vbatts/llama.cpp
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temporary commit while I move dev environments

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it finally outputs a functioning control vector - "functioning" in the sense that it can be loaded and it clearly has the right idea, but makes the model incoherent
This commit is contained in:
Christian Zhou-Zheng 2024-06-03 17:40:19 -04:00
parent 15d5c257a0
commit 07dba13ab6
1 changed files with 197 additions and 161 deletions
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358
examples/control-vector-generator/control-vector-generator.cpp
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@ -22,30 +22,50 @@
// TODO read everything over and make sure it makes sense because I'm dropping logic errors left and right - Christian
// to reduce the amount of stuff that gets sent to cb_eval I separated it somewhat - Christian
struct callback_data {
std::vector<uint8_t> data;
ggml_context * ctx_ggml;
ggml_context * ctx_ggml; // holds v_pos, v_neg
int n_tokens = 0;
int n_embd = 0;
bool is_eval_pos = true;
// each element of the vector correspond to one layer
std::vector<struct ggml_tensor *> v_pos; // vector of matrices of size [n_embd, n_tokens]
std::vector<struct ggml_tensor *> v_neg; // vector of matrices of size [n_embd, n_tokens]
std::vector<struct ggml_tensor *> v_final; // vector of finished vectors of size [n_embd]
std::vector<struct ggml_tensor *> v_diff; // vector of matrices of size [n_embd, m] where m ~ n_tokens * n_completions
std::vector<struct ggml_tensor *> v_pos; // vector of matrices of size [n_embd, n_tokens]
std::vector<struct ggml_tensor *> v_neg; // vector of matrices of size [n_embd, n_tokens]
// each element of the outer vector correspond to one layer, each element of the inner vector correspond to one prompt pass
std::vector<std::vector<struct ggml_tensor *>> v_diffs_wrapped; // vector of compiled diff matrices to be concatenated
// TODO ggml destructor?
~callback_data() {
for (auto ptr : v_pos) free(ptr);
for (auto ptr : v_neg) free(ptr);
ggml_free(ctx_ggml);
}
};
// I prefer having the different contexts so we can free each immediately after we're done using it
// e.g. we don't need the diffs_wrapped once we strip zero rows + concatenate them so we can ggml_free it, etc.
// @ngxson let me know what you think - @christianazinn
struct diff_ctx {
int n_embd = 0;
int n_threads = 8;
ggml_context * ctx_diffs_wrapped; // holds v_diffs_wrapped
ggml_context * ctx_diff; // holds v_diff
ggml_context * ctx_final; // holds v_final
// each element of the vector correspond to one layer
std::vector<struct ggml_tensor *> v_diff; // vector of matrices of size [n_embd, m] where m ~ n_tokens * n_completions
std::vector<struct ggml_tensor *> v_final; // vector of vectors of size [n_embd] to be written to file
// each element of the outer vector correspond to one layer, each element of the inner vector correspond to one prompt pass
std::vector<std::vector<struct ggml_tensor *>> v_diffs_wrapped; // vector of compiled diff matrices of size [n_embd, n_tokens] to be concatenated
~diff_ctx() {
for (auto ptr : v_diff) free(ptr);
for (auto ptr : v_final) free(ptr);
for (auto & vec : v_diffs_wrapped) for (auto ptr : vec) free(ptr);
ggml_free(ctx_diffs_wrapped);
ggml_free(ctx_diff);
ggml_free(ctx_final);
}
};
@ -289,9 +309,6 @@ static bool cb_eval(struct ggml_tensor * t, bool ask, void * user_data) {
// copy the data from the GPU memory if needed
const bool is_host = ggml_backend_buffer_is_host(t->buffer);
// FIXME something is very wrong here
// v_pos and v_neg are being populated, but the values aren't correct - it writes the same values to all vectors, it looks like?
