--- /dev/null
+# Convert HF models to ggml format
+#
+
+import sys
+import struct
+import json
+import torch
+import numpy as np
+import re
+import os
+
+from transformers import AutoModelForCausalLM
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BloomForCausalLM
+
+# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
+def bytes_to_unicode():
+ """
+ Returns list of utf-8 byte and a corresponding list of unicode strings.
+ The reversible bpe codes work on unicode strings.
+ This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
+ When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
+ This is a signficant percentage of your normal, say, 32K bpe vocab.
+ To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
+ And avoids mapping to whitespace/control characters the bpe code barfs on.
+ """
+ bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
+ cs = bs[:]
+ n = 0
+ for b in range(2**8):
+ if b not in bs:
+ bs.append(b)
+ cs.append(2**8+n)
+ n += 1
+ cs = [chr(n) for n in cs]
+ return dict(zip(bs, cs))
+
+if len(sys.argv) < 2:
+ print("Usage: python convert-hf-to-ggml.py hf-model-name [use-f32]")
+ print("Example: python convert-hf-to-ggml.py bigcode/gpt_bigcode-santacoder")
+ print("Example: python convert-hf-to-ggml.py bigcode/starcoder")
+ sys.exit(1)
+
+model_name = sys.argv[1].strip()
+fname_out = "models/" + sys.argv[1].strip() + "-ggml.bin"
+os.makedirs(os.path.dirname(fname_out), exist_ok=True)
+
+
+
+# use 16-bit or 32-bit floats
+use_f16 = True
+if len(sys.argv) > 2:
+ use_f16 = False
+
+print("Loading model: ", model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+hparams = config.to_dict()
+model = AutoModelForCausalLM.from_pretrained(model_name, config=config, torch_dtype=torch.float16 if use_f16 else torch.float32, low_cpu_mem_usage=True, trust_remote_code=True, offload_state_dict=True)
+print("Model loaded: ", model_name)
+
+#print (model)
+
+list_vars = model.state_dict()
+#print (list_vars)
+
+encoder = tokenizer.vocab
+# Add added_tokens (special tokens) to the encoder
+encoder.update(tokenizer.get_added_vocab())
+print(hparams)
+
+print("Saving ggml model to: ", fname_out)
+fout = open(fname_out, "wb")
+
+fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
+vocab_size = hparams["vocab_size"]
+fout.write(struct.pack("i", vocab_size))
+# fout.write(struct.pack("i", len(encoder)))
+fout.write(struct.pack("i", hparams["n_positions"]))
+fout.write(struct.pack("i", hparams["n_embd"]))
+fout.write(struct.pack("i", hparams["n_head"]))
+fout.write(struct.pack("i", hparams["n_layer"]))
+fout.write(struct.pack("i", use_f16))
+
+byte_encoder = bytes_to_unicode()
+byte_decoder = {v:k for k, v in byte_encoder.items()}
+
+fout.write(struct.pack("i", vocab_size))
+
+counter = 0
+# sort by value
+for key in sorted(encoder, key=encoder.get):
+ text = bytearray([byte_decoder[c] for c in key])
+ fout.write(struct.pack("i", len(text)))
+ fout.write(text)
+ counter += 1
+
+# TODO: Repeat last token until vocab_size
+while counter < vocab_size:
+ fout.write(struct.pack("i", len(text)))
+ fout.write(text)
+ counter += 1
+# assert counter == config.vocab_size
+
+for name in list_vars.keys():
+ data = list_vars[name].squeeze().numpy()
+ print("Processing variable: " + name + " with shape: ", data.shape)
+
+ # rename headers to keep compatibility
+ if name == "transformer.ln_f.weight":
+ name = "model/ln_f/g"
+ elif name == "transformer.ln_f.bias":
+ name = "model/ln_f/b"
+ elif name == "transformer.wte.weight":
+ name = "model/wte"
+ elif name == "transformer.wpe.weight":
+ name = "model/wpe"
+ elif name == "lm_head.weight":
+ name = "model/lm_head"
+ elif re.match(r"transformer.h\.\d+\.ln_1\.weight", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/ln_1/g"
+ elif re.match(r"transformer.h\.\d+\.ln_1\.bias", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/ln_1/b"
+ elif re.match(r"transformer.h\.\d+\.attn\.c_attn\.weight", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/attn/c_attn/w"
+ elif re.match(r"transformer.h\.\d+\.attn\.c_attn\.bias", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/attn/c_attn/b"
