subprocess llama.cpp server (#401)

* remove c code
* pack llama.cpp
* use request context for llama_cpp
* let llama_cpp decide the number of threads to use
* stop llama runner when app stops
* remove sample count and duration metrics
* use go generate to get libraries
* tmp dir for running llm

.gitignore

@@ -5,4 +5,3 @@
 .swp
 dist
 ollama
-/ggml-metal.metal

.gitmodules

+[submodule "llm/llama.cpp/ggml"]
+	path = llm/llama.cpp/ggml
+	url = https://github.com/ggerganov/llama.cpp.git

api/types.go

@@ -7,7 +7,6 @@ import (
 	"math"
 	"os"
 	"reflect"
-	"runtime"
 	"strings"
 	"time"
 )
@@ -113,8 +112,6 @@ type GenerateResponse struct {
 
 	TotalDuration      time.Duration `json:"total_duration,omitempty"`
 	LoadDuration       time.Duration `json:"load_duration,omitempty"`
-	SampleCount        int           `json:"sample_count,omitempty"`
-	SampleDuration     time.Duration `json:"sample_duration,omitempty"`
 	PromptEvalCount    int           `json:"prompt_eval_count,omitempty"`
 	PromptEvalDuration time.Duration `json:"prompt_eval_duration,omitempty"`
 	EvalCount          int           `json:"eval_count,omitempty"`
@@ -130,15 +127,6 @@ func (r *GenerateResponse) Summary() {
 		fmt.Fprintf(os.Stderr, "load duration:        %v\n", r.LoadDuration)
 	}
 
-	if r.SampleCount > 0 {
-		fmt.Fprintf(os.Stderr, "sample count:         %d token(s)\n", r.SampleCount)
-	}
-
-	if r.SampleDuration > 0 {
-		fmt.Fprintf(os.Stderr, "sample duration:      %s\n", r.SampleDuration)
-		fmt.Fprintf(os.Stderr, "sample rate:          %.2f tokens/s\n", float64(r.SampleCount)/r.SampleDuration.Seconds())
-	}
-
 	if r.PromptEvalCount > 0 {
 		fmt.Fprintf(os.Stderr, "prompt eval count:    %d token(s)\n", r.PromptEvalCount)
 	}
@@ -182,15 +170,16 @@ type Options struct {
 	RopeFrequencyScale float32 `json:"rope_frequency_scale,omitempty"`
 
 	// Predict options
-	RepeatLastN      int      `json:"repeat_last_n,omitempty"`
-	RepeatPenalty    float32  `json:"repeat_penalty,omitempty"`
-	FrequencyPenalty float32  `json:"frequency_penalty,omitempty"`
-	PresencePenalty  float32  `json:"presence_penalty,omitempty"`
-	Temperature      float32  `json:"temperature,omitempty"`
+	NumPredict       int      `json:"num_predict,omitempty"`
 	TopK             int      `json:"top_k,omitempty"`
 	TopP             float32  `json:"top_p,omitempty"`
 	TFSZ             float32  `json:"tfs_z,omitempty"`
 	TypicalP         float32  `json:"typical_p,omitempty"`
+	RepeatLastN      int      `json:"repeat_last_n,omitempty"`
+	Temperature      float32  `json:"temperature,omitempty"`
+	RepeatPenalty    float32  `json:"repeat_penalty,omitempty"`
+	PresencePenalty  float32  `json:"presence_penalty,omitempty"`
+	FrequencyPenalty float32  `json:"frequency_penalty,omitempty"`
 	Mirostat         int      `json:"mirostat,omitempty"`
 	MirostatTau      float32  `json:"mirostat_tau,omitempty"`
 	MirostatEta      float32  `json:"mirostat_eta,omitempty"`
@@ -314,7 +303,7 @@ func DefaultOptions() Options {
 		MirostatEta:      0.1,
 		PenalizeNewline:  true,
 
-		NumThread: runtime.NumCPU(),
+		NumThread: 0, // let the runtime decide
 	}
 }
 

app/src/index.ts

@@ -158,7 +158,7 @@ function restart() {
 app.on('before-quit', () => {
   if (proc) {
     proc.off('exit', restart)
-    proc.kill()
+    proc.kill('SIGINT') // send SIGINT signal to the server, which also stops any loaded llms
   }
 })
 

docs/development.md

 # Development
 
+- Install cmake or (optionally, required tools for GPUs)
+- run `go generate ./...`
+- run `go build .`
+
 Install required tools:
 
 ```
-brew install go
+brew install go cmake gcc
 ```
 
-Enable CGO:
+Get the required libraries:
 
 ```
-export CGO_ENABLED=1
+go generate ./...
 ```
 
