add_subdirectory(mnist)
add_subdirectory(gpt-neox)
add_subdirectory(dolly-v2)
+add_subdirectory(replit)
+add_subdirectory(mpt)
add_subdirectory(starcoder)
--- /dev/null
+#
+# mpt
+
+set(TEST_TARGET mpt)
+add_executable(${TEST_TARGET} main.cpp)
+target_link_libraries(${TEST_TARGET} PRIVATE ggml common common-ggml)
+
+#
+# mpt-quantize
+
+set(TEST_TARGET mpt-quantize)
+add_executable(${TEST_TARGET} quantize.cpp)
+target_link_libraries(${TEST_TARGET} PRIVATE ggml common common-ggml)
--- /dev/null
+import sys
+import struct
+import json
+import numpy as np
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import sentencepiece.sentencepiece_model_pb2 as model
+
+if len(sys.argv) < 3:
+ print("Usage: convert-h5-to-ggml.py dir-model [use-f32]\n")
+ print(" ftype == 0 -> float32")
+ print(" ftype == 1 -> float16")
+ sys.exit(1)
+
+
+# output in the same directory as the model
+dir_model = sys.argv[1]
+fname_out = sys.argv[1] + "/ggml-model.bin"
+
+
+with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
+ hparams = json.load(f)
+
+# possible data types
+# ftype == 0 -> float32
+# ftype == 1 -> float16
+#
+# map from ftype to string
+ftype_str = ["f32", "f16"]
+
+ftype = 1
+if len(sys.argv) > 2:
+ ftype = int(sys.argv[2])
+ if ftype < 0 or ftype > 1:
+ print("Invalid ftype: " + str(ftype))
+ sys.exit(1)
+ fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin"
+
+
+tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+ dir_model, low_cpu_mem_usage=True, trust_remote_code=True
+)
+# print (model)
+
+# print(tokenizer.encode('I believe the meaning of life is'))
+
+list_vars = model.state_dict()
+for name in list_vars.keys():
+ print(name, list_vars[name].shape, list_vars[name].dtype)
+
+fout = open(fname_out, "wb")
+
+print(hparams)
+
+fout.write(struct.pack("i", 0x67676D6C)) # magic: ggml in hex
+fout.write(struct.pack("i", hparams["d_model"]))
+fout.write(struct.pack("i", hparams["max_seq_len"]))
+fout.write(struct.pack("i", hparams["n_heads"]))
+fout.write(struct.pack("i", hparams["n_layers"]))
+fout.write(struct.pack("i", hparams["vocab_size"]))
+fout.write(struct.pack("f", hparams["attn_config"]["alibi_bias_max"]))
+fout.write(struct.pack("f", hparams["attn_config"]["clip_qkv"] or 0.0))
+fout.write(struct.pack("i", ftype))
+
+
+# TODO: temporary hack to not deal with implementing the tokenizer
+dot_token = tokenizer.encode(".")[0]
+for i in range(hparams["vocab_size"]):
+ text = tokenizer.decode([dot_token, i]).encode("utf-8")
+ # remove the first byte (it's always '.')
+ text = text[1:]
+ fout.write(struct.pack("i", len(text)))
+ fout.write(text)
+
+for name in list_vars.keys():
+ data = list_vars[name].squeeze().numpy()
+ print("Processing variable: " + name + " with shape: ", data.shape)
+
+ n_dims = len(data.shape)
+
+ # ftype == 0 -> float32, ftype == 1 -> float16
+ ftype_cur = 0
+ if ftype != 0:
+ if name[-7:] == ".weight" and n_dims == 2:
+ print(" Converting to float16")
+ data = data.astype(np.float16)
+ ftype_cur = 1
+ else:
+ print(" Converting to float32")
+ data = data.astype(np.float32)
+ ftype_cur = 0
+ else:
+ if data.dtype != np.float32:
+ print(" Converting to float32")
+ data = data.astype(np.float32)
+ ftype_cur = 0
+
+ # header
+ str = name.encode("utf-8")
+ fout.write(struct.pack("iii", n_dims, len(str), ftype_cur))
+ for i in range(n_dims):
+ fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
+ fout.write(str)
+
+ # data
+ data.tofile(fout)
+
+fout.close()
+
+print("Done. Output file: " + fname_out)
+print("")
--- /dev/null
+#include "ggml/ggml.h"
+
+#include "common-ggml.h"
+#include "common.h"
+
+#include <cassert>
+#include <cmath>
+#include <cstddef>
+#include <cstdio>
+#include <cstring>
+#include <fstream>
+#include <iostream>
+#include <map>
+#include <stdint.h>
+#include <string>
+#include <unistd.h>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+int n_ctx = 4096;
+
+// no defaults for now
+struct mpt_hparams {
+ int32_t d_model = 0;
+ int32_t max_seq_len = 0;
+ int32_t n_heads = 0;
+ int32_t n_layers = 0;
+ int32_t n_vocab = 0;
+ float alibi_bias_max = 0;
+ float clip_qkv = 0;
+ int32_t ftype = 0;
+};
+
+struct mpt_layer {
+ // pre normalization
+ struct ggml_tensor * norm_1_weight;
+
+ // attention
+ struct ggml_tensor * c_attn_wqkv_weight;
+ struct ggml_tensor * c_attn_out_proj_weight;
+
+ // post normalization
+ struct ggml_tensor * norm_2_weight;
+
+ // ff
+ struct ggml_tensor * ffn_up_proj;
+ struct ggml_tensor * ffn_down_proj;
+};
+
+struct mpt_model {
+ mpt_hparams hparams;
+
+ struct ggml_tensor * wte_weight; // position embedding
+ struct ggml_tensor * norm_f_weight; // language model head
+
+ std::vector<mpt_layer> layers;
+
+ // key + value memory
+ struct ggml_tensor * memory_k;
+ struct ggml_tensor * memory_v;
+
+ struct ggml_context * ctx;
+ std::map<std::string, struct ggml_tensor *> tensors;
+};
+
+// load the model's weights from a file
+bool mpt_model_load(const std::string & fname, mpt_model & model, gpt_vocab & vocab) {
+ printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
+
+ auto fin = std::ifstream(fname, std::ios::binary);
+ if (!fin) {
+ fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
+ return false;
+ }
+
+ // verify magic
+ {
+ 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());
+ return false;
+ }
+ }
+
+ // load hparams
+ {
+ auto & hparams = model.hparams;
+
+ fin.read((char *)&hparams.d_model, sizeof(hparams.d_model));
+ fin.read((char *)&hparams.max_seq_len, sizeof(hparams.max_seq_len));
+ fin.read((char *)&hparams.n_heads, sizeof(hparams.n_heads));
+ fin.read((char *)&hparams.n_layers, sizeof(hparams.n_layers));
+ fin.read((char *)&hparams.n_vocab, sizeof(hparams.n_vocab));
+ fin.read((char *)&hparams.alibi_bias_max, sizeof(hparams.alibi_bias_max));
+ fin.read((char *)&hparams.clip_qkv, sizeof(hparams.clip_qkv));
+ fin.read((char *)&hparams.ftype, sizeof(hparams.ftype));
+
+ printf("%s: d_model = %d\n", __func__, hparams.d_model);
+ printf("%s: max_seq_len = %d\n", __func__, hparams.max_seq_len);
+ printf("%s: n_heads = %d\n", __func__, hparams.n_heads);
+ printf("%s: n_layers = %d\n", __func__, hparams.n_layers);
+ printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
+ printf("%s: alibi_bias_max = %f\n", __func__, hparams.alibi_bias_max);
+ printf("%s: clip_qkv = %f\n", __func__, hparams.clip_qkv);
+ printf("%s: ftype = %d\n", __func__, hparams.ftype);
+ }
+
+ // load vocab
+ {
+ int32_t n_vocab = model.hparams.n_vocab;
+
+ std::string word;
+ for (int i = 0; i < n_vocab; i++) {
+ uint32_t len;
+ fin.read((char *)&len, sizeof(len));
+
+ word.resize(len);
+ fin.read((char *)word.data(), len);
+
+ vocab.token_to_id[word] = i;
+ vocab.id_to_token[i] = word;
+ }
+ }
+
+ // 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_ftype_to_ggml_type((ggml_ftype)(model.hparams.ftype));
+ if (wtype == GGML_TYPE_COUNT) {
+ fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n", __func__, fname.c_str(),
+ model.hparams.ftype);
+ return false;
+ }
+
+ auto & ctx = model.ctx;
+
+ size_t ctx_size = 0;
+
+ {
+ const auto & hparams = model.hparams;
+
+ const size_t n_embd = hparams.d_model;
+ const size_t n_layer = hparams.n_layers;
+ const size_t n_vocab = hparams.n_vocab;
+
+ ctx_size += n_embd * n_vocab * ggml_type_sizef(wtype); // wte_weight
+ ctx_size += n_embd * ggml_type_sizef(GGML_TYPE_F32); // norm_f_weight
+
+ ctx_size += n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // ln_1_weight
+ ctx_size += n_layer * (3 * n_embd * n_embd * ggml_type_sizef(wtype)); // attn_Wqkv_weight
+ ctx_size += n_layer * (n_embd * n_embd * ggml_type_sizef(wtype)); // attn_out_proj_weight