// this leads ultimately to an error in calc_diff where diff becomes entirely zeroes and eventually a segfault several iterations into pca
struct ggml_tensor * t_host;
auto n_bytes = ggml_nbytes(t);
t_host = ggml_new_tensor_2d(cb_data->ctx_ggml, t->type, t->ne[0], t->ne[1]);
@ -328,6 +345,40 @@ static void padding_seq(llama_context * ctx, std::vector<llama_token> & tokens,
}
}
static void calc_diff(callback_data & cb_data, diff_ctx & dctx) {
// TODO: assert cb_data.v_pos.size() == cb_data.v_neg.size()
dctx.v_diffs_wrapped.resize(cb_data.v_pos.size());
for (size_t il = 0; il < cb_data.v_pos.size(); il++) {
std::cout << "il: " << il << " of " << cb_data.v_pos.size()-1 << std::endl;
auto & inp_pos = cb_data.v_pos[il];
auto & inp_neg = cb_data.v_neg[il];
auto n_bytes = ggml_nbytes(inp_pos);
printf("inp_pos [0][%d]: %f\n", DEBUG_POS, ggml_get_f32_nd(inp_pos, 0, DEBUG_POS, 0, 0));
printf("inp_neg [0][%d]: %f\n", DEBUG_POS, ggml_get_f32_nd(inp_neg, 0, DEBUG_POS, 0, 0));
// TODO is this the best way to get dimension? i don't know which way n_embd/n_tokens go
// for that matter can we get rid of n_embd/n_tokens fields in favor of ne[0]/ne[1]?
// TODO assert inp_pos->ne[0] == inp_neg->ne[0] && inp_pos->ne[1] == inp_neg->ne[1]
struct ggml_tensor * dest = ggml_new_tensor_2d(dctx.ctx_diffs_wrapped, GGML_TYPE_F32, inp_pos->ne[0], inp_pos->ne[1]);
dest->data = malloc(n_bytes); // TODO @ngxson get rid of this malloc somehow
for (size_t i = 0; i < inp_pos->ne[0]; i++) {
for (size_t j = 0; j < inp_pos->ne[1]; j++) {
ggml_set_f32_nd(dest, i, j, 0, 0, ggml_get_f32_nd(inp_pos, i, j, 0, 0) - ggml_get_f32_nd(inp_neg, i, j, 0, 0));
}
}
printf("dest [0][%d]: %f\n", DEBUG_POS, ggml_get_f32_nd(dest, 0, DEBUG_POS, 0, 0));
dctx.v_diffs_wrapped[il].push_back(dest);
}
}
// TODO nomenclature is probably wrong! this should be cols
// row/col mixup has been giving me a headache this entire time because apparently ggml accesses 2d as [col][row] - @christianazinn
// TODO check row/col because that's probably where the logic error is
static bool is_row_all_zeros(struct ggml_tensor * diff, int row, int cols, float eps = 1e-6) {
for (int i = 0; i < cols; ++i) {
if (ggml_get_f32_nd(diff, i, row, 0, 0) > eps) {
@ -337,114 +388,61 @@ static bool is_row_all_zeros(struct ggml_tensor * diff, int row, int cols, float
return true;
}
static void calc_diff(callback_data & cb_data) {
// TODO: assert cb_data.v_pos.size() == cb_data.v_neg.size()
cb_data.v_diffs_wrapped.resize(cb_data.v_pos.size());
for (size_t il = 0; il < cb_data.v_pos.size(); il++) {
auto & inp_pos = cb_data.v_pos[il];
auto & inp_neg = cb_data.v_neg[il];
auto n_bytes = ggml_nbytes(inp_pos);
static void concatenate_diffs(diff_ctx & dctx) {
// TODO can you do this inplace?
