mii/llama.cpp
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mii
2023-03-16 12:50:30 +00:00
parent ac15de7895
commit 8c91c48552
2 changed files with 25170 additions and 496 deletions
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@ -11,6 +11,9 @@
#include <string>
#include <vector>
#include <crow.h>
#include <json.hpp>
#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__))
#include <signal.h>
#include <unistd.h>
@ -86,8 +89,10 @@ struct llama_model {
};
// load the model's weights from a file
bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab & vocab, int n_ctx) {
fprintf(stderr, "%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
bool llama_model_load(const std::string &fname, llama_model &model,
gpt_vocab &vocab, int n_ctx) {
fprintf(stderr, "%s: loading model from '%s' - please wait ...\n", __func__,
fname.c_str());
std::vector<char> f_buf(1024 * 1024);
@ -103,7 +108,8 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
uint32_t magic;
fin.read((char *)&magic, sizeof(magic));
if (magic != 0x67676d6c) {
fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__,
fname.c_str());
return false;
}
}
@ -126,7 +132,9 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
hparams.n_ctx = n_ctx;
n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
n_ff =
((2 * (4 * hparams.n_embd) / 3 + hparams.n_mult - 1) / hparams.n_mult) *
hparams.n_mult;
n_parts = LLAMA_N_PARTS.at(hparams.n_embd);
fprintf(stderr, "%s: n_vocab = %d\n", __func__, hparams.n_vocab);
@ -163,21 +171,29 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
vocab.id_to_token[i] = word;
// if (i < 30000) {
// fprintf(stderr, "%s: vocab[%d] = '%s'\n", __func__, i, word.c_str());
// fprintf(stderr, "%s: vocab[%d] = '%s'\n", __func__, i,
// word.c_str());
//}
}
}
// for the big tensors, we have the option to store the data in 16-bit floats or quantized
// in order to save memory and also to speed up the computation
// for the big tensors, we have the option to store the data in 16-bit floats
// or quantized in order to save memory and also to speed up the computation
ggml_type wtype = GGML_TYPE_COUNT;
switch (model.hparams.f16) {
case 0: wtype = GGML_TYPE_F32; break;
case 1: wtype = GGML_TYPE_F16; break;
case 2: wtype = GGML_TYPE_Q4_0; break;
case 3: wtype = GGML_TYPE_Q4_1; break;
default:
{
case 0:
wtype = GGML_TYPE_F32;
break;
case 1:
wtype = GGML_TYPE_F16;
break;
case 2:
wtype = GGML_TYPE_Q4_0;
break;
case 3:
wtype = GGML_TYPE_Q4_1;
break;
default: {
fprintf(stderr, "%s: invalid model file '%s' (bad f16 value %d)\n",
__func__, fname.c_str(), model.hparams.f16);
return false;
@ -204,7 +220,8 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
ctx_size += n_embd * n_vocab * ggml_type_sizef(wtype); // output
ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // attention_norm
ctx_size +=
n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // attention_norm
ctx_size += n_layer * (n_embd * n_embd * ggml_type_sizef(wtype)); // wq
ctx_size += n_layer * (n_embd * n_embd * ggml_type_sizef(wtype)); // wk
@ -217,12 +234,15 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
ctx_size += n_layer * (n_ff * n_embd * ggml_type_sizef(wtype)); // w2
ctx_size += n_layer * (n_ff * n_embd * ggml_type_sizef(wtype)); // w3
ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_k
ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v
ctx_size +=
n_ctx * n_layer * n_embd * ggml_type_sizef(GGML_TYPE_F32); // memory_k
ctx_size +=
n_ctx * n_layer * n_embd * ggml_type_sizef(GGML_TYPE_F32); // memory_v
ctx_size += (5 + 10 * n_layer) * 256; // object overhead
fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__,
ctx_size / (1024.0 * 1024.0));
}
// create the ggml context
@ -278,18 +298,27 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
layer.w3 = ggml_new_tensor_2d(ctx, wtype, n_embd, n_ff);
// map by name
model.tensors["layers." + std::to_string(i) + ".attention_norm.weight"] = layer.attention_norm;