+ ctx_size += n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // ln_2_weight
+ ctx_size += n_layer * (4 * n_embd * n_embd * ggml_type_sizef(wtype)); // mlp_mlp_up_weight
+ ctx_size += n_layer * (n_embd * n_embd * 4 * ggml_type_sizef(wtype)); // mlp_mlp_down_weight
+
+ ctx_size += n_ctx * n_layer * n_embd * ggml_type_sizef(GGML_TYPE_F16); // memory_k
+ ctx_size += n_ctx * n_layer * n_embd * ggml_type_sizef(GGML_TYPE_F16); // memory_v
+
+ ctx_size += (1 + 6 * n_layer) * 512; // object overhead
+
+ printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size / (1024.0 * 1024.0));
+ }
+
+ // create the ggml context
+ {
+ struct ggml_init_params params = {
+ .mem_size = ctx_size,
+ .mem_buffer = NULL,
+ .no_alloc = false,
+ };
+
+ model.ctx = ggml_init(params);
+ if (!model.ctx) {
+ fprintf(stderr, "%s: ggml_init() failed\n", __func__);
+ return false;
+ }
+ }
+
+ // prepare memory for the weights
+ {
+ const auto & hparams = model.hparams;
+
+ const size_t n_embd = hparams.d_model;
+ const size_t n_layer = hparams.n_layers;
+ const size_t n_vocab = hparams.n_vocab;
+
+ model.layers.resize(n_layer);
+
+ model.wte_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
+ model.norm_f_weight = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+
+ // map by name
+ model.tensors["transformer.wte.weight"] = model.wte_weight;
+ model.tensors["transformer.norm_f.weight"] = model.norm_f_weight;
+
+ for (int i = 0; i < n_layer; ++i) {
+ auto & layer = model.layers[i];
+
+ layer.norm_1_weight = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+ layer.c_attn_wqkv_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, 3 * n_embd);
+ layer.c_attn_out_proj_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
+ layer.norm_2_weight = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+ layer.ffn_up_proj = ggml_new_tensor_2d(ctx, wtype, n_embd, 4 * n_embd);
+ layer.ffn_down_proj = ggml_new_tensor_2d(ctx, wtype, 4 * n_embd, n_embd);
+
+ // map by name
+ model.tensors["transformer.blocks." + std::to_string(i) + ".norm_1.weight"] = layer.norm_1_weight;
+ model.tensors["transformer.blocks." + std::to_string(i) + ".attn.Wqkv.weight"] = layer.c_attn_wqkv_weight;
+ model.tensors["transformer.blocks." + std::to_string(i) + ".attn.out_proj.weight"] =
+ layer.c_attn_out_proj_weight;
+ model.tensors["transformer.blocks." + std::to_string(i) + ".norm_2.weight"] = layer.norm_2_weight;
+ model.tensors["transformer.blocks." + std::to_string(i) + ".ffn.up_proj.weight"] = layer.ffn_up_proj;
+ model.tensors["transformer.blocks." + std::to_string(i) + ".ffn.down_proj.weight"] = layer.ffn_down_proj;
+ }
+ }
+
+ // key + value memory
+ {
+ const auto & hparams = model.hparams;
+
+ const size_t n_embd = hparams.d_model;
+ const size_t n_layer = hparams.n_layers;
+
+ const int64_t n_mem = n_layer * n_ctx;
+ const int64_t n_elements = n_embd * n_mem;
+
+ model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
+ model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
+
+ const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
+
+ printf("%s: memory_size = %8.2f MB, n_mem = %lld\n", __func__, memory_size / 1024.0 / 1024.0, n_mem);
+ }
+
+ // load weights
+ {
+ int n_tensors = 0;
+ size_t total_size = 0;
+
+ printf("%s: ", __func__);
+
+ while (true) {
+ int32_t n_dims;
+ int32_t length;
+ int32_t ttype;
+
+ fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
+ fin.read(reinterpret_cast<char *>(&length), sizeof(length));
+ fin.read(reinterpret_cast<char *>(&ttype), sizeof(ttype));
+
+ if (fin.eof()) {
+ break;
+ }
+
+ int32_t nelements = 1;
+ int32_t ne[2] = {1, 1};
+ for (int i = 0; i < n_dims; ++i) {
+ fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
+ nelements *= ne[i];
+ }
+
+ std::string name(length, 0);
+ 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());
+ return false;
+ }
+
+ auto tensor = model.tensors[name.data()];
+ if (ggml_nelements(tensor) != nelements) {
+ fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
+ return false;
+ }
+
+ if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
+ fprintf(stderr,
+ "%s: tensor '%s' has wrong shape in model file: got [%5d, "
+ "%5d], expected [%5d, %5d]\n",
+ __func__, name.data(), (int)tensor->ne[0], (int)tensor->ne[1], ne[0], ne[1]);
+ return false;
+ }
+
+ // for debugging
+ if (0) {
+ printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1],
+ ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor) / 1024.0 / 1024.0, ggml_nbytes(tensor));
+ }
+
+ const size_t bpe = ggml_type_size(ggml_type(ttype));
+
+ 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;
+ }
+
+ fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
+
+ total_size += ggml_nbytes(tensor);
+ if (++n_tensors % 8 == 0) {
+ printf(".");
+ fflush(stdout);
+ }
+ }
+
+ printf(" done\n");
+
+ printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size / 1024.0 / 1024.0, n_tensors);
+ }
+
+ fin.close();
+
+ return true;
+}
+
+// evaluate the transformer
+//
+// - model: the model
+// - n_threads: number of threads to use
+// - n_past: the context size so far
+// - embd_inp: the embeddings of the tokens in the context
+// - embd_w: the predicted logits for the next token
+//
+bool mpt_eval(const mpt_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) {
+ const int N = embd_inp.size();
+
+ const auto & hparams = model.hparams;
+
+ const int n_embd = hparams.d_model;
+ const int n_layer = hparams.n_layers;
+ const int n_head = hparams.n_heads;
+ const int n_vocab = hparams.n_vocab;
+
+ static size_t buf_size = 256u * 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
+ // printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__,
+ // buf_size, buf_size_new);
+
+ // reallocate
+ buf_size = buf_size_new;
+ buf = realloc(buf, buf_size);
+ if (buf == nullptr) {
+ fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
+ return false;
+ }
+ }
+
+ struct ggml_init_params params = {
+ .mem_size = buf_size,
+ .mem_buffer = buf,
+ .no_alloc = false,
+ };
+
+ struct ggml_context * ctx0 = ggml_init(params);
+ struct ggml_cgraph gf = {.n_threads = n_threads};
+
+ struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
+ memcpy(embd->data, embd_inp.data(), N * ggml_element_size(embd));
+
+ struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.wte_weight, embd);
+
+ for (int il = 0; il < n_layer; ++il) {
+
+ struct ggml_tensor * cur;
+
+ // a = self.ln_1(x)
+ {
+ cur = ggml_norm(ctx0, inpL);
+
+ cur = ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].norm_1_weight, cur), cur);
+ }
+
+ // self-attention
+ // b, _, past_key_value = self.attn(a, past_key_value=past_key_value,
+ // attn_bias=attn_bias, attention_mask=attention_mask,
+ // is_causal=is_causal)
+ {
+
+ // compute QKV
+ cur = ggml_mul_mat(ctx0, model.layers[il].c_attn_wqkv_weight, cur);
+
+ if (model.hparams.clip_qkv > 0.0f) {
+ cur = ggml_clamp(ctx0, cur, -model.hparams.clip_qkv, model.hparams.clip_qkv);
+ }
+
+ struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0 * sizeof(float) * n_embd);
+ struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1 * sizeof(float) * n_embd);
+ struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2 * sizeof(float) * n_embd);
+
+ // store key and value to memory
+ {
+ 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) [64, N, 12]
+ struct ggml_tensor * Q = ggml_permute(
+ ctx0, ggml_cpy(ctx0, Qcur, ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd / n_head, n_head, N)), 0, 2,
+ 1, 3);
+
+ // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1,