-You will also need a C/C++ compiler such as GCC for MacOS and Linux or Mingw-w64 GCC for Windows.
-
 Then build ollama:
 
 ```

go.mod

@@ -38,9 +38,9 @@ require (
 	github.com/twitchyliquid64/golang-asm v0.15.1 // indirect
 	github.com/ugorji/go/codec v1.2.11 // indirect
 	golang.org/x/arch v0.3.0 // indirect
-	golang.org/x/crypto v0.10.0 // indirect
+	golang.org/x/crypto v0.10.0
 	golang.org/x/net v0.10.0 // indirect
-	golang.org/x/sys v0.10.0 // indirect
+	golang.org/x/sys v0.11.0 // indirect
 	golang.org/x/term v0.10.0
 	golang.org/x/text v0.10.0 // indirect
 	gonum.org/v1/gonum v0.13.0

go.sum

@@ -120,6 +120,7 @@ golang.org/x/arch v0.3.0/go.mod h1:5om86z9Hs0C8fWVUuoMHwpExlXzs5Tkyp9hOrfG7pp8=
 golang.org/x/crypto v0.0.0-20210711020723-a769d52b0f97/go.mod h1:GvvjBRRGRdwPK5ydBHafDWAxML/pGHZbMvKqRZ5+Abc=
 golang.org/x/crypto v0.10.0 h1:LKqV2xt9+kDzSTfOhx4FrkEBcMrAgHSYgzywV9zcGmM=
 golang.org/x/crypto v0.10.0/go.mod h1:o4eNf7Ede1fv+hwOwZsTHl9EsPFO6q6ZvYR8vYfY45I=
+golang.org/x/exp v0.0.0-20230321023759-10a507213a29 h1:ooxPy7fPvB4kwsA2h+iBNHkAbp/4JxTSwCmvdjEYmug=
 golang.org/x/net v0.0.0-20210226172049-e18ecbb05110/go.mod h1:m0MpNAwzfU5UDzcl9v0D8zg8gWTRqZa9RBIspLL5mdg=
 golang.org/x/net v0.10.0 h1:X2//UzNDwYmtCLn7To6G58Wr6f5ahEAQgKNzv9Y951M=
 golang.org/x/net v0.10.0/go.mod h1:0qNGK6F8kojg2nk9dLZ2mShWaEBan6FAoqfSigmmuDg=
@@ -130,8 +131,8 @@ golang.org/x/sys v0.0.0-20210806184541-e5e7981a1069/go.mod h1:oPkhp1MJrh7nUepCBc
 golang.org/x/sys v0.0.0-20220310020820-b874c991c1a5/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.0.0-20220704084225-05e143d24a9e/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.6.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
-golang.org/x/sys v0.10.0 h1:SqMFp9UcQJZa+pmYuAKjd9xq1f0j5rLcDIk0mj4qAsA=
-golang.org/x/sys v0.10.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
+golang.org/x/sys v0.11.0 h1:eG7RXZHdqOJ1i+0lgLgCpSXAp6M3LYlAo6osgSi0xOM=
+golang.org/x/sys v0.11.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/term v0.0.0-20201126162022-7de9c90e9dd1/go.mod h1:bj7SfCRtBDWHUb9snDiAeCFNEtKQo2Wmx5Cou7ajbmo=
 golang.org/x/term v0.10.0 h1:3R7pNqamzBraeqj/Tj8qt1aQ2HpmlC+Cx/qL/7hn4/c=
 golang.org/x/term v0.10.0/go.mod h1:lpqdcUyK/oCiQxvxVrppt5ggO2KCZ5QblwqPnfZ6d5o=

llm/ggml-alloc.h

-/**
- * llama.cpp - git 3ebb00935f3f0522b75df49c2769ab1774b91380
- *
- * MIT License
- *
- * Copyright (c) 2023 Georgi Gerganov
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-#pragma once
-
-#include "ggml.h"
-
-#ifdef  __cplusplus
-extern "C" {
-#endif
-
-
-GGML_API struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment);
-GGML_API struct ggml_allocr * ggml_allocr_new_measure(size_t alignment);
-
-GGML_API void   ggml_allocr_free(struct ggml_allocr * alloc);
-GGML_API bool   ggml_allocr_is_measure(struct ggml_allocr * alloc);
-GGML_API void   ggml_allocr_reset(struct ggml_allocr * alloc);
-GGML_API void   ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor);
-GGML_API size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph);
-
-
-#ifdef  __cplusplus
-}
-#endif

llm/ggml-cuda.h

-/**
- * llama.cpp - git 3ebb00935f3f0522b75df49c2769ab1774b91380
- *
- * MIT License
- *
- * Copyright (c) 2023 Georgi Gerganov
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-#pragma once
-
-#include "ggml.h"
-
-#ifdef  __cplusplus
-extern "C" {
-#endif
-
-#define GGML_CUDA_MAX_DEVICES       16
-
-void   ggml_init_cublas(void);
-void   ggml_cuda_set_tensor_split(const float * tensor_split);
-
-void   ggml_cuda_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
-bool   ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
-size_t ggml_cuda_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
-void   ggml_cuda_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
-
-// TODO: export these with GGML_API
-void * ggml_cuda_host_malloc(size_t size);
-void   ggml_cuda_host_free(void * ptr);
-
-void   ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);
-
-void   ggml_cuda_free_data(struct ggml_tensor * tensor);
-void   ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
-void   ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
-void   ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
-void   ggml_cuda_set_main_device(int main_device);
-void   ggml_cuda_set_mul_mat_q(bool mul_mat_q);
-void   ggml_cuda_set_scratch_size(size_t scratch_size);
-void   ggml_cuda_free_scratch(void);
-bool   ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
-
-#ifdef  __cplusplus
-}
-#endif

llm/ggml-metal.h

-//go:build darwin
-
-/**
- * llama.cpp - git 3ebb00935f3f0522b75df49c2769ab1774b91380
- *
- * MIT License
- *
- * Copyright (c) 2023 Georgi Gerganov
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-// An interface allowing to compute ggml_cgraph with Metal