model.tensors["layers." + std::to_string(i) + ".attention_norm.weight"] =
layer.attention_norm;
model.tensors["layers." + std::to_string(i) + ".attention.wq.weight"] = layer.wq;
model.tensors["layers." + std::to_string(i) + ".attention.wk.weight"] = layer.wk;
model.tensors["layers." + std::to_string(i) + ".attention.wv.weight"] = layer.wv;
model.tensors["layers." + std::to_string(i) + ".attention.wo.weight"] = layer.wo;
model.tensors["layers." + std::to_string(i) + ".attention.wq.weight"] =
layer.wq;
model.tensors["layers." + std::to_string(i) + ".attention.wk.weight"] =
layer.wk;
model.tensors["layers." + std::to_string(i) + ".attention.wv.weight"] =
layer.wv;
model.tensors["layers." + std::to_string(i) + ".attention.wo.weight"] =
layer.wo;
model.tensors["layers." + std::to_string(i) + ".ffn_norm.weight"] = layer.ffn_norm;
model.tensors["layers." + std::to_string(i) + ".ffn_norm.weight"] =
layer.ffn_norm;
model.tensors["layers." + std::to_string(i) + ".feed_forward.w1.weight"] = layer.w1;
model.tensors["layers." + std::to_string(i) + ".feed_forward.w2.weight"] = layer.w2;
model.tensors["layers." + std::to_string(i) + ".feed_forward.w3.weight"] = layer.w3;
model.tensors["layers." + std::to_string(i) + ".feed_forward.w1.weight"] =
layer.w1;
model.tensors["layers." + std::to_string(i) + ".feed_forward.w2.weight"] =
layer.w2;
model.tensors["layers." + std::to_string(i) + ".feed_forward.w3.weight"] =
layer.w3;
}
}
@ -307,9 +336,11 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
const size_t memory_size =
ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
fprintf(stderr, "%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
fprintf(stderr, "%s: memory_size = %8.2f MB, n_mem = %d\n", __func__,
memory_size / 1024.0 / 1024.0, n_mem);
}
const size_t file_offset = fin.tellg();
@ -327,7 +358,8 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
fname_part += "." + std::to_string(i);
}
fprintf(stderr, "%s: loading model part %d/%d from '%s'\n", __func__, i+1, n_parts, fname_part.c_str());
fprintf(stderr, "%s: loading model part %d/%d from '%s'\n", __func__, i + 1,
n_parts, fname_part.c_str());
fin = std::ifstream(fname_part, std::ios::binary);
fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
@ -364,7 +396,8 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
fin.read(&name[0], length);
if (model.tensors.find(name.data()) == model.tensors.end()) {
fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__,
name.data());
return false;
}
@ -404,61 +437,92 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
if (n_dims == 1) {
if (ggml_nelements(tensor) != nelements) {
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n",
__func__, name.data());
return false;
}
} else {
if (ggml_nelements(tensor) / n_parts != nelements) {
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n",
__func__, name.data());
return false;
}
}
if (n_dims == 1) {
if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
__func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
fprintf(stderr,
"%s: tensor '%s' has wrong shape in model file: got [%d, "
"%d], expected [%d, %d]\n",
__func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0],
ne[1]);
return false;
}
} else {
if (split_type == 0) {
if (tensor->ne[0] / n_parts != ne[0] || tensor->ne[1] != ne[1]) {
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
__func__, name.data(), tensor->ne[0]/n_parts, tensor->ne[1], ne[0], ne[1]);
fprintf(stderr,
"%s: tensor '%s' has wrong shape in model file: got [%d, "
"%d], expected [%d, %d]\n",
__func__, name.data(), tensor->ne[0] / n_parts,
tensor->ne[1], ne[0], ne[1]);
return false;
}
} else {
if (tensor->ne[0] != ne[0] || tensor->ne[1] / n_parts != ne[1]) {
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
__func__, name.data(), tensor->ne[0], tensor->ne[1]/n_parts, ne[0], ne[1]);
fprintf(stderr,
"%s: tensor '%s' has wrong shape in model file: got [%d, "
"%d], expected [%d, %d]\n",