+ // 3) [64, n_past + N, 12]
+ struct ggml_tensor * K =
+ ggml_permute(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),
+ 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_alibi =
+ ggml_alibi(ctx0, KQ_scaled, n_past, n_head, model.hparams.alibi_bias_max);
+
+ // KQ_masked = mask_past(KQ_scaled)
+ struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled_alibi, n_past);
+
+ // 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() [n_past + N, 64, 12]
+ struct ggml_tensor * V_trans = ggml_cpy(
+ ctx0,
+ 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),
+ ggml_new_tensor_3d(ctx0, model.memory_v->type, n_past + N, n_embd / n_head, n_head));
+
+ // KQV = transpose(V) * KQ_soft_max
+ struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
+
+ // KQV_merged = KQV.permute(0, 2, 1, 3)
+ 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));
+
+ // projection
+ { cur = ggml_mul_mat(ctx0, model.layers[il].c_attn_out_proj_weight, cur); }
+ }
+
+ inpL = ggml_add(ctx0, inpL, cur);
+
+ // m = self.ln_2(x)
+ {
+ cur = ggml_norm(ctx0, inpL);
+
+ cur = ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].norm_2_weight, cur), cur);
+ }
+
+ // n = self.mlp(m)
+ {
+
+ cur = ggml_mul_mat(ctx0, model.layers[il].ffn_up_proj, cur);
+
+ // GELU activation
+ cur = ggml_gelu(ctx0, cur);
+
+ // projection
+ // cur = proj_w*cur + proj_b
+ cur = ggml_mul_mat(ctx0, model.layers[il].ffn_down_proj, cur);
+ }
+
+ // x = x + n
+ inpL = ggml_add(ctx0, inpL, cur);
+ }
+
+ // norm
+ {
+ inpL = ggml_norm(ctx0, inpL);
+ // inpL = ln_f_g*inpL
+ inpL = ggml_mul(ctx0, ggml_repeat(ctx0, model.norm_f_weight, inpL), inpL);
+ }
+
+ // output embedding weight tied to input embedding
+ inpL = ggml_mul_mat(ctx0, model.wte_weight, inpL);
+
+ // logits -> probs
+ // inpL = ggml_soft_max(ctx0, inpL);
+
+ // run the computation
+ ggml_build_forward_expand(&gf, inpL);
+ ggml_graph_compute(ctx0, &gf);
+
+ // std::cout << "Qcur" << std::endl;
+ // print_tensor(Qcur);
+
+ // if (n_past%100 == 0) {
+ // ggml_graph_print(&gf);
+ // ggml_graph_dump_dot(&gf, NULL, "mpt-model.dot");
+ // }
+
+ // 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);
+
+ if (mem_per_token == 0) {
+ mem_per_token = ggml_used_mem(ctx0) / N;
+ }
+ // printf("used_mem = %zu\n", ggml_used_mem(ctx0));
+
+ ggml_free(ctx0);
+
+ return true;
+}
+
+int main(int argc, char ** argv) {
+ const int64_t t_main_start_us = ggml_time_us();
+
+ gpt_params params;
+ params.model = "";
+
+ if (gpt_params_parse(argc, argv, params) == false) {
+ return 1;
+ }
+
+ if (params.seed < 0) {
+ params.seed = time(NULL);
+ }
+
+ printf("%s: seed = %d\n", __func__, params.seed);
+
+ std::mt19937 rng(params.seed);
+ if (params.prompt.empty()) {
+ if (!isatty(STDIN_FILENO)) {
+ std::string line;
+ while (std::getline(std::cin, line)) {
+ params.prompt = params.prompt + "\n" + line;
+ }
+ } else {
+ params.prompt = gpt_random_prompt(rng);
+ }
+ }
+
+ int64_t t_load_us = 0;
+
+ gpt_vocab vocab;
+ mpt_model model;
+
+ // load the model
+ {
+ const int64_t t_start_us = ggml_time_us();
+
+ if (!mpt_model_load(params.model, model, vocab)) {
+ fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
+ return 1;
+ }
+
+ t_load_us = ggml_time_us() - t_start_us;
+ }
+
+ int n_past = 0;
+
+ int64_t t_sample_us = 0;
+ int64_t t_predict_us = 0;
+
+ std::vector<float> logits;
+
+ // tokenize the prompt
+ std::vector<int> embd_inp = ::gpt_tokenize(vocab, params.prompt);
+
+ printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
+
+ for (int i = 0; i < embd_inp.size(); i++) {
+ printf("%s: token[%d] = %6d\n", __func__, i, embd_inp[i]);
+ // vocab.id_to_token.at(embd_inp[i]).c_str()
+ }
+ printf("\n");
+
+ params.n_predict = std::min(params.n_predict, n_ctx - (int)embd_inp.size());
+
+ std::vector<gpt_vocab::id> embd;
+
+ // determine the required inference memory per token:
+ size_t mem_per_token = 0;
+ mpt_eval(model, params.n_threads, 0, {0, 1, 2, 3}, logits, mem_per_token);
+
+ for (int i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {
+ // predict
+ if (embd.size() > 0) {
+ const int64_t t_start_us = ggml_time_us();
+
+ if (!mpt_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {
+ printf("Failed to predict\n");
+ return 1;
+ }
+
+ t_predict_us += ggml_time_us() - t_start_us;
+ }
+
+ n_past += embd.size();
+ embd.clear();
+
+ if (i >= embd_inp.size()) {
+ // sample next token
+ const int top_k = params.top_k;
+ const float top_p = params.top_p;
+ const float temp = params.temp;
+
+ const int n_vocab = model.hparams.n_vocab;
+
+ gpt_vocab::id id = 0;
+
+ {
+ const int64_t t_start_sample_us = ggml_time_us();
+
+ id = gpt_sample_top_k_top_p(vocab, logits.data() + (logits.size() - n_vocab), top_k, top_p, temp, rng);
+
+ t_sample_us += ggml_time_us() - t_start_sample_us;
+ }
+
+ // add it to the context
+ embd.push_back(id);
+ } else {
+ // if here, it means we are still processing the input prompt
+ for (int k = i; k < embd_inp.size(); k++) {
+ embd.push_back(embd_inp[k]);
+ if (embd.size() > params.n_batch) {
+ break;
+ }
+ }
+ i += embd.size() - 1;
+ }
+
+ // display text
+ for (auto id : embd) {
+ printf("%s", vocab.id_to_token[id].c_str());
+ }
+ fflush(stdout);
+
+ // end of text token
+ if (embd.back() == 0) {
+ break;
+ }
+ }
+
+ // report timing
+ {
+ const int64_t t_main_end_us = ggml_time_us();
+
+ printf("\n\n");
+ printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
+ printf("%s: load time = %8.2f ms\n", __func__, t_load_us / 1000.0f);
+ printf("%s: sample time = %8.2f ms\n", __func__, t_sample_us / 1000.0f);
+ printf("%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us / 1000.0f,
+ t_predict_us / 1000.0f / n_past);
+ printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us) / 1000.0f);
+ }
+
+ ggml_free(model.ctx);
+
+ return 0;
+}
--- /dev/null
+#include "ggml/ggml.h"
+
+#include "common-ggml.h"
+#include "common.h"
+
+#include <cassert>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <fstream>
+#include <map>
+#include <regex>
+#include <string>
+#include <vector>
+
+struct mpt_hparams {
+ int32_t d_model = 0;
+ int32_t max_seq_len = 0;
+ int32_t n_heads = 0;
+ int32_t n_layers = 0;
+ int32_t n_vocab = 0;
+ float alibi_bias_max = 0;
+ float clip_qkv = 0;
+ int32_t ftype = 0;
+};
+
+// quantize a model
+bool mpt_model_quantize(const std::string & fname_inp,
+ const std::string & fname_out, ggml_ftype ftype) {
+
+ printf("%s: loading model from '%s'\n", __func__, fname_inp.c_str());
+
+ auto finp = std::ifstream(fname_inp, std::ios::binary);
+ if (!finp) {
+ fprintf(stderr, "%s: failed to open '%s' for reading\n", __func__,
+ fname_inp.c_str());
+ return false;
+ }
+
+ auto fout = std::ofstream(fname_out, std::ios::binary);
+ if (!fout) {
+ fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__,
+ fname_out.c_str());
+ return false;
+ }
+
+ // verify magic
+ {
+ uint32_t magic;
+ finp.read((char *)&magic, sizeof(magic));
+ if (magic != 0x67676d6c) {
+ fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n",
+ __func__, fname_inp.c_str());
+ return false;
+ }
+
+ fout.write((char *)&magic, sizeof(magic));
+ }
+
+ mpt_hparams hparams;
+
+ // load hparams
+ {
+ finp.read((char *)&hparams.d_model, sizeof(hparams.d_model));
+ finp.read((char *)&hparams.max_seq_len, sizeof(hparams.max_seq_len));
+ finp.read((char *)&hparams.n_heads, sizeof(hparams.n_heads));
+ finp.read((char *)&hparams.n_layers, sizeof(hparams.n_layers));
+ finp.read((char *)&hparams.n_vocab, sizeof(hparams.n_vocab));
+ finp.read((char *)&hparams.alibi_bias_max, sizeof(hparams.alibi_bias_max));