+ elif re.match(r"transformer.h\.\d+\.attn\.c_proj\.weight", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/attn/c_proj/w"
+ elif re.match(r"transformer.h.\d+.attn.c_proj.bias", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/attn/c_proj/b"
+ elif re.match(r"transformer.h.\d+.ln_2.weight", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/ln_2/g"
+ elif re.match(r"transformer.h.\d+.ln_2.bias", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/ln_2/b"
+ elif re.match(r"transformer.h.\d+.mlp.c_fc.weight", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/mlp/c_fc/w"
+ elif re.match(r"transformer.h.\d+.mlp.c_fc.bias", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/mlp/c_fc/b"
+ elif re.match(r"transformer.h.\d+.mlp.c_proj.weight", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/mlp/c_proj/w"
+ elif re.match(r"transformer.h.\d+.mlp.c_proj.bias", name):
+ i = re.findall("\d+", name)[0]
+ name = f"model/h{i}/mlp/c_proj/b"
+ else:
+ print("Unrecognized variable name. %s", name)
+
+ # we don't need these
+ if name.endswith("attn.masked_bias") or name.endswith(".attn.bias"):
+ print(" Skipping variable: " + name)
+ continue
+
+ n_dims = len(data.shape);
+
+ # ftype == 0 -> float32, ftype == 1 -> float16
+ ftype = 0;
+ if use_f16:
+ if (name == "model/wte" or name == "model/lm_head" or name[-2:] == "/g" or name[-2:] == "/w") and n_dims == 2:
+ print(" Converting to float16")
+ data = data.astype(np.float16)
+ ftype = 1
+ else:
+ print(" Converting to float32")
+ data = data.astype(np.float32)
+ ftype = 0
+
+ "model/h.*/attn/c_attn/w"
+ "model/h.*/attn/c_proj/w"
+ "model/h.*/mlp/c_fc/w"
+ "model/h.*/mlp/c_proj/w"
+ if name[-14:] == "/attn/c_attn/w" or name[-14:] == "/attn/c_attn/b":
+ print(" Duplicate K,V heads to use MHA instead of MQA")
+
+ embed_dim = hparams["n_embd"]
+ head_dim = embed_dim // hparams["n_head"]
+
+ # ((n_heads + 2) * head_dim, hidden_dim) -> (3 * n_heads * head_dim, hidden_dim)
+ q, k ,v = np.split(data, (hparams["n_head"] * head_dim, (hparams["n_head"] + 1) * head_dim), axis=0)
+ # duplicate k, v along the first axis (head_dim, hidden_dim) -> (n_heads * head_dim, hidden_dim)
+ if len(k.shape) == 2:
+ k = np.tile(k, (hparams["n_head"], 1))
+ v = np.tile(v, (hparams["n_head"], 1))
+ elif len(k.shape) == 1:
+ k = np.tile(k, (hparams["n_head"]))
+ v = np.tile(v, (hparams["n_head"]))
+ # concat q, k, v along the first axis (n_heads * head_dim, hidden_dim) -> (3 * n_heads * head_dim, hidden_dim)
+ data = np.concatenate((q, k, v), axis=0)
+
+ # header
+ str = name.encode('utf-8')
+ fout.write(struct.pack("iii", n_dims, len(str), ftype))
+ 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.h"
+#include "common-ggml.h"
+
+#include <cassert>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <fstream>
+#include <map>
+#include <string>
+#include <vector>
+#include <iostream>
+#include <unistd.h>
+
+// default hparams (GPT-2 117M)
+// https://huggingface.co/bigcode/gpt_bigcode-santacoder/blob/main/config.json
+struct gpt2_hparams {
+ int32_t n_vocab = 49280;
+ int32_t n_ctx = 2048;
+ int32_t n_embd = 2048;
+ int32_t n_head = 16;
+ int32_t n_layer = 24;
+ int32_t ftype = 1;
+};
+
+struct gpt2_layer {
+ // normalization
+ struct ggml_tensor * ln_1_g;
+ struct ggml_tensor * ln_1_b;
+
+ struct ggml_tensor * ln_2_g;
+ struct ggml_tensor * ln_2_b;
+
+ // attention
+ struct ggml_tensor * c_attn_attn_w;
+ struct ggml_tensor * c_attn_attn_b;
+
+ struct ggml_tensor * c_attn_proj_w;
+ struct ggml_tensor * c_attn_proj_b;
+
+ // mlp
+ struct ggml_tensor * c_mlp_fc_w;
+ struct ggml_tensor * c_mlp_fc_b;
+
+ struct ggml_tensor * c_mlp_proj_w;
+ struct ggml_tensor * c_mlp_proj_b;
+};
+
+struct gpt2_model {
+ gpt2_hparams hparams;
+
+ // normalization
+ struct ggml_tensor * ln_f_g;
+ struct ggml_tensor * ln_f_b;
+
+ struct ggml_tensor * wte; // position embedding
+ struct ggml_tensor * wpe; // token embedding
+ struct ggml_tensor * lm_head; // language model head
+
+ std::vector<gpt2_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 gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab & vocab) {
