char-fork/llama.cpp

git clone https://git.t4t.associates/char-fork/llama.cpp

Christian Kastnertests: export-graph-ops: exit gracefully when called w/o arguments (#25619)657e01125

char/aliceai-t5-moe
brancheschar/aliceai-t5-moeall refs →
6.3 KiB230 linesraw
1#include "arg.h"
2#include "common.h"
3#include "log.h"
4#include "llama-cpp.h"
5#include "../src/llama-ext.h"
6#include "ggml.h"
7#include "gguf-model-data.h"
8#include "gguf.h"
9#include "ggml-backend.h"
10#include "download.h"
11
12#include <array>
13#include <vector>
14#include <set>
15#include <fstream>
16#include <iostream>
17#include <random>
18
19// Noop because weights are not needed
20static void set_tensor_data(struct ggml_tensor * tensor, void * userdata) {
21    GGML_UNUSED(tensor);
22    GGML_UNUSED(userdata);
23}
24
25struct input_tensor {
26    ggml_type type;
27    std::array<int64_t, 4> ne;
28    std::array<size_t, 4> nb;
29
30    input_tensor(ggml_type type, int64_t * ne, size_t * nb): type(type) {
31        memcpy(this->ne.data(), ne, 4 * sizeof(int64_t));
32        memcpy(this->nb.data(), nb, 4 * sizeof(size_t));
33    }
34
35    bool operator<(const input_tensor &b) const {
36        return std::tie(type, ne, nb) <
37               std::tie(b.type, b.ne, b.nb);
38    }
39
40    void serialize(std::ostream& out) const {
41        out << type << ' ';
42        for (size_t i = 0; i < 4; i++) {
43            out << ne[i] << ' ';
44        }
45        for (size_t i = 0; i < 4; i++) {
46            out << nb[i] << ' ';
47        }
48    }
49};
50
51struct test_object {
52    ggml_op op;
53    ggml_type type;
54    std::array<int64_t, 4> ne;
55    std::vector<int32_t> op_params;
56    std::vector<input_tensor> sources;
57    std::string name;
58
59    void serialize(std::ostream& out) const {
60        out << op << ' ' << type << ' ';
61        for (size_t i = 0; i < 4; i++) {
62            out << ne[i] << ' ';
63        }
64
65        out << op_params.size() << ' ';
66        for (size_t i = 0; i < op_params.size(); i++) {
67            out << op_params[i] << ' ';
68        }
69
70        out << sources.size() << ' ';
71        for (size_t s = 0; s < sources.size(); s++) {
72            sources[s].serialize(out);
73        }
74
75        if (!name.empty()) {
76            out << name;
77        } else {
78            out << '-';
79        }
80
81        out << '\n';
82    }
83
84    bool operator<(const test_object &b) const {
85        return std::tie(op, type, ne, op_params, sources) <
86               std::tie(b.op, b.type, b.ne, b.op_params, b.sources);
87    }
88};
89
90static void extract_graph_ops(ggml_cgraph * cgraph, const char * label, std::set<test_object> & tests) {
91    int n_nodes = ggml_graph_n_nodes(cgraph);
92    int n_skipped = 0;
93    int n_before = (int) tests.size();
94    for (int i = 0; i < n_nodes; i++) {
95        ggml_tensor * node = ggml_graph_node(cgraph, i);
96
97        if (node->op == GGML_OP_NONE || node->op == GGML_OP_VIEW || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_TRANSPOSE) {
98            n_skipped++;
99            continue;
100        }
101
102        test_object test;
103
104        test.op = node->op;
105        test.type = node->type;
106        memcpy(&test.ne, node->ne, 4 * sizeof(int64_t));
107
108        test.op_params.resize(GGML_MAX_OP_PARAMS / sizeof(int32_t));
109        memcpy(test.op_params.data(), node->op_params, GGML_MAX_OP_PARAMS);
110
111        for (size_t s = 0; s < GGML_MAX_SRC; s++) {
112            if (node->src[s] == nullptr) {
113                break;
114            }
115