+ finp.read((char *)&hparams.clip_qkv, sizeof(hparams.clip_qkv));
+ finp.read((char *)&hparams.ftype, sizeof(hparams.ftype));
+
+ printf("%s: d_model = %d\n", __func__, hparams.d_model);
+ printf("%s: max_seq_len = %d\n", __func__, hparams.max_seq_len);
+ printf("%s: n_heads = %d\n", __func__, hparams.n_heads);
+ printf("%s: n_layers = %d\n", __func__, hparams.n_layers);
+ printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
+ printf("%s: alibi_bias_max = %f\n", __func__, hparams.alibi_bias_max);
+ printf("%s: clip_qkv = %f\n", __func__, hparams.clip_qkv);
+ printf("%s: ftype = %d\n", __func__, hparams.ftype);
+
+ fout.write((char *)&hparams.d_model, sizeof(hparams.d_model));
+ fout.write((char *)&hparams.max_seq_len, sizeof(hparams.max_seq_len));
+ fout.write((char *)&hparams.n_heads, sizeof(hparams.n_heads));
+ fout.write((char *)&hparams.n_layers, sizeof(hparams.n_layers));
+ fout.write((char *)&hparams.n_vocab, sizeof(hparams.n_vocab));
+ fout.write((char *)&hparams.alibi_bias_max, sizeof(hparams.alibi_bias_max));
+ fout.write((char *)&hparams.clip_qkv, sizeof(hparams.clip_qkv));
+ fout.write((char *)&ftype, sizeof(hparams.ftype));
+ }
+
+ // load vocab
+ {
+ const int32_t n_vocab = hparams.n_vocab;
+
+ std::string word;
+ for (int i = 0; i < n_vocab; i++) {
+ uint32_t len;
+ finp.read((char *)&len, sizeof(len));
+ fout.write((char *)&len, sizeof(len));
+
+ word.resize(len);
+ finp.read((char *)word.data(), len);
+ fout.write((char *)word.data(), len);
+ }
+ }
+
+ printf("%s: quantizing tensors\n", __func__);
+
+ // regexes of tensor names to be quantized
+ const std::vector<std::string> to_quant = {
+ ".*weight",
+ };
+
+ if (!ggml_common_quantize_0(finp, fout, ftype, to_quant, {})) {
+ fprintf(stderr, "%s: failed to quantize model '%s'\n", __func__,
+ fname_inp.c_str());
+ return false;
+ }
+
+ finp.close();
+ fout.close();
+
+ return true;
+}
+
+// usage:
+// ./mpt-quantize models/mpt/ggml-model.bin
+// models/mpt/ggml-model-quant.bin type
+//
+int main(int argc, char ** argv) {
+ if (argc != 4) {
+ fprintf(stderr, "usage: %s model-f32.bin model-quant.bin type\n",
+ argv[0]);
+ ggml_print_ftypes(stderr);
+ return 1;
+ }
+
+ // needed to initialize f16 tables
+ {
+ struct ggml_init_params params = {0, NULL, false};
+ struct ggml_context * ctx = ggml_init(params);
+ ggml_free(ctx);
+ }
+
+ const std::string fname_inp = argv[1];
+ const std::string fname_out = argv[2];
+
+ const ggml_ftype ftype = ggml_parse_ftype(argv[3]);
+
+ const int64_t t_main_start_us = ggml_time_us();
+
+ int64_t t_quantize_us = 0;
+
+ // load the model
+ {
+ const int64_t t_start_us = ggml_time_us();
+
+ if (!mpt_model_quantize(fname_inp, fname_out, ggml_ftype(ftype))) {
+ fprintf(stderr, "%s: failed to quantize model from '%s'\n",
+ __func__, fname_inp.c_str());
+ return 1;
+ }
+
+ t_quantize_us = ggml_time_us() - t_start_us;
+ }
+
+ // report timing
+ {
+ const int64_t t_main_end_us = ggml_time_us();
+
+ printf("\n");
+ printf("%s: quantize time = %8.2f ms\n", __func__,
+ t_quantize_us / 1000.0f);
+ printf("%s: total time = %8.2f ms\n", __func__,
+ (t_main_end_us - t_main_start_us) / 1000.0f);
+ }
+
+ return 0;
+}
--- /dev/null
+#
+# replit
+
+set(TEST_TARGET replit)
+add_executable(${TEST_TARGET} main.cpp)
+target_link_libraries(${TEST_TARGET} PRIVATE ggml common common-ggml)
+
+#
+# replit-quantize
+
+set(TEST_TARGET replit-quantize)
+add_executable(${TEST_TARGET} quantize.cpp)
+target_link_libraries(${TEST_TARGET} PRIVATE ggml common common-ggml)
--- /dev/null
+from pathlib import Path
+import sys
+import struct
+import json
+import numpy as np
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import sentencepiece.sentencepiece_model_pb2 as model
+
+if len(sys.argv) < 3:
+ print("Usage: convert-h5-to-ggml.py dir-model [use-f32]\n")
+ print(" ftype == 0 -> float32")
+ print(" ftype == 1 -> float16")
+ sys.exit(1)
+
+
+# output in the same directory as the model
+dir_model = sys.argv[1]
+fname_out = sys.argv[1] + "/ggml-model.bin"
+
+
+with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
+ hparams = json.load(f)
+
+sp_proto = model.ModelProto()
+sp_proto.ParseFromString(open(Path(sys.argv[1]) / "spiece.model", "rb").read())
+
+
+# possible data types
+# ftype == 0 -> float32
+# ftype == 1 -> float16
+#
+# map from ftype to string
+ftype_str = ["f32", "f16"]
+
+ftype = 1
+if len(sys.argv) > 2:
+ ftype = int(sys.argv[2])
+ if ftype < 0 or ftype > 1:
+ print("Invalid ftype: " + str(ftype))
+ sys.exit(1)
+ fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin"
+
+
+tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+ dir_model, low_cpu_mem_usage=True, trust_remote_code=True
+)
+# print (model)
+
+# print(tokenizer.encode('I believe the meaning of life is'))
+
+list_vars = model.state_dict()
+for name in list_vars.keys():
+ print(name, list_vars[name].shape, list_vars[name].dtype)
+
+fout = open(fname_out, "wb")
+
+print(hparams)
+
+fout.write(struct.pack("i", 0x67676D6C)) # magic: ggml in hex
+fout.write(struct.pack("i", hparams["d_model"]))
+fout.write(struct.pack("i", hparams["max_seq_len"]))
+fout.write(struct.pack("i", hparams["n_heads"]))
+fout.write(struct.pack("i", hparams["n_layers"]))
+fout.write(struct.pack("i", hparams["vocab_size"]))
+fout.write(struct.pack("i", ftype))
+
+
+# TODO: temporary hack to not deal with implementing the tokenizer
+for piece in sp_proto.pieces:
+ encoded_piece = piece.piece.encode("utf-8")
+ fout.write(struct.pack("i", len(encoded_piece)))
+ fout.write(encoded_piece)
+ fout.write(struct.pack("f", piece.score))
+
+
+for name in list_vars.keys():
+ data = list_vars[name].squeeze().numpy()
+ print("Processing variable: " + name + " with shape: ", data.shape)
+
+ n_dims = len(data.shape)
+
+ # ftype == 0 -> float32, ftype == 1 -> float16
+ ftype_cur = 0
+ if ftype != 0:
+ if name[-7:] == ".weight" and n_dims == 2:
+ print(" Converting to float16")
+ data = data.astype(np.float16)
+ ftype_cur = 1
+ else:
+ print(" Converting to float32")
+ data = data.astype(np.float32)
+ ftype_cur = 0
+ else:
+ if data.dtype != np.float32:
+ print(" Converting to float32")
+ data = data.astype(np.float32)
+ ftype_cur = 0
+
+ # header
+ str = name.encode("utf-8")
+ fout.write(struct.pack("iii", n_dims, len(str), ftype_cur))
+ for i in range(n_dims):
+ fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
+ fout.write(str)
+
+ # data
+ data.tofile(fout)
+
+fout.close()
+
+print("Done. Output file: " + fname_out)
+print("")
--- /dev/null
+#include "ggml/ggml.h"
+
+#include "common-ggml.h"
+#include "common.h"
+
+#include <cassert>
+#include <cmath>
+#include <cstddef>
+#include <cstdio>
+#include <cstring>
+#include <fstream>
+#include <iostream>
+#include <map>
+#include <stdint.h>
+#include <string>
+#include <unistd.h>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+using piece_t = std::pair<std::size_t, float>;
+using piece_map_t = std::unordered_map<std::string, piece_t>;
+
+struct replit_tokenizer {
+ gpt_vocab raw_vocab;
+ piece_map_t piece_map;
+ std::vector<std::string> vocab;
+};
+
+std::pair<std::vector<std::size_t>, float> encode_word(const std::string & word, const piece_map_t & model) {
+ std::vector<int> best_segmentations_starts(word.length() + 1, -1);
+ best_segmentations_starts[0] = 0;
+
+ std::vector<float> best_segmentations_scores(word.length() + 1, -std::numeric_limits<float>::infinity());
+ best_segmentations_scores[0] = 1.0;
+
+ for (int start_idx = 0; start_idx < word.length(); ++start_idx) {
+ float best_score_at_start = best_segmentations_scores[start_idx];
+ for (int end_idx = start_idx + 1; end_idx <= word.length(); ++end_idx) {