// TODO assert each tensor has the same ->ne[0] and it equals dctx.n_embd
printf("concatenate_diffs\n");
for (size_t il = 0; il < dctx.v_diffs_wrapped.size(); ++il) {
printf("il: %zu of %zu\n", il, dctx.v_diffs_wrapped.size()-1);
std::vector<struct ggml_tensor *> & vec = dctx.v_diffs_wrapped[il];
struct ggml_init_params params = {
/*.mem_size =*/ n_bytes + ggml_tensor_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ false,
};
struct ggml_context * ctx_data = ggml_init(params);
printf("inp_pos [0][%d]: %f\n", DEBUG_POS, ggml_get_f32_nd(inp_pos, 0, DEBUG_POS, 0, 0));
printf("inp_neg [0][%d]: %f\n", DEBUG_POS, ggml_get_f32_nd(inp_neg, 0, DEBUG_POS, 0, 0));
// TODO is this the best way to get dimension? i don't know which way n_embd/n_tokens go
// for that matter can we get rid of n_embd/n_tokens fields in favor of ne[0]/ne[1]?
struct ggml_tensor * dest = ggml_new_tensor_2d(ctx_data, GGML_TYPE_F32, inp_pos->ne[0], inp_pos->ne[1]);
for (size_t i = 0; i < cb_data.n_embd; i++) {
for (size_t j = 0; j < cb_data.n_tokens; j++) {
ggml_set_f32_nd(dest, i, j, 0, 0, ggml_get_f32_nd(inp_pos, i, j, 0, 0) - ggml_get_f32_nd(inp_neg, i, j, 0, 0));
}
}
printf("dest [0][%d]: %f\n", DEBUG_POS, ggml_get_f32_nd(dest, 0, DEBUG_POS, 0, 0));
// TODO can we make this faster? like check during the above operation rather than on a second pass?
std::cout << "vec size: " << vec.size() << std::endl;
// strip zero rows
std::vector<int> nonzero_rows;
for (int i = 0; i < cb_data.n_tokens; ++i) {
if (!is_row_all_zeros(dest, i, cb_data.n_embd)) {
nonzero_rows.push_back(i);
int n_nonzero_rows = 0;
std::vector<std::vector<int>> nonzero_rows; // outer vector is tensor idx, inner vector is row in tensor
nonzero_rows.resize(vec.size());
for (int i = 0; i < vec.size(); ++i) {
for (int j = 0; j < vec[i]->ne[1]; ++j) {
if (!is_row_all_zeros(vec[i], j, vec[i]->ne[0])) {
nonzero_rows[i].push_back(j);
n_nonzero_rows++;
}
}
}
/* debug
if(cb_data.n_tokens != nonzero_rows.size()) {
std::cout << "original n_tokens: " << cb_data.n_tokens << std::endl;
std::cout << "zero rows in layer " << il << ": " << cb_data.n_tokens - nonzero_rows.size() << std::endl;
} */
std::cout << "n_nonzero_rows: " << n_nonzero_rows << std::endl;
struct ggml_init_params params2 = {
/*.mem_size =*/ inp_pos->ne[0] * nonzero_rows.size() * sizeof(float) + ggml_tensor_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ false,
};
struct ggml_context * ctx_data2 = ggml_init(params);
// we transpose it here because ggml mul_mat is really weird
struct ggml_tensor * diff = ggml_new_tensor_2d(dctx.ctx_diff, GGML_TYPE_F32, n_nonzero_rows, dctx.n_embd);
struct ggml_tensor * diff = ggml_new_tensor_2d(ctx_data2, GGML_TYPE_F32, inp_pos->ne[0], nonzero_rows.size());
diff->data = malloc(dctx.n_embd * n_nonzero_rows * sizeof(float) + ggml_tensor_overhead()); // @ngxson get rid of this malloc somehow
//size_t offset = 0;
for (size_t i = 0; i < nonzero_rows.size(); ++i) {
// probably eschew this in favor of the iterative method?