__func__, name.data(), tensor->ne[0],
tensor->ne[1] / n_parts, ne[0], ne[1]);
return false;
}
}
}
if (0) {
static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
fprintf(stderr, "%24s - [%5d, %5d], type = %6s, split = %d\n", name.data(), ne[0], ne[1], ftype_str[ftype], split_type);
static const char *ftype_str[] = {
"f32",
"f16",
"q4_0",
"q4_1",
};
fprintf(stderr, "%24s - [%5d, %5d], type = %6s, split = %d\n",
name.data(), ne[0], ne[1], ftype_str[ftype], split_type);
}
size_t bpe = 0;
switch (ftype) {
case 0: bpe = ggml_type_size(GGML_TYPE_F32); break;
case 1: bpe = ggml_type_size(GGML_TYPE_F16); break;
case 2: bpe = ggml_type_size(GGML_TYPE_Q4_0); assert(ne[0] % 64 == 0); break;
case 3: bpe = ggml_type_size(GGML_TYPE_Q4_1); assert(ne[0] % 64 == 0); break;
default:
{
fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype);
case 0:
bpe = ggml_type_size(GGML_TYPE_F32);
break;
case 1:
bpe = ggml_type_size(GGML_TYPE_F16);
break;
case 2:
bpe = ggml_type_size(GGML_TYPE_Q4_0);
assert(ne[0] % 64 == 0);
break;
case 3:
bpe = ggml_type_size(GGML_TYPE_Q4_1);
assert(ne[0] % 64 == 0);
break;
default: {
fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__,
ftype);
return false;
}
};
if (n_dims == 1 || n_parts == 1) {
if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
__func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
if ((nelements * bpe) / ggml_blck_size(tensor->type) !=
ggml_nbytes(tensor)) {
fprintf(stderr,
"%s: tensor '%s' has wrong size in model file: got %zu, "
"expected %zu\n",
__func__, name.data(), ggml_nbytes(tensor),
nelements * bpe);
return false;
}
@ -470,27 +534,38 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
total_size += ggml_nbytes(tensor);
} else {
if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)/n_parts) {
fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
__func__, name.data(), ggml_nbytes(tensor)/n_parts, nelements*bpe);
if ((nelements * bpe) / ggml_blck_size(tensor->type) !=
ggml_nbytes(tensor) / n_parts) {
fprintf(stderr,
"%s: tensor '%s' has wrong size in model file: got %zu, "
"expected %zu\n",
__func__, name.data(), ggml_nbytes(tensor) / n_parts,
nelements * bpe);
return false;
}
if (split_type == 0) {
const int np0 = ne[0];
const size_t row_size = (tensor->ne[0]/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
const size_t row_size =
(tensor->ne[0] / ggml_blck_size(tensor->type)) *
ggml_type_size(tensor->type);
assert(row_size == tensor->nb[1]);
for (int i1 = 0; i1 < ne[1]; ++i1) {
const size_t offset_row = i1 * row_size;
const size_t offset = offset_row + ((part_id*np0)/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
const size_t offset =
offset_row +
((part_id * np0) / ggml_blck_size(tensor->type)) *
ggml_type_size(tensor->type);
fin.read(reinterpret_cast<char *>(tensor->data) + offset, row_size / n_parts);
}
} else {
const int np1 = ne[1];
const size_t row_size = (tensor->ne[0]/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
const size_t row_size =
(tensor->ne[0] / ggml_blck_size(tensor->type)) *
ggml_type_size(tensor->type);
for (int i1 = 0; i1 < ne[1]; ++i1) {
const size_t offset_row = (i1 + part_id * np1) * row_size;
@ -501,7 +576,9 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
total_size += ggml_nbytes(tensor) / n_parts;
}
//fprintf(stderr, "%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
// fprintf(stderr, "%42s - [%5d, %5d], type = %6s, %6.2f MB\n",
// name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16",
// ggml_nbytes(tensor)/1024.0/1024.0);
if (++n_tensors % 8 == 0) {
fprintf(stderr, ".");
fflush(stderr);
@ -510,7 +587,8 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
fprintf(stderr, " done\n");
fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n",
__func__, total_size / 1024.0 / 1024.0, n_tensors);
}
fin.close();
@ -529,13 +607,9 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
//
// The GPT-J model requires about 16MB of memory per input token.