+ std::string token = word.substr(start_idx, end_idx - start_idx);
+ if (model.count(token) && best_score_at_start != -std::numeric_limits<float>::infinity()) {
+ float token_score = model.at(token).second;
+ float score = token_score + best_score_at_start;
+ if (best_segmentations_scores[end_idx] == -std::numeric_limits<float>::infinity() ||
+ best_segmentations_scores[end_idx] > score) {
+ best_segmentations_starts[end_idx] = start_idx;
+ best_segmentations_scores[end_idx] = score;
+ }
+ }
+ }
+ }
+
+ if (best_segmentations_scores.back() == -std::numeric_limits<float>::infinity()) {
+ return std::make_pair(std::vector<std::size_t>{0}, 0.0f);
+ }
+
+ float score = best_segmentations_scores.back();
+ int start = best_segmentations_starts.back();
+ int end = word.length();
+ std::vector<std::size_t> tokens;
+ while (start != 0) {
+ const auto token_id = model.at(word.substr(start, end - start)).first;
+ tokens.insert(tokens.begin(), token_id);
+ int next_start = best_segmentations_starts[start];
+ end = start;
+ start = next_start;
+ }
+ const auto token_id = model.at(word.substr(start, end - start)).first;
+ tokens.insert(tokens.begin(), token_id);
+ return std::make_pair(tokens, score);
+}
+
+bool replit_tokenizer_load(replit_tokenizer & tokenizer, std::istream & fin, int max_vocab_size) {
+
+ for (std::size_t i = 0; i < max_vocab_size; i++) {
+
+ uint32_t len;
+ fin.read((char *)&len, sizeof(len));
+
+ std::string word;
+ word.resize(len);
+ fin.read((char *)word.data(), len);
+
+ float score;
+ fin.read((char *)&score, sizeof(score));
+
+ tokenizer.piece_map[word] = std::make_pair(i, -score);
+ tokenizer.raw_vocab.id_to_token[i] = word;
+ }
+
+ return true;
+}
+
+std::string replace_all(const std::string & str, // where to work
+ const std::string & find, // substitute 'find'
+ const std::string & replace // by 'replace'
+) {
+ using namespace std;
+ string result;
+ size_t find_len = find.size();
+ size_t pos, from = 0;
+ while (string::npos != (pos = str.find(find, from))) {
+ result.append(str, from, pos - from);
+ result.append(replace);
+ from = pos + find_len;
+ }
+ result.append(str, from, string::npos);
+ return result;
+}
+
+std::string ws_symbol = "\342\226\201";
+std::vector<std::size_t> replit_tokenizer_tokenize(replit_tokenizer & tokenizer, const std::string & text) {
+ std::vector<std::size_t> tokens;
+ auto normalized_text = replace_all(text, " ", ws_symbol);
+ auto tokenized = encode_word(normalized_text, tokenizer.piece_map);
+
+ return tokenized.first;
+}
+
+std::string replit_tokenizer_detokenize(replit_tokenizer & tokenizer, const std::vector<std::size_t> & tokens) {
+ std::string text;
+ for (auto token : tokens) {
+ text += tokenizer.raw_vocab.id_to_token[token];
+ }
+ auto denormalized_text = replace_all(text, ws_symbol, " ");
+ return denormalized_text;
+}
+
+// no defaults for now
+struct mpt_hparams {
+ int32_t d_model = 0;
+ int32_t max_seq_len = 0;
+ int32_t n_heads = 0;
+ int32_t n_layers = 0;
+ int32_t n_vocab = 0;
+ int32_t ftype = 0;
+};
+
+struct replit_layer {
+ // pre normalization
+ struct ggml_tensor * ln_1_weight;
+
+ // attention
+ struct ggml_tensor * c_attn_wqkv_weight;
+
+ struct ggml_tensor * c_attn_out_proj_weight;
+
+ // post normalization
+ struct ggml_tensor * ln_2_weight;
+
+ // ff
+ struct ggml_tensor * c_mlp_mlp_up_weight;
+
+ struct ggml_tensor * c_mlp_mlp_down_weight;
+};
+
+struct replit_model {
+ mpt_hparams hparams;
+
+ struct ggml_tensor * wte_weight; // position embedding
+ struct ggml_tensor * ln_f_weight; // language model head
+
+ std::vector<replit_layer> layers;
+
+ // key + value memory
+ struct ggml_tensor * memory_k;
+ struct ggml_tensor * memory_v;
+
+ struct ggml_context * ctx;
+ std::map<std::string, struct ggml_tensor *> tensors;
+};
+
+// load the model's weights from a file
+bool replit_model_load(const std::string & fname, replit_model & model, replit_tokenizer & vocab) {
+ printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
+
+ auto fin = std::ifstream(fname, std::ios::binary);
+ if (!fin) {
+ fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
+ return false;
+ }
+
+ // verify magic
+ {
+ 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());
+ return false;
+ }
+ }
+
+ // load hparams
+ {
+ auto & hparams = model.hparams;
+
+ fin.read((char *)&hparams.d_model, sizeof(hparams.d_model));
+ fin.read((char *)&hparams.max_seq_len, sizeof(hparams.max_seq_len));
+ fin.read((char *)&hparams.n_heads, sizeof(hparams.n_heads));
+ fin.read((char *)&hparams.n_layers, sizeof(hparams.n_layers));
+ fin.read((char *)&hparams.n_vocab, sizeof(hparams.n_vocab));
+ fin.read((char *)&hparams.ftype, sizeof(hparams.ftype));
+
+ printf("%s: d_model = %d\n", __func__, hparams.d_model);
+ printf("%s: max_seq_len = %d\n", __func__, hparams.max_seq_len);
+ printf("%s: n_heads = %d\n", __func__, hparams.n_heads);
+ printf("%s: n_layers = %d\n", __func__, hparams.n_layers);
+ printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
+ printf("%s: ftype = %d\n", __func__, hparams.ftype);
+ }
+
+ // load vocab
+ replit_tokenizer_load(vocab, fin, model.hparams.n_vocab);
+
+ // 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_ftype_to_ggml_type((ggml_ftype)(model.hparams.ftype));
+ if (wtype == GGML_TYPE_COUNT) {
+ fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n", __func__, fname.c_str(),
+ model.hparams.ftype);
+ return false;
+ }
+
+ auto & ctx = model.ctx;
+
+ size_t ctx_size = 0;
+
+ {
+ const auto & hparams = model.hparams;
+
+ const int n_embd = hparams.d_model;
+ const int n_layer = hparams.n_layers;
+ const int n_ctx = hparams.max_seq_len;
+ const int n_vocab = hparams.n_vocab;
+
+ ctx_size += n_embd * n_vocab * ggml_type_sizef(wtype); // wte_weight
+ ctx_size += n_embd * ggml_type_sizef(GGML_TYPE_F32); // ln_f_weight
+
+ ctx_size += n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // ln_1_weight
+ ctx_size += n_layer * (3 * n_embd * n_embd * ggml_type_sizef(wtype)); // attn_Wqkv_weight
+ ctx_size += n_layer * (n_embd * n_embd * ggml_type_sizef(wtype)); // attn_out_proj_weight
+ ctx_size += n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // ln_2_weight
+ ctx_size += n_layer * (4 * n_embd * n_embd * ggml_type_sizef(wtype)); // mlp_mlp_up_weight
+ ctx_size += n_layer * (n_embd * n_embd * 4 * ggml_type_sizef(wtype)); // mlp_mlp_down_weight
+
+ ctx_size += n_ctx * n_layer * n_embd * ggml_type_sizef(GGML_TYPE_F16); // memory_k
+ ctx_size += n_ctx * n_layer * n_embd * ggml_type_sizef(GGML_TYPE_F16); // memory_v
+
+ ctx_size += (1 + 6 * n_layer) * 512; // object overhead
+
+ printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size / (1024.0 * 1024.0));
+ }
+
+ // create the ggml context
+ {
+ struct ggml_init_params params = {
+ .mem_size = ctx_size,
+ .mem_buffer = NULL,
+ .no_alloc = false,
+ };
+
+ model.ctx = ggml_init(params);
+ if (!model.ctx) {
+ fprintf(stderr, "%s: ggml_init() failed\n", __func__);
+ return false;
+ }
+ }
+
+ // prepare memory for the weights
+ {
+ const auto & hparams = model.hparams;
+
+ const int n_embd = hparams.d_model;
+ const int n_layer = hparams.n_layers;
+ const int n_vocab = hparams.n_vocab;
+
+ model.layers.resize(n_layer);
+
+ model.wte_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
+ model.ln_f_weight = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+
+ // map by name
+ model.tensors["transformer.wte.weight"] = model.wte_weight;
+ model.tensors["transformer.ln_f.weight"] = model.ln_f_weight;
+
+ for (int i = 0; i < n_layer; ++i) {
+ auto & layer = model.layers[i];
+