//float * origin = (float *)(dest->data) + nonzero_rows[i] * cb_data.n_embd;
//memcpy((float *)(diff->data) + offset, origin, cb_data.n_embd * sizeof(float));
//offset += cb_data.n_embd;
for (size_t j = 0; j < cb_data.n_embd; j++) {
ggml_set_f32_nd(diff, j, i, 0, 0, ggml_get_f32_nd(dest, j, nonzero_rows[i], 0, 0));
for (size_t j : nonzero_rows[i]) {
for (size_t k = 0; k < vec[i]->ne[0]; k++) {
//std::cout << ggml_get_f32_nd(vec[i], k, j, 0, 0) << std::endl;
ggml_set_f32_nd(diff, i, k, 0, 0, ggml_get_f32_nd(vec[i], k, j, 0, 0));
}
}
}
// FIXME ggml_nbytes(diff) is 0
printf("diff[0][1]: %f\n", ggml_get_f32_nd(diff, 0, 1, 0, 0));
cb_data.v_diffs_wrapped[il].push_back(diff);
ggml_free(ctx_data);
ggml_free(ctx_data2);
// TODO assert row == n_nonzero_rows
dctx.v_diff.push_back(diff);
}
ggml_free(dctx.ctx_diffs_wrapped);
}
static void concatenate_diffs(callback_data & cb_data) {
printf("concatenate_diffs\n");
for (size_t i = 0; i < cb_data.v_diffs_wrapped.size(); ++i) {
std::vector<struct ggml_tensor *> & vec = cb_data.v_diffs_wrapped[i];
size_t n_rows_total = 0;
for (size_t j = 0; j < vec.size(); ++j) {
n_rows_total += vec[j]->ne[1];
}
struct ggml_init_params params = {
/*.mem_size =*/ cb_data.n_embd * n_rows_total * sizeof(float) + ggml_tensor_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ false,
};
struct ggml_context * ctx_data = ggml_init(params);
// std::cout << "n_rows_total: " << n_rows_total << std::endl;
struct ggml_tensor * diff = ggml_new_tensor_2d(ctx_data, GGML_TYPE_F32, cb_data.n_embd, n_rows_total);
size_t offset = 0;
for (size_t j = 0; j < vec.size(); ++j) {
float * origin = (float *)(vec[j]->data);
memcpy((float *)(diff->data) + offset, origin, vec[j]->ne[1] * cb_data.n_embd * sizeof(float));
offset += vec[j]->ne[0] * cb_data.n_embd;
}
cb_data.v_diff.push_back(diff);
}
}
// TODO translate everything below this
// TODO make sure to free everything in a timely manner
struct pca_model {
struct ggml_tensor * v_diff_original;
struct ggml_tensor * square;
struct ggml_tensor * square_transpose;
struct ggml_tensor * eigenvector;
ggml_backend_t backend = NULL;
@ -452,7 +450,7 @@ struct pca_model {
struct ggml_context * ctx;
};
void load_pca_model(pca_model & model, struct ggml_tensor * v_diff_original, const int n_embd) {
void load_pca_model(pca_model & model, struct ggml_tensor * v_diff_original) {
#ifdef GGML_USE_CUDA
fprintf(stderr, "%s: using CUDA backend\n", __func__);
model.backend = ggml_backend_cuda_init(0); // init device 0
@ -474,8 +472,10 @@ void load_pca_model(pca_model & model, struct ggml_tensor * v_diff_original, con
if (!model.backend) {
model.backend = ggml_backend_cpu_init();
}
printf("v_diff_original[0][1]: %f\n", ggml_get_f32_nd(v_diff_original, 0, 1, 0, 0));
const int num_tensors = 3;
const int num_tensors = 4;
struct ggml_init_params params {
/*.mem_size =*/ ggml_tensor_overhead() * num_tensors,
@ -486,19 +486,21 @@ void load_pca_model(pca_model & model, struct ggml_tensor * v_diff_original, con
model.ctx = ggml_init(params);
model.v_diff_original = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, v_diff_original->ne[0], v_diff_original->ne[1]);
model.square = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, n_embd, n_embd);
model.eigenvector = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, n_embd);
model.square = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, v_diff_original->ne[1], v_diff_original->ne[1]);
model.square_transpose = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, v_diff_original->ne[1], v_diff_original->ne[1]);
model.eigenvector = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, v_diff_original->ne[1]);
model.buffer = ggml_backend_alloc_ctx_tensors(model.ctx, model.backend);
ggml_backend_tensor_set(model.v_diff_original, v_diff_original->data, 0, ggml_nbytes(v_diff_original));
// no need to load anything into square yet
// no need to load anything into square or square_transpose yet
// initialize model.eigenvector to random vector
std::vector<float> random_vec = std::vector<float>();
std::vector<float> random_vec;
std::default_random_engine generator(static_cast<unsigned int>(std::time(0)));
std::uniform_real_distribution<float> distribution(0.0, 1.0);
for (int i = 0; i < n_embd; ++i) {
for (int i = 0; i < v_diff_original->ne[1]; ++i) {
random_vec.push_back(distribution(generator));
}
@ -519,6 +521,7 @@ struct ggml_cgraph * square_diff_graph(const pca_model & model) {
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
struct ggml_tensor * square = ggml_mul_mat(ctx0, model.v_diff_original, model.v_diff_original);
//struct ggml_tensor * square_transpose = ggml_transpose(ctx0, square);
ggml_build_forward_expand(gf, square);
@ -526,6 +529,7 @@ struct ggml_cgraph * square_diff_graph(const pca_model & model) {
return gf;
}
// TODO do this before pca so the pca_model is easier to malloc?
