ -2697,6 +2729,18 @@ struct llama_context * llama_new_context_with_model(
}
#endif
#ifdef GGML_USE_MPI
ctx->ctx_mpi = ggml_mpi_init();
if (ggml_mpi_rank(ctx->ctx_mpi) > 0) {
// Enter a blocking eval loop with dummy input, letting rank=0 drive the process
const std::vector<llama_token> tmp(ctx->model.hparams.n_ctx, llama_token_bos());
while (!llama_eval(ctx, tmp.data(), tmp.size(), 0, 0)) {};
llama_backend_free();
exit(1);
}
#endif
return ctx;
}

4
llama.h
View File

@ -158,7 +158,9 @@ extern "C" {
// Initialize the llama + ggml backend
// If numa is true, use NUMA optimizations
// Call once at the start of the program
LLAMA_API void llama_init_backend(bool numa);
LLAMA_API void llama_backend_init(bool numa);
// Call once at the end of the program - currently only used for MPI
LLAMA_API void llama_backend_free();
LLAMA_API int64_t llama_time_us();

4
tests/test-tokenizer-0.cpp
View File

@ -31,6 +31,8 @@ int main(int argc, char **argv) {
llama_model * model;
llama_context * ctx;
llama_backend_init(false);
// load the vocab
{
auto lparams = llama_context_default_params();
@ -97,5 +99,7 @@ int main(int argc, char **argv) {
llama_free_model(model);
llama_free(ctx);
llama_backend_free();
return 0;
}