+ layer.ln_1_weight = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+ layer.c_attn_wqkv_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, 3 * n_embd);
+ layer.c_attn_out_proj_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
+ layer.ln_2_weight = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+ layer.c_mlp_mlp_up_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, 4 * n_embd);
+ layer.c_mlp_mlp_down_weight = ggml_new_tensor_2d(ctx, wtype, 4 * n_embd, n_embd);
+
+ // map by name
+ model.tensors["transformer.blocks." + std::to_string(i) + ".ln_1.weight"] = layer.ln_1_weight;
+ model.tensors["transformer.blocks." + std::to_string(i) + ".attn.Wqkv.weight"] = layer.c_attn_wqkv_weight;
+ model.tensors["transformer.blocks." + std::to_string(i) + ".attn.out_proj.weight"] =
+ layer.c_attn_out_proj_weight;
+ model.tensors["transformer.blocks." + std::to_string(i) + ".ln_2.weight"] = layer.ln_2_weight;
+ model.tensors["transformer.blocks." + std::to_string(i) + ".mlp.mlp_up.weight"] = layer.c_mlp_mlp_up_weight;
+ model.tensors["transformer.blocks." + std::to_string(i) + ".mlp.mlp_down.weight"] =
+ layer.c_mlp_mlp_down_weight;
+ }
+ }
+
+ // key + value memory
+ {
+ const auto & hparams = model.hparams;
+
+ const int n_embd = hparams.d_model;
+ const int n_layer = hparams.n_layers;
+ const int n_ctx = hparams.max_seq_len;
+
+ const int64_t n_mem = n_layer * n_ctx;
+ const int64_t n_elements = n_embd * n_mem;
+
+ model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
+ model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
+
+ const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
+
+ printf("%s: memory_size = %8.2f MB, n_mem = %lld\n", __func__, memory_size / 1024.0 / 1024.0, n_mem);
+ }
+
+ // load weights
+ {
+ int n_tensors = 0;
+ size_t total_size = 0;
+
+ printf("%s: ", __func__);
+
+ while (true) {
+ int32_t n_dims;
+ int32_t length;
+ int32_t ttype;
+
+ fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
+ fin.read(reinterpret_cast<char *>(&length), sizeof(length));
+ fin.read(reinterpret_cast<char *>(&ttype), sizeof(ttype));
+
+ if (fin.eof()) {
+ break;
+ }
+
+ int32_t nelements = 1;
+ int32_t ne[2] = {1, 1};
+ for (int i = 0; i < n_dims; ++i) {
+ fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
+ nelements *= ne[i];
+ }
+
+ std::string name(length, 0);
+ 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());
+ return false;
+ }
+
+ auto tensor = model.tensors[name.data()];
+ if (ggml_nelements(tensor) != nelements) {
+ fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
+ return false;
+ }
+
+ if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
+ fprintf(stderr,
+ "%s: tensor '%s' has wrong shape in model file: got [%5d, "
+ "%5d], expected [%5d, %5d]\n",
+ __func__, name.data(), (int)tensor->ne[0], (int)tensor->ne[1], ne[0], ne[1]);
+ return false;
+ }
+
+ // for debugging
+ if (0) {
+ printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1],
+ ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor) / 1024.0 / 1024.0, ggml_nbytes(tensor));
+ }
+
+ const size_t bpe = ggml_type_size(ggml_type(ttype));
+
+ 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;
+ }
+
+ fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
+
+ total_size += ggml_nbytes(tensor);
+ if (++n_tensors % 8 == 0) {
+ printf(".");
+ fflush(stdout);
+ }
+ }
+
+ printf(" done\n");
+
+ printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size / 1024.0 / 1024.0, n_tensors);
+ }
+
+ fin.close();
+
+ return true;
+}
+
+// evaluate the transformer
+//
+// - model: the model
+// - n_threads: number of threads to use
+// - n_past: the context size so far
+// - embd_inp: the embeddings of the tokens in the context
+// - embd_w: the predicted logits for the next token
+//
+bool replit_eval(const replit_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) {
+ const int N = embd_inp.size();
+
+ const auto & hparams = model.hparams;
+
+ const int n_embd = hparams.d_model;
+ const int n_layer = hparams.n_layers;
+ const int n_ctx = hparams.max_seq_len;
+ const int n_head = hparams.n_heads;
+ const int n_vocab = hparams.n_vocab;
+
+ static size_t buf_size = 256u * 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
+ // printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__,
+ // buf_size, buf_size_new);
+
+ // reallocate
+ buf_size = buf_size_new;
+ buf = realloc(buf, buf_size);
+ if (buf == nullptr) {
+ fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
+ return false;
+ }
+ }
+
+ struct ggml_init_params params = {
+ .mem_size = buf_size,
+ .mem_buffer = buf,
+ .no_alloc = false,
+ };
+
+ struct ggml_context * ctx0 = ggml_init(params);
+ struct ggml_cgraph gf = {.n_threads = n_threads};
+
+ struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
+ memcpy(embd->data, embd_inp.data(), N * ggml_element_size(embd));
+
+ struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.wte_weight, embd);
+
+ for (int il = 0; il < n_layer; ++il) {
+
+ struct ggml_tensor * cur;
+
+ // a = self.ln_1(x)
+ {
+ cur = ggml_norm(ctx0, inpL);
+
+ cur = ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ln_1_weight, cur), cur);
+ }
+
+ // self-attention
+ // b, _, past_key_value = self.attn(a, past_key_value=past_key_value,
+ // attn_bias=attn_bias, attention_mask=attention_mask,
+ // is_causal=is_causal)
+ {
+
+ // compute QKV
+ { cur = ggml_mul_mat(ctx0, model.layers[il].c_attn_wqkv_weight, cur); }
+
+ struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0 * sizeof(float) * n_embd);
+ struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1 * sizeof(float) * n_embd);
+ struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2 * sizeof(float) * n_embd);
+
+ // store key and value to memory
+ {
+ 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) [64, N, 12]
+ struct ggml_tensor * Q = ggml_permute(
+ ctx0, ggml_cpy(ctx0, Qcur, ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd / n_head, n_head, N)), 0, 2,
+ 1, 3);
+
+ // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1,
+ // 3) [64, n_past + N, 12]
+ struct ggml_tensor * K =
+ ggml_permute(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),
+ 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_alibi = ggml_alibi(ctx0, KQ_scaled, n_past, n_head, 8.0);
+
+ // KQ_masked = mask_past(KQ_scaled)
+ struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled_alibi, n_past);
+
+ // 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() [n_past + N, 64, 12]
+ struct ggml_tensor * V_trans = ggml_cpy(
+ ctx0,
+ 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),
+ ggml_new_tensor_3d(ctx0, model.memory_v->type, n_past + N, n_embd / n_head, n_head));
+
+ // KQV = transpose(V) * KQ_soft_max
+ struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
+
+ // KQV_merged = KQV.permute(0, 2, 1, 3)
+ 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));
+
+ // projection
+ { cur = ggml_mul_mat(ctx0, model.layers[il].c_attn_out_proj_weight, cur); }
+ }
+
+ inpL = ggml_add(ctx0, inpL, cur);
+
+ // m = self.ln_2(x)
+ {
+ cur = ggml_norm(ctx0, inpL);
+
+ cur = ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ln_2_weight, cur), cur);
+ }
+
+ // n = self.mlp(m)
+ {
+
+ cur = ggml_mul_mat(ctx0, model.layers[il].c_mlp_mlp_up_weight, cur);
+
+ // GELU activation
+ cur = ggml_gelu(ctx0, cur);
+
+ // projection
+ // cur = proj_w*cur + proj_b
+ cur = ggml_mul_mat(ctx0, model.layers[il].c_mlp_mlp_down_weight, cur);
+ }
+
+ // x = x + n
+ inpL = ggml_add(ctx0, inpL, cur);
+ }
+
+ // norm
+ {
+ inpL = ggml_norm(ctx0, inpL);
+ // inpL = ln_f_g*inpL