struct ggml_tensor * compute_square(const pca_model & model, ggml_gallocr_t allocr, int n_threads) {
struct ggml_cgraph * gf = square_diff_graph(model);
@ -589,51 +593,44 @@ struct ggml_tensor * compute_piter(const pca_model & model, ggml_gallocr_t alloc
return gf->nodes[gf->n_nodes - 1];
}
static void power_iteration(callback_data & cb_data, int idx, int n_threads, int maxIterations = 1000, float tolerance = 1e-7) {
static void power_iteration(diff_ctx & dctx, int idx, int maxIterations = 1000, float tolerance = 1e-7) {
printf("in power iteration\n");
pca_model model;
load_pca_model(model, cb_data.v_diff[idx], cb_data.n_embd);
load_pca_model(model, dctx.v_diff[idx]);
std::cout << "model.v_diff_original->ne[0]: " << model.v_diff_original->ne[0] << std::endl;
ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(model.backend));
// FIXME ggml_nbytes(square) is 0 because everything going back to diff in calc_diff is 0
struct ggml_tensor * square = compute_square(model, allocr, n_threads);
ggml_backend_tensor_set(model.square, square->data, 0, ggml_nbytes(square));
struct ggml_tensor * square = compute_square(model, allocr, dctx.n_threads);
ggml_backend_tensor_set(model.square, square->data, 0, ggml_nbytes(model.square));
// yes?
ggml_gallocr_free(allocr);
struct ggml_init_params host_params = {
/*.mem_size =*/ (dctx.n_embd * sizeof(float) + ggml_tensor_overhead()) * 2u,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ false,
};
struct ggml_context * host_ctx = ggml_init(host_params);
struct ggml_tensor * host_old_eigenvector = ggml_new_tensor_1d(host_ctx, GGML_TYPE_F32, dctx.n_embd);
struct ggml_tensor * host_new_eigenvector = ggml_new_tensor_1d(host_ctx, GGML_TYPE_F32, dctx.n_embd);
for (int iter = 0; iter < maxIterations; ++iter) {
// TODO do I need to reset it like this every time?
allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(model.backend));
// i have no idea how ggml_contexts work so i'm making a different one for the original and the old one
struct ggml_init_params hov_params = {
/*.mem_size =*/ cb_data.n_embd * sizeof(float) + ggml_tensor_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ false,
};
struct ggml_context * hov_ctx = ggml_init(hov_params);
struct ggml_init_params hnv_params = {
/*.mem_size =*/ cb_data.n_embd * sizeof(float) + ggml_tensor_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ false,
};
struct ggml_context * hnv_ctx = ggml_init(hnv_params);
struct ggml_tensor * host_old_eigenvector = ggml_new_tensor_1d(hov_ctx, GGML_TYPE_F32, cb_data.n_embd);
struct ggml_tensor * host_new_eigenvector = ggml_new_tensor_1d(hnv_ctx, GGML_TYPE_F32, cb_data.n_embd);
struct ggml_tensor * b_tensor = compute_piter(model, allocr, n_threads, tolerance);
struct ggml_tensor * b_tensor = compute_piter(model, allocr, dctx.n_threads, tolerance);
ggml_backend_tensor_get(b_tensor, host_new_eigenvector->data, 0, ggml_nbytes(b_tensor));
ggml_backend_tensor_get(model.eigenvector, host_old_eigenvector->data, 0, ggml_nbytes(model.eigenvector));
// convergence check
float diff = 0.0;
for (int i = 0; i < cb_data.n_embd; ++i) {
for (int i = 0; i < dctx.n_embd; ++i) {
diff += std::pow((ggml_get_f32_1d(host_new_eigenvector, i) - ggml_get_f32_1d(host_old_eigenvector, i)), 2);
}
@ -649,36 +646,29 @@ static void power_iteration(callback_data & cb_data, int idx, int n_threads, int
// catch division by zero I guess
break;
}
ggml_free(hnv_ctx);
}
ggml_backend_tensor_get(model.eigenvector, cb_data.v_final[idx]->data, 0, ggml_nbytes(model.eigenvector));
ggml_backend_tensor_get(model.eigenvector, dctx.v_final[idx]->data, 0, ggml_nbytes(model.eigenvector));
ggml_gallocr_free(allocr);
ggml_free(host_ctx);
ggml_free(model.ctx);
ggml_backend_buffer_free(model.buffer);
ggml_backend_free(model.backend);
}
static void pca(callback_data & cb_data, int n_threads) {
static void pca(diff_ctx & dctx) {
printf("Running PCA...\n");
for (int il = 0; il < cb_data.v_diff.size(); ++il) {
struct ggml_init_params params = {
/*.mem_size =*/ cb_data.n_embd * sizeof(float) + ggml_tensor_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ false,
};
struct ggml_context * ctx_data = ggml_init(params);
cb_data.v_final.push_back(ggml_new_tensor_1d(ctx_data, GGML_TYPE_F32, cb_data.n_embd));
power_iteration(cb_data, il, n_threads);
for (int il = 0; il < dctx.v_diff.size(); ++il) {
dctx.v_final.push_back(ggml_new_tensor_1d(dctx.ctx_final, GGML_TYPE_F32, dctx.n_embd));
power_iteration(dctx, il);
printf("Done with layer %d\n", il);
printf("il = %d | %f %f \n", il, ggml_get_f32_1d(cb_data.v_final[il], 0), ggml_get_f32_1d(cb_data.v_final[il], 1));
printf("il = %d | %f %f \n", il, ggml_get_f32_1d(dctx.v_final[il], 0), ggml_get_f32_1d(dctx.v_final[il], 1));
}
printf("Done with PCA.\n");
}
static void export_gguf(callback_data & cb_data, int n_layers, const std::string fname, const std::string model_hint) {
static void export_gguf(diff_ctx & dctx, int n_layers, const std::string fname, const std::string model_hint) {
struct gguf_context * ctx = gguf_init_empty();
size_t v_final_size_eff = n_layers - 1;
@ -693,9 +683,11 @@ static void export_gguf(callback_data & cb_data, int n_layers, const std::string
// i'm pretty sure it's right now
const std::string name = "direction." + to_string(i+1);
ggml_set_name(cb_data.v_final[i], name.c_str());
std::cout << "dctx.v_final[i][0][1]: " << ggml_get_f32_nd(dctx.v_final[i], 0, 1, 0, 0) << std::endl;
gguf_add_tensor(ctx, cb_data.v_final[i]);
ggml_set_name(dctx.v_final[i], name.c_str());
gguf_add_tensor(ctx, dctx.v_final[i]);
printf("Added tensor %zu\n", i);
}
@ -752,17 +744,47 @@ int main(int argc, char ** argv) {