+ printf("%s: loading model from '%s'\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.n_vocab, sizeof(hparams.n_vocab));
+ fin.read((char *) &hparams.n_ctx, sizeof(hparams.n_ctx));
+ fin.read((char *) &hparams.n_embd, sizeof(hparams.n_embd));
+ fin.read((char *) &hparams.n_head, sizeof(hparams.n_head));
+ fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
+ fin.read((char *) &hparams.ftype, sizeof(hparams.ftype));
+
+ printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
+ printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
+ printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
+ printf("%s: n_head = %d\n", __func__, hparams.n_head);
+ printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
+ printf("%s: ftype = %d\n", __func__, hparams.ftype);
+ }
+
+ // load vocab
+ {
+ int32_t n_vocab = 0;
+ fin.read((char *) &n_vocab, sizeof(n_vocab));
+
+ if (n_vocab != model.hparams.n_vocab) {
+ fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
+ __func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
+ return false;
+ }
+
+ 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;
+
+ // if (i < 10) 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
+ 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.n_embd;
+ const int n_layer = hparams.n_layer;
+ const int n_ctx = hparams.n_ctx;
+ const int n_vocab = hparams.n_vocab;
+
+ const int head_dim = n_embd / hparams.n_head;
+ const int kv_heads = hparams.n_head; // 1 if MQA else hparams.n_head
+ const int kv_dim = kv_heads * head_dim;
+
+ ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_g
+ ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_b
+
+ ctx_size += n_vocab*n_embd*ggml_type_sizef(wtype); // wte
+ ctx_size += n_ctx*n_embd*ggml_type_sizef(GGML_TYPE_F32); // wpe
+ ctx_size += n_vocab*n_embd*ggml_type_sizef(wtype); // lm_head
+
+ ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_g
+ ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_b
+
+ ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_g
+ ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_b
+
+ ctx_size += n_layer*((n_embd + 2*kv_dim)*n_embd*ggml_type_sizef(wtype)); // c_attn_attn_w // TODO:
+ ctx_size += n_layer*( (n_embd + 2*kv_dim)*ggml_type_sizef(GGML_TYPE_F32)); // c_attn_attn_b
+
+ ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_proj_w
+ ctx_size += n_layer*( n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_attn_proj_b
+
+ ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_fc_w
+ ctx_size += n_layer*( 4*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_fc_b
+
+ ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_proj_w
+ ctx_size += n_layer*( n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_proj_b
+
+ 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 += (6 + 12*n_layer)*256; // 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.n_embd;
+ const int n_layer = hparams.n_layer;
+ const int n_ctx = hparams.n_ctx;
+ const int n_vocab = hparams.n_vocab;
+
+ const int head_dim = n_embd / hparams.n_head;
+ const int kv_heads = hparams.n_head; // 1 if MQA else hparams.n_head
+ const int kv_dim = kv_heads * head_dim;
+
+ model.layers.resize(n_layer);
+
+ model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+ model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+
+ model.wte = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
+ model.wpe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ctx);
+ model.lm_head = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
+
+ // map by name
+ model.tensors["model/ln_f/g"] = model.ln_f_g;
+ model.tensors["model/ln_f/b"] = model.ln_f_b;
+
+ model.tensors["model/wte"] = model.wte;
+ model.tensors["model/wpe"] = model.wpe;
+ model.tensors["model/lm_head"] = model.lm_head;
+
+ for (int i = 0; i < n_layer; ++i) {
+ auto & layer = model.layers[i];
+
+ layer.ln_1_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+ layer.ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+
+ layer.ln_2_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+ layer.ln_2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+
+ layer.c_attn_attn_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd + 2*kv_dim);