116            test.sources.emplace_back(node->src[s]->type, node->src[s]->ne, node->src[s]->nb);
117        }
118
119        test.name = node->name;
120        tests.insert(test);
121    }
122
123    int n_new = (int) tests.size() - n_before;
124    LOG_INF("%s: %d unique ops, %d total nodes, %d skipped (view ops)\n",
125            label, n_new, n_nodes, n_skipped);
126}
127
128int main(int argc, char ** argv) {
129    common_params params;
130    params.out_file = "tests.txt";
131
132    common_init();
133
134    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_EXPORT_GRAPH_OPS)) {
135        return 1;
136    }
137
138    // Load CPU-only
139    ggml_backend_dev_t cpu_device = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
140    params.devices = { cpu_device, nullptr };
141    params.fit_params = false;
142    params.n_gpu_layers = 0;
143
144    params.warmup = false;
145
146    llama_context * ctx;
147    common_init_result_ptr init_result;
148    llama_context_ptr ctx2;
149    llama_model_ptr model;
150
151    if (params.model.hf_repo.empty()) {
152        init_result = common_init_from_params(params);
153
154        ctx = init_result->context();
155        if (!ctx) {
156            LOG_ERR("failed to initialize params\n");
157            return 1;
158        }
159    } else {
160#ifdef LLAMA_HF_FETCH
161        auto [hf_repo, hf_quant] = common_download_split_repo_tag(params.model.hf_repo);
162        if (hf_quant.empty() || hf_quant == "latest") {
163            hf_quant = "Q4_K_M";
164        }
165
166        gguf_context_ptr gguf_ctx = gguf_fetch_gguf_ctx(hf_repo, hf_quant);
167        if (!gguf_ctx) {
168            LOG_ERR("failed to fetch GGUF metadata from %s\n", hf_repo.c_str());
169            return 1;
170        }
171
172        llama_model_params model_params = llama_model_default_params();
173        model_params.devices = params.devices.data();
174        model_params.no_alloc = true;
175
176        model.reset(llama_model_init_from_user(gguf_ctx.get(), set_tensor_data, nullptr, model_params));
177
178        if (!model) {
179            LOG_ERR("failed to create llama_model from %s\n", hf_repo.c_str());
180            return 1;
181        }
182
183        llama_context_params ctx_params = llama_context_default_params();
184        ctx2.reset(llama_init_from_model(model.get(), ctx_params));
185        ctx = ctx2.get();
186
187        if (!ctx) {
188            LOG_ERR("failed to create llama_context\n");
189            return 1;
190        }
191#else
192        LOG_ERR("test-export-graph-ops compiled without HF fetch support\n");
193        return 1;
194#endif
195    }
196
197    const uint32_t n_seqs  = llama_n_seq_max(ctx);
198    const uint32_t n_tokens = std::min(llama_n_ctx(ctx), llama_n_ubatch(ctx));
199
200    std::set<test_object> tests;
201
202    auto * gf_pp = llama_graph_reserve(ctx, n_tokens, n_seqs, n_tokens);
203    if (!gf_pp) {
204        LOG_ERR("failed to reserve prompt processing graph\n");
205        return 1;
206    }
207    extract_graph_ops(gf_pp, "pp", tests);
208
209    auto * gf_tg = llama_graph_reserve(ctx, n_seqs, n_seqs, n_seqs);
210    if (!gf_tg) {
211        LOG_ERR("failed to reserve token generation graph\n");
212        return 1;
213    }
214    extract_graph_ops(gf_tg, "tg", tests);
215
216    LOG_INF("%d unique ops total\n", (int) tests.size());
217
218    std::ofstream f(params.out_file);
219
220    if (!f.is_open()) {
221        LOG_ERR("unable to open output file: %s\n", params.out_file.c_str());
222        return 1;
223    }
224
225    for (const auto& test : tests) {
226        test.serialize(f);
227    }
228
229    return 0;
230}