//
bool llama_eval(
const llama_model & model,
const int n_threads,
const int n_past,
bool llama_eval(const llama_model &model, const int n_threads, const int n_past,
const std::vector<gpt_vocab::id> &embd_inp,
std::vector<float> & embd_w,
size_t & mem_per_token) {
std::vector<float> &embd_w, size_t &mem_per_token) {
const int N = embd_inp.size();
const auto &hparams = model.hparams;
@ -549,14 +623,18 @@ bool llama_eval(
const int d_key = n_embd / n_head;
// TODO: check if this size scales with n_ctx linearly and remove constant. somehow I feel it wasn't the case
// TODO: check if this size scales with n_ctx linearly and remove constant.
// somehow I feel it wasn't the case
// static size_t buf_size = hparams.n_ctx*1024*1024;
static size_t buf_size = 512u * 1024 * 1024;
static void *buf = malloc(buf_size);
if (mem_per_token > 0 && mem_per_token * N > buf_size) {
const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
//fprintf(stderr, "\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
const size_t buf_size_new =
1.1 *
(mem_per_token * N); // add 10% to account for ggml object overhead
// fprintf(stderr, "\n%s: reallocating buffer from %zu to %zu bytes\n",
// __func__, buf_size, buf_size_new);
// reallocate
buf_size = buf_size_new;
@ -591,9 +669,8 @@ bool llama_eval(
cur = ggml_rms_norm(ctx0, inpL);
// cur = attention_norm*cur
cur = ggml_mul(ctx0,
ggml_repeat(ctx0, model.layers[il].attention_norm, cur),
cur);
cur = ggml_mul(
ctx0, ggml_repeat(ctx0, model.layers[il].attention_norm, cur), cur);
}
// self-attention
@ -604,42 +681,31 @@ bool llama_eval(
// store key and value to memory
if (N >= 1) {
struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
struct ggml_tensor * v = ggml_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past));
struct ggml_tensor *k =
ggml_view_1d(ctx0, model.memory_k, N * n_embd,
(ggml_element_size(model.memory_k) * n_embd) *
(il * n_ctx + n_past));
struct ggml_tensor *v =
ggml_view_1d(ctx0, model.memory_v, N * n_embd,
(ggml_element_size(model.memory_v) * n_embd) *
(il * n_ctx + n_past));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
}
// Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
struct ggml_tensor * Q =
ggml_permute(ctx0,
ggml_rope(ctx0,
ggml_cpy(ctx0,
Qcur,
ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),
n_past, n_rot, 0),
0, 2, 1, 3);
// Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1,
// 3)
struct ggml_tensor *Q = ggml_permute(ctx0, ggml_rope(ctx0, ggml_cpy(ctx0, Qcur, ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd / n_head, n_head, N)), n_past, n_rot, 0), 0, 2, 1, 3);
// K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
struct ggml_tensor * K =
ggml_permute(ctx0,
ggml_rope(ctx0,
ggml_reshape_3d(ctx0,
ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd),
n_embd/n_head, n_head, n_past + N),
n_past, n_rot, 1),
0, 2, 1, 3);
struct ggml_tensor *K = ggml_permute(ctx0, ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_view_1d(ctx0, model.memory_k, (n_past + N) * n_embd, il * n_ctx * ggml_element_size(model.memory_k) * n_embd), n_embd / n_head, n_head, n_past + N), n_past, n_rot, 1), 0, 2, 1, 3);
// K * Q
struct ggml_tensor *KQ = ggml_mul_mat(ctx0, K, Q);
// KQ_scaled = KQ / sqrt(n_embd/n_head)
struct ggml_tensor * KQ_scaled =
ggml_scale(ctx0,
KQ,
ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
);
struct ggml_tensor *KQ_scaled = ggml_scale(ctx0, KQ, ggml_new_f32(ctx0, 1.0f / sqrt(float(n_embd) / n_head)));
// KQ_masked = mask_past(KQ_scaled)
struct ggml_tensor *KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
@ -647,10 +713,12 @@ bool llama_eval(
// KQ = soft_max(KQ_masked)
struct ggml_tensor *KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