+ layer.c_attn_attn_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd + 2*kv_dim);
+
+ layer.c_attn_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
+ layer.c_attn_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+
+ layer.c_mlp_fc_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 4*n_embd); //TODO: 4*n_embd = config.n_inner
+ layer.c_mlp_fc_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);
+
+ layer.c_mlp_proj_w = ggml_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd);
+ layer.c_mlp_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+
+ // map by name
+ model.tensors["model/h" + std::to_string(i) + "/ln_1/g"] = layer.ln_1_g;
+ model.tensors["model/h" + std::to_string(i) + "/ln_1/b"] = layer.ln_1_b;
+
+ model.tensors["model/h" + std::to_string(i) + "/ln_2/g"] = layer.ln_2_g;
+ model.tensors["model/h" + std::to_string(i) + "/ln_2/b"] = layer.ln_2_b;
+
+ model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/w"] = layer.c_attn_attn_w;
+ model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/b"] = layer.c_attn_attn_b;
+
+ model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/w"] = layer.c_attn_proj_w;
+ model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/b"] = layer.c_attn_proj_b;
+
+ model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/w"] = layer.c_mlp_fc_w;
+ model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/b"] = layer.c_mlp_fc_b;
+
+ model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/w"] = layer.c_mlp_proj_w;
+ model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"] = layer.c_mlp_proj_b;
+ }
+ }
+
+ // key + value memory
+ {
+ const auto & hparams = model.hparams;
+
+ const int n_embd = hparams.n_embd;
+ const int n_layer = hparams.n_layer;
+ const int n_ctx = hparams.n_ctx;
+
+ const int n_mem = n_layer*n_ctx;
+ const int n_elements = n_embd*n_mem;
+
+ 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);
+
+ printf("%s: memory size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
+ }
+
+ // load weights
+ {
+ size_t total_size = 0;
+
+ bool has_lm_head = false;
+
+ 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 (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(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]);
+ return false;
+ }
+ if (ggml_nelements(tensor) != nelements) {
+ fprintf(stderr, "%s: tensor '%s' has wrong size in model file. got %d, expected %d\n",
+ __func__, name.data(), (int) ggml_nelements(tensor), nelements);
+ 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));
+
+ // GPT-2 models share the WTE tensor as the LM head
+ if (name == "model/wte" && has_lm_head == false) {
+ memcpy(model.lm_head->data, tensor->data, ggml_nbytes(tensor));
+ }
+
+ if (name == "model/lm_head") {
+ has_lm_head = true;
+ }
+
+ total_size += ggml_nbytes(tensor);
+ }
+
+ printf("%s: model size = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
+ }
+
+ 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 gpt2_eval(
+ const gpt2_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.n_embd;
+ const int n_layer = hparams.n_layer;
+ const int n_ctx = hparams.n_ctx;
+ const int n_head = hparams.n_head;
+ 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 = {};
+ 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 * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
+ for (int i = 0; i < N; ++i) {
+ ((int32_t *) position->data)[i] = n_past + i;
+ }
+
+ // wte + wpe
+ struct ggml_tensor * inpL =
+ ggml_add(ctx0,
+ ggml_get_rows(ctx0, model.wte, embd),
+ ggml_get_rows(ctx0, model.wpe, position));
+
+ for (int il = 0; il < n_layer; ++il) {
+ struct ggml_tensor * cur;
+
+ // norm
+ {
+ // [ 768, N]
+ cur = ggml_norm(ctx0, inpL);
+
+ // cur = ln_1_g*cur + ln_1_b
+ // [ 768, N]
+ cur = ggml_add(ctx0,
+ ggml_mul(ctx0,
+ ggml_repeat(ctx0, model.layers[il].ln_1_g, cur),
+ cur),
+ ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
+ }
+
+ // attn
+ // [2304, 768] - model.layers[il].c_attn_attn_w
+ // [2304, 1] - model.layers[il].c_attn_attn_b
+ // [ 768, N] - cur (in)
+ // [2304, N] - cur (out)
+ //
+ // cur = attn_w*cur + attn_b