+ inpL = ggml_mul(ctx0, ggml_repeat(ctx0, model.ln_f_weight, inpL), inpL);
+ }
+
+ // output embedding weight tied to input embedding
+ inpL = ggml_mul_mat(ctx0, model.wte_weight, inpL);
+
+ // logits -> probs
+ // inpL = ggml_soft_max(ctx0, inpL);
+
+ // run the computation
+ ggml_build_forward_expand(&gf, inpL);
+ ggml_graph_compute(ctx0, &gf);
+
+ // std::cout << "Qcur" << std::endl;
+ // print_tensor(Qcur);
+
+ // if (n_past%100 == 0) {
+ // ggml_graph_print(&gf);
+ // ggml_graph_dump_dot(&gf, NULL, "replit-model.dot");
+ // }
+
+ // 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);
+
+ if (mem_per_token == 0) {
+ mem_per_token = ggml_used_mem(ctx0) / N;
+ }
+ // printf("used_mem = %zu\n", ggml_used_mem(ctx0));
+
+ ggml_free(ctx0);
+
+ return true;
+}
+
+int main(int argc, char ** argv) {
+ const int64_t t_main_start_us = ggml_time_us();
+
+ gpt_params params;
+ params.model = "";
+
+ if (gpt_params_parse(argc, argv, params) == false) {
+ return 1;
+ }
+
+ if (params.seed < 0) {
+ params.seed = time(NULL);
+ }
+
+ printf("%s: seed = %d\n", __func__, params.seed);
+
+ std::mt19937 rng(params.seed);
+ if (params.prompt.empty()) {
+ if (!isatty(STDIN_FILENO)) {
+ std::string line;
+ while (std::getline(std::cin, line)) {
+ params.prompt = params.prompt + "\n" + line;
+ }
+ } else {
+ params.prompt = gpt_random_prompt(rng);
+ }
+ }
+
+ int64_t t_load_us = 0;
+
+ replit_tokenizer vocab;
+ replit_model model;
+
+ // load the model
+ {
+ const int64_t t_start_us = ggml_time_us();
+
+ if (!replit_model_load(params.model, model, vocab)) {
+ fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
+ return 1;
+ }
+
+ t_load_us = ggml_time_us() - t_start_us;
+ }
+
+ int n_past = 0;
+
+ int64_t t_sample_us = 0;
+ int64_t t_predict_us = 0;
+
+ std::vector<float> logits;
+
+ // tokenize the prompt
+ std::vector<std::size_t> embd_inp = replit_tokenizer_tokenize(vocab, params.prompt);
+
+ printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
+
+ for (int i = 0; i < embd_inp.size(); i++) {
+ printf("%s: token[%d] = %6lu\n", __func__, i, embd_inp[i]);
+ // vocab.id_to_token.at(embd_inp[i]).c_str()
+ }
+ printf("\n");
+
+ params.n_predict = std::min(params.n_predict, model.hparams.max_seq_len - (int)embd_inp.size());
+
+ std::vector<gpt_vocab::id> embd;
+
+ // determine the required inference memory per token:
+ size_t mem_per_token = 0;
+ replit_eval(model, params.n_threads, 0, {0, 1, 2, 3}, logits, mem_per_token);
+
+ for (int i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {
+ // predict
+ if (embd.size() > 0) {
+ const int64_t t_start_us = ggml_time_us();
+
+ if (!replit_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {
+ printf("Failed to predict\n");
+ return 1;
+ }
+
+ t_predict_us += ggml_time_us() - t_start_us;
+ }
+
+ n_past += embd.size();
+ embd.clear();
+
+ if (i >= embd_inp.size()) {
+ // sample next token
+ const int top_k = params.top_k;
+ const float top_p = params.top_p;
+ const float temp = params.temp;
+
+ const int n_vocab = model.hparams.n_vocab;
+
+ gpt_vocab::id id = 0;
+
+ {
+ const int64_t t_start_sample_us = ggml_time_us();
+
+ id = gpt_sample_top_k_top_p(vocab.raw_vocab, logits.data() + (logits.size() - n_vocab), top_k, top_p,
+ temp, rng);
+
+ t_sample_us += ggml_time_us() - t_start_sample_us;
+ }
+
+ // add it to the context
+ embd.push_back(id);
+ } else {
+ // if here, it means we are still processing the input prompt
+ for (int k = i; k < embd_inp.size(); k++) {
+ embd.push_back(embd_inp[k]);
+ if (embd.size() > params.n_batch) {
+ break;
+ }
+ }
+ i += embd.size() - 1;
+ }
+
+ // display text
+ for (auto id : embd) {
+ printf("%s", replit_tokenizer_detokenize(vocab, {static_cast<std::size_t>(id)}).c_str());
+ }
+ fflush(stdout);
+
+ // end of text token
+ if (embd.back() == 0) {
+ break;
+ }
+ }
+
+ // report timing
+ {
+ const int64_t t_main_end_us = ggml_time_us();
+
+ printf("\n\n");
+ printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
+ printf("%s: load time = %8.2f ms\n", __func__, t_load_us / 1000.0f);
+ printf("%s: sample time = %8.2f ms\n", __func__, t_sample_us / 1000.0f);
+ printf("%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us / 1000.0f,
+ t_predict_us / 1000.0f / n_past);
+ printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us) / 1000.0f);
+ }
+
+ ggml_free(model.ctx);
+
+ return 0;
+}
--- /dev/null
+#include "ggml/ggml.h"
+
+#include "common-ggml.h"
+#include "common.h"
+
+#include <cassert>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <fstream>
+#include <map>
+#include <regex>
+#include <string>
+#include <vector>
+
+struct mpt_hparams {
+ int32_t d_model = 0;
+ int32_t max_seq_len = 0;
+ int32_t n_heads = 0;
+ int32_t n_layers = 0;
+ int32_t n_vocab = 0;
+ int32_t ftype = 0;
+};
+
+// quantize a model
+bool mpt_model_quantize(const std::string & fname_inp,
+ const std::string & fname_out, ggml_ftype ftype) {
+
+ printf("%s: loading model from '%s'\n", __func__, fname_inp.c_str());
+
+ auto finp = std::ifstream(fname_inp, std::ios::binary);
+ if (!finp) {
+ fprintf(stderr, "%s: failed to open '%s' for reading\n", __func__,
+ fname_inp.c_str());
+ return false;
+ }
+
+ auto fout = std::ofstream(fname_out, std::ios::binary);
+ if (!fout) {
+ fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__,
+ fname_out.c_str());
+ return false;
+ }
+
+ // verify magic
+ {
+ uint32_t magic;
+ finp.read((char *)&magic, sizeof(magic));
+ if (magic != 0x67676d6c) {
+ fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n",
+ __func__, fname_inp.c_str());
+ return false;
+ }
+
+ fout.write((char *)&magic, sizeof(magic));
+ }
+
+ mpt_hparams hparams;
+
+ // load hparams
+ {
+ finp.read((char *)&hparams.d_model, sizeof(hparams.d_model));
+ finp.read((char *)&hparams.max_seq_len, sizeof(hparams.max_seq_len));
+ finp.read((char *)&hparams.n_heads, sizeof(hparams.n_heads));
+ finp.read((char *)&hparams.n_layers, sizeof(hparams.n_layers));
+ finp.read((char *)&hparams.n_vocab, sizeof(hparams.n_vocab));
+ finp.read((char *)&hparams.ftype, sizeof(hparams.ftype));
+
+ printf("%s: d_model = %d\n", __func__, hparams.d_model);
+ printf("%s: max_seq_len = %d\n", __func__, hparams.max_seq_len);
+ printf("%s: n_heads = %d\n", __func__, hparams.n_heads);
+ printf("%s: n_layers = %d\n", __func__, hparams.n_layers);
+ printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
+ printf("%s: ftype = %d\n", __func__, hparams.ftype);
+
+ fout.write((char *)&hparams.d_model, sizeof(hparams.d_model));
+ fout.write((char *)&hparams.max_seq_len, sizeof(hparams.max_seq_len));
+ fout.write((char *)&hparams.n_heads, sizeof(hparams.n_heads));
+ fout.write((char *)&hparams.n_layers, sizeof(hparams.n_layers));
+ fout.write((char *)&hparams.n_vocab, sizeof(hparams.n_vocab));
+ fout.write((char *)&ftype, sizeof(hparams.ftype));
+ }
+
+ // load vocab
+ {
+ const int32_t n_vocab = hparams.n_vocab;
+
+ std::string word;
+ for (int i = 0; i < n_vocab; i++) {
+ uint32_t len;
+ finp.read((char *)&len, sizeof(len));
+ fout.write((char *)&len, sizeof(len));
+
+ word.resize(len);
+ finp.read((char *)word.data(), len);
+ fout.write((char *)word.data(), len);
+
+ float prob;
+ finp.read((char *)&prob, sizeof(prob));
+ fout.write((char *)&prob, sizeof(prob));
+ }
+ }
+
+ printf("%s: quantizing tensors\n", __func__);
+
+ // regexes of tensor names to be quantized
+ const std::vector<std::string> to_quant = {
+ ".*weight",
+ };
+
+ if (!ggml_common_quantize_0(finp, fout, ftype, to_quant, {})) {
+ fprintf(stderr, "%s: failed to quantize model '%s'\n", __func__,
+ fname_inp.c_str());
+ return false;
+ }
+
+ finp.close();