struct ggml_tensor * V_trans =
ggml_permute(ctx0,
ggml_reshape_3d(ctx0,
// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0,
// 3).contiguous()
struct ggml_tensor *V_trans = ggml_permute(
ctx0,
ggml_reshape_3d(
ctx0,
ggml_view_1d(ctx0, model.memory_v, (n_past + N) * n_embd, il * n_ctx * ggml_element_size(model.memory_v) * n_embd),
n_embd / n_head, n_head, n_past + N),
1, 2, 0, 3);
@ -662,14 +730,10 @@ bool llama_eval(
struct ggml_tensor *KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
// cur = KQV_merged.contiguous().view(n_embd, N)
cur = ggml_cpy(ctx0,
KQV_merged,
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
cur = ggml_cpy(ctx0, KQV_merged, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
// projection (no bias)
cur = ggml_mul_mat(ctx0,
model.layers[il].wo,
cur);
cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
}
struct ggml_tensor *inpFF = ggml_add(ctx0, cur, inpSA);
@ -681,28 +745,19 @@ bool llama_eval(
cur = ggml_rms_norm(ctx0, inpFF);
// cur = ffn_norm*cur
cur = ggml_mul(ctx0,
ggml_repeat(ctx0, model.layers[il].ffn_norm, cur),
cur);
cur = ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ffn_norm, cur), cur);
}
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
model.layers[il].w3,
cur);
struct ggml_tensor *tmp = ggml_mul_mat(ctx0, model.layers[il].w3, cur);
cur = ggml_mul_mat(ctx0,
model.layers[il].w1,
cur);
cur = ggml_mul_mat(ctx0, model.layers[il].w1, cur);
// SILU activation
cur = ggml_silu(ctx0, cur);
cur = ggml_mul(ctx0, cur, tmp);
cur = ggml_mul_mat(ctx0,
model.layers[il].w2,
cur);
cur = ggml_mul_mat(ctx0, model.layers[il].w2, cur);
}
cur = ggml_add(ctx0, cur, inpFF);
@ -716,15 +771,11 @@ bool llama_eval(
inpL = ggml_rms_norm(ctx0, inpL);
// inpL = norm*inpL
inpL = ggml_mul(ctx0,
ggml_repeat(ctx0, model.norm, inpL),
inpL);
inpL = ggml_mul(ctx0, ggml_repeat(ctx0, model.norm, inpL), inpL);
}
// lm_head
{
inpL = ggml_mul_mat(ctx0, model.output, inpL);
}
{ inpL = ggml_mul_mat(ctx0, model.output, inpL); }
// logits -> probs
// inpL = ggml_soft_max(ctx0, inpL);
@ -743,7 +794,8 @@ bool llama_eval(
// return result for just the last token
embd_w.resize(n_vocab);
memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
memcpy(embd_w.data(), (float *)ggml_get_data(inpL) + (n_vocab * (N - 1)),
sizeof(float) * n_vocab);
if (mem_per_token == 0) {
mem_per_token = ggml_used_mem(ctx0) / N;
@ -757,7 +809,8 @@ bool llama_eval(
static bool is_interacting = false;
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) || \
defined(_WIN32)
void sigint_handler(int signo) {
printf(ANSI_COLOR_RESET);
if (signo == SIGINT) {
@ -795,7 +848,7 @@ int main(int argc, char ** argv) {
const int64_t t_main_start_us = ggml_time_us();
gpt_params params;
params.model = "models/llama-7B/ggml-model.bin";
params.model = "models/7B/ggml-model.bin";
if (gpt_params_parse(argc, argv, params) == false) {
return 1;
@ -824,7 +877,8 @@ int main(int argc, char ** argv) {
{
const int64_t t_start_us = ggml_time_us();
if (!llama_model_load(params.model, model, vocab, params.n_ctx)) {
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__,
params.model.c_str());
return 1;
}
@ -834,10 +888,35 @@ int main(int argc, char ** argv) {
// print system information
{
fprintf(stderr, "\n");
fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
fprintf(stderr, "system_info: n_threads = %d / %d | %s\n", params.n_threads,
std::thread::hardware_concurrency(), llama_print_system_info());
}
printf("Initializing crow...");
crow::SimpleApp app;
CROW_ROUTE(app, "/")([&](const crow::request& req) -> std::string {
nlohmann::json json_data = nlohmann::json::parse(req.body);
std::string input = "Transcript of a dialog, where the User interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.\n";