-//
-// This is a fully functional interface that extends ggml with GPU support for Apple devices.
-// A similar interface can be created for other GPU backends (e.g. Vulkan, CUDA, OpenCL, etc.)
-//
-// How it works?
-//
-// As long as your program can create and evaluate a ggml_cgraph on the CPU, you can use this
-// interface to evaluate the same graph on the GPU. Instead of using ggml_graph_compute(), you
-// use ggml_metal_graph_compute() (or ggml_vulkan_graph_compute(), etc.)
-//
-// You only need to make sure that all memory buffers that you used during the graph creation
-// are mapped to the device memory with the ggml_metal_add_buffer() function. This mapping is
-// used during the graph evaluation to determine the arguments of the compute kernels.
-//
-// Synchronization between device and host memory (for example for input and output tensors)
-// is done with the ggml_metal_set_tensor() and ggml_metal_get_tensor() functions.
-//
-
-#pragma once
-
-#include <stddef.h>
-#include <stdbool.h>
-
-// max memory buffers that can be mapped to the device
-#define GGML_METAL_MAX_BUFFERS 16
-
-struct ggml_tensor;
-struct ggml_cgraph;
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-struct ggml_metal_context;
-
-// number of command buffers to use
-struct ggml_metal_context * ggml_metal_init(int n_cb);
-void ggml_metal_free(struct ggml_metal_context * ctx);
-
-// set the number of command buffers to use
-void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb);
-
-// creates a mapping between a host memory buffer and a device memory buffer
-// - make sure to map all buffers used in the graph before calling ggml_metal_graph_compute
-// - the mapping is used during computation to determine the arguments of the compute kernels
-// - you don't need to keep the host memory buffer allocated as it is never accessed by Metal
-// - max_size specifies the maximum size of a tensor and is used to create shared views such
-//   that it is guaranteed that the tensor will fit in at least one of the views
-//
-bool ggml_metal_add_buffer(
-        struct ggml_metal_context * ctx,
-                       const char * name,
-                             void * data,
-                           size_t   size,
-                           size_t   max_size);
-
-// set data from host memory into the device
-void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
-
-// get data from the device into host memory
-void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
-
-// try to find operations that can be run concurrently in the graph
-// you should run it again if the topology of your graph changes
-void ggml_metal_graph_find_concurrency(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
-
-// if the graph has been optimized for concurrently dispatch
-bool ggml_metal_if_optimized(struct ggml_metal_context * ctx);
-
-// same as ggml_graph_compute but uses Metal
-// creates gf->n_threads command buffers in parallel
-void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
-
-#ifdef __cplusplus
-}
-#endif
-

llm/ggml-mpi.c

-//go:build mpi
-
-/**
- * llama.cpp - git 3ebb00935f3f0522b75df49c2769ab1774b91380
- *
- * MIT License
- *
- * Copyright (c) 2023 Georgi Gerganov
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-#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]->src[0] == gf->nodes[idx_l0]) {
-                gf->nodes[i]->src[0] =  inp0;
-            }
-            if (gf->nodes[i]->src[1] == gf->nodes[idx_l0]) {
-                gf->nodes[i]->src[1] =  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);
-    }
-}

llm/ggml-mpi.h

-//go:build mpi
-
-/**
- * llama.cpp - git 3ebb00935f3f0522b75df49c2769ab1774b91380
- *
- * MIT License
- *
- * Copyright (c) 2023 Georgi Gerganov
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-#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

llm/ggml-opencl.h

-//go:build opencl
-
-/**
- * llama.cpp - git 3ebb00935f3f0522b75df49c2769ab1774b91380
- *
- * MIT License
- *
- * Copyright (c) 2023 Georgi Gerganov
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-#pragma once
-
-#include "ggml.h"
-
-#ifdef  __cplusplus
-extern "C" {
-#endif
-
-void ggml_cl_init(void);
-
-void   ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
-bool   ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
-size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
-void   ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
-
-void * ggml_cl_host_malloc(size_t size);
-void   ggml_cl_host_free(void * ptr);
-
-void ggml_cl_free_data(const struct ggml_tensor* tensor);
-
-void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor);
-
-#ifdef  __cplusplus
-}
-#endif