+ fout.close();
+
+ return true;
+}
+
+// usage:
+// ./replit-quantize models/replit/ggml-model.bin
+// models/replit/ggml-model-quant.bin type
+//
+int main(int argc, char ** argv) {
+ if (argc != 4) {
+ fprintf(stderr, "usage: %s model-f32.bin model-quant.bin type\n",
+ argv[0]);
+ ggml_print_ftypes(stderr);
+ return 1;
+ }
+
+ // needed to initialize f16 tables
+ {
+ struct ggml_init_params params = {0, NULL, false};
+ struct ggml_context * ctx = ggml_init(params);
+ ggml_free(ctx);
+ }
+
+ const std::string fname_inp = argv[1];
+ const std::string fname_out = argv[2];
+
+ const ggml_ftype ftype = ggml_parse_ftype(argv[3]);
+
+ const int64_t t_main_start_us = ggml_time_us();
+
+ int64_t t_quantize_us = 0;
+
+ // load the model
+ {
+ const int64_t t_start_us = ggml_time_us();
+
+ if (!mpt_model_quantize(fname_inp, fname_out, ggml_ftype(ftype))) {
+ fprintf(stderr, "%s: failed to quantize model from '%s'\n",
+ __func__, fname_inp.c_str());
+ return 1;
+ }
+
+ t_quantize_us = ggml_time_us() - t_start_us;
+ }
+
+ // report timing
+ {
+ const int64_t t_main_end_us = ggml_time_us();
+
+ printf("\n");
+ printf("%s: quantize time = %8.2f ms\n", __func__,
+ t_quantize_us / 1000.0f);
+ printf("%s: total time = %8.2f ms\n", __func__,
+ (t_main_end_us - t_main_start_us) / 1000.0f);
+ }
+
+ return 0;
+}
GGML_OP_ROPE,
GGML_OP_ROPE_BACK,
GGML_OP_ALIBI,
+ GGML_OP_CLAMP,
GGML_OP_CONV_1D_1S,
GGML_OP_CONV_1D_2S,
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
- int n_head);
+ int n_head,
+ float bias_max);
+
+ // clamp
+ // in-place, returns view(a)
+ struct ggml_tensor * ggml_clamp(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ float min,
+ float max);
// padding = 1
// TODO: we don't support extra parameters for now
"ROPE",
"ROPE_BACK",
"ALIBI",
+ "CLAMP",
"CONV_1D_1S",
"CONV_1D_2S",
"MAP_BINARY",
};
-static_assert(GGML_OP_COUNT == 50, "GGML_OP_COUNT != 50");
+static_assert(GGML_OP_COUNT == 51, "GGML_OP_COUNT != 51");
+
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
"rope(x)",
"rope_back(x)",
"alibi(x)",
+ "clamp(x)",
"conv_1d_1s(x)",
"conv_1d_2s(x)",
"f(x,y)",
};
-static_assert(GGML_OP_COUNT == 50, "GGML_OP_COUNT != 50");
+static_assert(GGML_OP_COUNT == 51, "GGML_OP_COUNT != 51");
static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
- int n_head) {
+ int n_head,
+ float bias_max) {
GGML_ASSERT(n_past >= 0);
bool is_node = false;
((int32_t *) b->data)[0] = n_past;
((int32_t *) b->data)[1] = n_head;
+ GGML_ASSERT(sizeof(float) == sizeof(int32_t));
+ (((float *) b->data)[2]) = bias_max;
+
ggml_scratch_load(ctx);
return result;
}
+// ggml_alibi
+
+struct ggml_tensor * ggml_clamp(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ float min,
+ float max) {
+ bool is_node = false;
+
+ if (a->grad) {
+ GGML_ASSERT(false); // TODO: implement backward
+ is_node = true;
+ }
+
+ // TODO: when implement backward, fix this:
+ struct ggml_tensor * result = ggml_view_tensor(ctx, a);
+
+ struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 3);
+ ((float *) b->data)[0] = min;
+ ((float *) b->data)[1] = max;
+
+
+ result->op = GGML_OP_CLAMP;
+ result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->src0 = a;
+ result->src1 = b;
+
+ return result;
+}
+
// ggml_conv_1d_1s
struct ggml_tensor * ggml_conv_1d_1s(
struct ggml_tensor * dst) {
assert(params->ith == 0);
assert(src1->type == GGML_TYPE_I32);
- assert(ggml_nelements(src1) == 2);
+ assert(ggml_nelements(src1) == 3);
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
const int n_past = ((int32_t *) src1->data)[0];
const int n_head = ((int32_t *) src1->data)[1];
+ const float max_bias = ((float *) src1->data)[2];
assert(n_past >= 0);
// add alibi to src0 (KQ_scaled)
const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
- const float m0 = powf(2.0f, -8.0f / n_heads_log2_floor);
- const float m1 = powf(2.0f, -4.0f / n_heads_log2_floor);
+ const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
+ const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
for (int i = 0; i < ne0; i++) {
for (int j = 0; j < ne1; j++) {
m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
}
- pdst[0] = i * m_k + src[0];
+ pdst[0] = (i-ne0+1) * m_k + src[0];
+
}
}
}
struct ggml_tensor * dst) {
assert(params->ith == 0);
assert(src1->type == GGML_TYPE_I32);
- assert(ggml_nelements(src1) == 2);
+ assert(ggml_nelements(src1) == 3);
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
const int n_past = ((int32_t *) src1->data)[0];
const int n_head = ((int32_t *) src1->data)[1];
+ const float max_bias = ((float *) src1->data)[2];
assert(n_past >= 0);
// add alibi to src0 (KQ_scaled)
const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
- const float m0 = powf(2.0f, -8.0f / n_heads_log2_floor);
- const float m1 = powf(2.0f, -4.0f / n_heads_log2_floor);
+ const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
+ const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
for (int i = 0; i < ne0; i++) {
for (int j = 0; j < ne1; j++) {
}
// we return F32
- pdst[0] = i * m_k + GGML_FP16_TO_FP32(src[0]);
+ pdst[0] = (i-ne0+1) * m_k + GGML_FP16_TO_FP32(src[0]);
}
}
}
}
}
+
+// ggml_compute_forward_alibi
+
+static void ggml_compute_forward_clamp_f32(
+ const struct ggml_compute_params * params,
+ const struct ggml_tensor * src0,
+ const struct ggml_tensor * src1,
+ struct ggml_tensor * dst) {
+ assert(params->ith == 0);
+ assert(src1->type == GGML_TYPE_I32);
+ assert(ggml_nelements(src1) == 2);
+
+ if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+ return;
+ }
+
+ const int min = ((float *) src1->data)[0];
+ const int max = ((float *) src1->data)[1];
+
+
+ const int ith = params->ith;
+ const int nth = params->nth;
+
+ const int n = ggml_nrows(src0);
+ const int nc = src0->ne[0];
+
+ const size_t nb00 = src0->nb[0];
+ const size_t nb01 = src0->nb[1];
+
+ const size_t nb0 = dst->nb[0];
+ const size_t nb1 = dst->nb[1];
+
+ GGML_ASSERT( nb0 == sizeof(float));
+ GGML_ASSERT(nb00 == sizeof(float));
+
+ for (int j = ith; j < n; j += nth) {
+ float * dst_ptr = (float *) ((char *) dst->data + j*nb1);
+ float * src0_ptr = (float *) ((char *) src0->data + j*nb01);
+ for (int i = 0; i < nc; i++) {
+
+ dst_ptr[i] = MAX(MIN(src0_ptr[i], max), min);
+ }
+ }
+}
+
+
+static void ggml_compute_forward_clamp(
+ const struct ggml_compute_params * params,
+ const struct ggml_tensor * src0,
+ const struct ggml_tensor * src1,
+ struct ggml_tensor * dst) {
+ switch (src0->type) {
+ case GGML_TYPE_F32:
+ {
+ ggml_compute_forward_clamp_f32(params, src0, src1, dst);
+ } break;
+ case GGML_TYPE_F16:
+ case GGML_TYPE_Q4_0:
+ case GGML_TYPE_Q4_1:
+ case GGML_TYPE_Q5_0:
+ case GGML_TYPE_Q5_1:
+ case GGML_TYPE_Q8_0:
+ case GGML_TYPE_Q8_1:
+ case GGML_TYPE_I8:
+ case GGML_TYPE_I16:
+ case GGML_TYPE_I32:
+ case GGML_TYPE_COUNT:
+ {
+ GGML_ASSERT(false);
+ } break;
+ }
+}
+
// ggml_compute_forward_rope
static void ggml_compute_forward_rope_f32(
{
ggml_compute_forward_alibi(params, tensor->src0, tensor->src1, tensor);
} break;
+ case GGML_OP_CLAMP:
+ {
+ ggml_compute_forward_clamp(params, tensor->src0, tensor->src1, tensor);
+ } break;
case GGML_OP_CONV_1D_1S:
{
ggml_compute_forward_conv_1d_1s(params, tensor->src0, tensor->src1, tensor);
{
GGML_ASSERT(false); // TODO: not implemented
} break;
+ case GGML_OP_CLAMP:
+ {
+ GGML_ASSERT(false); // TODO: not implemented
+ } break;
case GGML_OP_SILU:
{
// necessary for llama
{
node->n_tasks = 1; //TODO
} break;
+ case GGML_OP_CLAMP:
+ {
+ node->n_tasks = 1; //TODO
+ } break;
case GGML_OP_CONV_1D_1S:
case GGML_OP_CONV_1D_2S:
{
overview / pkg / ggml / sources / ggml / commitdiff