int n_ctx = llama_n_ctx(ctx);
int n_layers = llama_n_layer(model);
int n_embd = llama_n_embd(model);
cb_data.n_embd = n_embd;
int n_prompts = cparams.positive_prompts.size();
// init ctx_ggml
struct ggml_init_params params_ggml = {
/*.mem_size =*/ ggml_tensor_overhead() * n_prompts * n_layers * 4u,
/*.mem_size =*/ ggml_tensor_overhead() * n_layers * 2u,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
cb_data.ctx_ggml = ggml_init(params_ggml);
// init diff_ctx
diff_ctx dctx;
// FIXME FIXME FIXME we are running out of memory here
// n_prompts should really be n_tokens damnit - remove the 2u and adapt
// we will either have to pretokenize everything so we know how much memory to allocate
// or allocate the tensor overhead as we go
struct ggml_init_params params_diffs_wrapped = {
/*.mem_size =*/ ggml_tensor_overhead() * n_prompts * n_layers * 16u,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
// this we know how much overhead to allocate in advance
struct ggml_init_params params_diff = {
/*.mem_size =*/ ggml_tensor_overhead() * n_layers,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
// and this we know exactly how much memory to allocate in advance without malloc() hacks
struct ggml_init_params params_final = {
/*.mem_size =*/ n_embd * sizeof(float) * n_layers
+ ggml_tensor_overhead() * n_layers,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ false,
};
dctx.n_embd = n_embd;
dctx.n_threads = cparams.n_threads;
dctx.ctx_diffs_wrapped = ggml_init(params_diffs_wrapped);
dctx.ctx_diff = ggml_init(params_diff);
dctx.ctx_final = ggml_init(params_final);
// create templated prompts
for (int i = 0; i < n_prompts; ++i) {
populate_entries(cparams, cparams.positive_prompts[i], cparams.negative_prompts[i]);
@ -804,24 +826,33 @@ int main(int argc, char ** argv) {
cb_data.is_eval_pos = false;
get_hidden_layers(ctx, tokens_neg);
// TODO check whether the same tokens correspond to zero rows because we don't seem to be getting many zero rows anymore
// we get a lot of zero rows for the first few prompts and then they drop off
// likewise most of the zero rows are in the first few layers for each prompt
calc_diff(cb_data);
calc_diff(cb_data, dctx);
// reset for next iteration
// TODO @ngxson : find a more proper way to alloc / free tensors
for (auto ptr : cb_data.v_pos) free(ptr->data);
for (auto ptr : cb_data.v_neg) free(ptr->data);
ggml_free(cb_data.ctx_ggml);
// TODO move this to the top of the loop and remove the ggml_free() outside
cb_data.ctx_ggml = ggml_init(params_ggml);
cb_data.v_pos.clear();
cb_data.v_neg.clear();
}
// TODO we can actually delete cb_data here
//ggml_free(cb_data.ctx_ggml);
printf("dctx.v_diffs_wrapped[0][0][2]: %f\n", ggml_get_f32_nd(dctx.v_diffs_wrapped[0][0], 0, 2, 0, 0));
printf("Done evaluate prompts\n");
concatenate_diffs(cb_data);
pca(cb_data, cparams.n_threads);
concatenate_diffs(dctx);
printf("dctx.v_diff[0][0][1]: %f\n", ggml_get_f32_nd(dctx.v_diff[0], 0, 1, 0, 0));
printf("Done concatenate diffs\n");
// code is known to work up to here
pca(dctx);
//printf("v_final %f %f \n", cb_data.v_final[0][0], cb_data.v_final[0][1]);
llama_free(ctx);
@ -831,9 +862,14 @@ int main(int argc, char ** argv) {
// we need get_arch_name() from llama.cpp
// TODO also has support been implemeneted for arches other than llama yet? see #5970
std::string model_hint = "llama";
export_gguf(cb_data, n_layers, cparams.outfile, model_hint);
export_gguf(dctx, n_layers, cparams.outfile, model_hint);
llama_backend_free();
std::cout << "okay which of you is failing" << std::endl;
// TODO free(): invalid pointer after the entire program is done????????
// probably because destructors free after you've already manually freed
// TODO fix destructor/ggml_free positioning
return 0;
}