auto messages = json_data["messages"];
for (auto& message : messages) {
std::string role = message["role"];
std::string content = message["content"];
if (role == "user") {
input = input + "\nUser: " + content;
} else {
input = input + "\nBob: " + content;
}
}
input = input + "\nBob: ";
std::string result = "";
int n_past = 0;
int64_t t_sample_us = 0;
@ -846,18 +925,23 @@ int main(int argc, char ** argv) {
std::vector<float> logits;
// tokenize the prompt
std::vector<gpt_vocab::id> embd_inp = ::llama_tokenize(vocab, params.prompt, true);
std::vector<gpt_vocab::id> embd_inp =
::llama_tokenize(vocab, input, true);
params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
params.n_predict =
std::min(params.n_predict, model.hparams.n_ctx - (int)embd_inp.size());
// tokenize the reverse prompt
std::vector<gpt_vocab::id> antiprompt_inp = ::llama_tokenize(vocab, params.antiprompt, false);
std::vector<gpt_vocab::id> antiprompt_inp =
::llama_tokenize(vocab, params.antiprompt, false);
fprintf(stderr, "\n");
fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__,
embd_inp.size());
for (int i = 0; i < (int)embd_inp.size(); i++) {
fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
fprintf(stderr, "%6d -> '%s'\n", embd_inp[i],
vocab.id_to_token.at(embd_inp[i]).c_str());
}
fprintf(stderr, "\n");
if (params.interactive) {
@ -874,15 +958,22 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s: interactive mode on.\n", __func__);
if (antiprompt_inp.size()) {
fprintf(stderr, "%s: reverse prompt: '%s'\n", __func__, params.antiprompt.c_str());
fprintf(stderr, "%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
fprintf(stderr, "%s: reverse prompt: '%s'\n", __func__,
params.antiprompt.c_str());
fprintf(stderr, "%s: number of tokens in reverse prompt = %zu\n",
__func__, antiprompt_inp.size());
for (int i = 0; i < (int)antiprompt_inp.size(); i++) {
fprintf(stderr, "%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).c_str());
fprintf(stderr, "%6d -> '%s'\n", antiprompt_inp[i],
vocab.id_to_token.at(antiprompt_inp[i]).c_str());
}
fprintf(stderr, "\n");
}
}
fprintf(stderr, "sampling parameters: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
fprintf(stderr,
"sampling parameters: temp = %f, top_k = %d, top_p = %f, "
"repeat_last_n = %i, repeat_penalty = %f\n",
params.temp, params.top_k, params.top_p, params.repeat_last_n,
params.repeat_penalty);
fprintf(stderr, "\n\n");
std::vector<gpt_vocab::id> embd;
@ -895,10 +986,11 @@ int main(int argc, char ** argv) {
std::vector<gpt_vocab::id> last_n_tokens(last_n_size);
std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
if (params.interactive) {
fprintf(stderr, "== Running in interactive mode. ==\n"
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
fprintf(stderr,
"== Running in interactive mode. ==\n"
#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) || \
defined(_WIN32)
" - Press Ctrl+C to interject at any time.\n"
#endif
" - Press Return to return control to LLaMa.\n"
@ -924,9 +1016,10 @@ int main(int argc, char ** argv) {
if (embd.size() > 0) {
const int64_t t_start_us = ggml_time_us();
if (!llama_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {
if (!llama_eval(model, params.n_threads, n_past, embd, logits,
mem_per_token)) {
fprintf(stderr, "Failed to predict\n");
return 1;
return std::string("Failed to predict");
}
t_predict_us += ggml_time_us() - t_start_us;
@ -949,7 +1042,9 @@ int main(int argc, char ** argv) {
{
const int64_t t_start_sample_us = ggml_time_us();
id = llama_sample_top_p_top_k(vocab, logits.data() + (logits.size() - n_vocab), last_n_tokens, repeat_penalty, top_k, top_p, temp, rng);
id = llama_sample_top_p_top_k(
vocab, logits.data() + (logits.size() - n_vocab), last_n_tokens,
repeat_penalty, top_k, top_p, temp, rng);
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(id);
@ -962,11 +1057,9 @@ int main(int argc, char ** argv) {
// echo this to console
input_noecho = false;
// decrement remaining sampling budget
--remaining_tokens;
} else {
// some user input remains from prompt or interaction, forward it to processing
// some user input remains from prompt or interaction, forward it to
// processing
while (embd_inp.size() > input_consumed) {
embd.push_back(embd_inp[input_consumed]);
last_n_tokens.erase(last_n_tokens.begin());
@ -978,64 +1071,39 @@ int main(int argc, char ** argv) {
}
// reset color to default if we there is no pending user input
if (!input_noecho && params.use_color && embd_inp.size() == input_consumed) {
if (!input_noecho && params.use_color &&
embd_inp.size() == input_consumed) {
printf(ANSI_COLOR_RESET);
}
}
// display text
if (!input_noecho) {
for (auto id : embd) {
printf("%s", vocab.id_to_token[id].c_str());
}
fflush(stdout);
}
// in interactive mode, and not currently processing queued inputs;
// check if we should prompt the user for more
if (params.interactive && embd_inp.size() <= input_consumed) {
// check for reverse prompt
if (antiprompt_inp.size() && std::equal(antiprompt_inp.rbegin(), antiprompt_inp.rend(), last_n_tokens.rbegin())) {
if (antiprompt_inp.size() &&
std::equal(antiprompt_inp.rbegin(), antiprompt_inp.rend(),
last_n_tokens.rbegin())) {
// reverse prompt found
printf("Reverse prompt found.\n");
result.erase(result.size() - 4);
is_interacting = true;
}
if (is_interacting) {
// currently being interactive
bool another_line=true;
while (another_line) {
fflush(stdout);
char buf[256] = {0};
int n_read;
if(params.use_color) printf(ANSI_BOLD ANSI_COLOR_GREEN);
if (scanf("%255[^\n]%n%*c", buf, &n_read) <= 0) {
// presumable empty line, consume the newline
scanf("%*c");
n_read=0;
}
if(params.use_color) printf(ANSI_COLOR_RESET);
if (n_read > 0 && buf[n_read-1]=='\\') {
another_line = true;
buf[n_read-1] = '\n';
buf[n_read] = 0;
} else {
another_line = false;
buf[n_read] = '\n';
buf[n_read+1] = 0;
}
std::vector<gpt_vocab::id> line_inp = ::llama_tokenize(vocab, buf, false);
embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
remaining_tokens -= line_inp.size();
input_noecho = true; // do not echo this again
}
is_interacting = false;
break;
}
}
// display text
if (!input_noecho) {
for (auto id : embd) {
result = result + vocab.id_to_token[id].c_str();
printf("%s", vocab.id_to_token[id].c_str());
}
fflush(stdout);
}
// end of text token
if (embd.back() == 2) {
fprintf(stderr, " [end of text]\n");
@ -1052,13 +1120,23 @@ int main(int argc, char ** argv) {
const int64_t t_main_end_us = ggml_time_us();
fprintf(stderr, "\n\n");
fprintf(stderr, "%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
fprintf(stderr, "%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
fprintf(stderr, "%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);
fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
fprintf(stderr, "%s: mem per token = %8zu bytes\n", __func__,
mem_per_token);
fprintf(stderr, "%s: load time = %8.2f ms\n", __func__,
t_load_us / 1000.0f);
fprintf(stderr, "%s: sample time = %8.2f ms\n", __func__,
t_sample_us / 1000.0f);
fprintf(stderr, "%s: predict time = %8.2f ms / %.2f ms per token\n",
__func__, t_predict_us / 1000.0f, t_predict_us / 1000.0f / n_past);
fprintf(stderr, "%s: total time = %8.2f ms\n", __func__,
(t_main_end_us - t_main_start_us) / 1000.0f);
}
return result.substr(input.length());
});
app.port(18080).multithreaded().run();
ggml_free(model.ctx);
if (params.use_color) {