+ // [2304, N]
+ {
+ cur = ggml_mul_mat(ctx0,
+ model.layers[il].c_attn_attn_w,
+ cur);
+
+ cur = ggml_add(ctx0,
+ ggml_repeat(ctx0, model.layers[il].c_attn_attn_b, cur),
+ cur);
+ }
+
+ // self-attention
+ {
+ 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
+ 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));
+
+ 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); //TODO: need to be tiled
+
+ // GG: flash attention
+ //struct ggml_tensor * V =
+ // 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, GGML_TYPE_F32, n_past + N, n_embd/n_head, n_head));
+
+ //struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, true);
+
+ // K * Q
+ // [n_past + N, N, 12]
+ struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); //TODO: check if it broadcasts
+
+ // KQ_scaled = KQ / sqrt(n_embd/n_head)
+ // [n_past + N, N, 12]
+ 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)
+ // [n_past + N, N, 12]
+ struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
+
+ // KQ = soft_max(KQ_masked)
+ // [n_past + N, N, 12]
+ 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
+ // [64, N, 12]
+ struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
+
+ // KQV_merged = KQV.permute(0, 2, 1, 3)
+ // [64, 12, N]
+ struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+
+ // cur = KQV_merged.contiguous().view(n_embd, N)
+ // [768, N]
+ cur = ggml_cpy(ctx0,
+ KQV_merged,
+ ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
+ }
+
+ // projection
+ // [ 768, 768] - model.layers[il].c_attn_proj_w
+ // [ 768, 1] - model.layers[il].c_attn_proj_b
+ // [ 768, N] - cur (in)
+ // [ 768, N] - cur (out)
+ //
+ // cur = proj_w*cur + proj_b
+ // [768, N]
+ {
+ cur = ggml_mul_mat(ctx0,
+ model.layers[il].c_attn_proj_w,
+ cur);
+
+ cur = ggml_add(ctx0,
+ ggml_repeat(ctx0, model.layers[il].c_attn_proj_b, cur),
+ cur);
+ }
+
+ // add the input
+ cur = ggml_add(ctx0, cur, inpL);
+
+ struct ggml_tensor * inpFF = cur;
+
+ // feed-forward network
+ {
+ // norm
+ {
+ cur = ggml_norm(ctx0, inpFF);
+
+ // cur = ln_2_g*cur + ln_2_b
+ // [ 768, N]
+ cur = ggml_add(ctx0,
+ ggml_mul(ctx0,
+ ggml_repeat(ctx0, model.layers[il].ln_2_g, cur),
+ cur),
+ ggml_repeat(ctx0, model.layers[il].ln_2_b, cur));
+ }
+
+ // fully connected
+ // [3072, 768] - model.layers[il].c_mlp_fc_w
+ // [3072, 1] - model.layers[il].c_mlp_fc_b
+ // [ 768, N] - cur (in)
+ // [3072, N] - cur (out)
+ //
+ // cur = fc_w*cur + fc_b
+ // [3072, N]
+ cur = ggml_mul_mat(ctx0,
+ model.layers[il].c_mlp_fc_w,
+ cur);
+
+ cur = ggml_add(ctx0,
+ ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
+ cur);
+
+ // GELU activation
+ // [3072, N]
+ cur = ggml_gelu(ctx0, cur);
+
+ // projection
+ // [ 768, 3072] - model.layers[il].c_mlp_proj_w
+ // [ 768, 1] - model.layers[il].c_mlp_proj_b
+ // [3072, N] - cur (in)
+ // [ 768, N] - cur (out)
+ //
+ // cur = proj_w*cur + proj_b
+ // [768, N]
+ cur = ggml_mul_mat(ctx0,
+ model.layers[il].c_mlp_proj_w,
+ cur);
+
+ cur = ggml_add(ctx0,
+ ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),
+ cur);
+ }
+
+ // input for next layer
+ inpL = ggml_add(ctx0, cur, inpFF);
+ }
+
+ // norm
+ {
+ // [ 768, N]
+ inpL = ggml_norm(ctx0, inpL);
+
+ // inpL = ln_f_g*inpL + ln_f_b
+ // [ 768, N]
+ inpL = ggml_add(ctx0,
+ ggml_mul(ctx0,
+ ggml_repeat(ctx0, model.ln_f_g, inpL),
+ inpL),
+ ggml_repeat(ctx0, model.ln_f_b, inpL));
+ }
+
+ // inpL = WTE * inpL
+ // [ 768, 50257] - model.lm_head
+ // [ 768, N] - inpL
+ inpL = ggml_mul_mat(ctx0, model.lm_head, inpL);
+
+ // logits -> probs
+ //inpL = ggml_soft_max(ctx0, inpL);
+
+ // run the computation
+ ggml_build_forward_expand(&gf, inpL);
+ ggml_graph_compute (ctx0, &gf);
+
+ //if (n_past%100 == 0) {
+ // ggml_graph_print (&gf);
+ // ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
+ //}
+
+ //embd_w.resize(n_vocab*N);
+ //memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
+
+ // return result just for 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 = "models/gpt-2-117M/ggml-model.bin";
+
+ 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;
+ gpt2_model model;
+
+ // load the model
+ {
+ const int64_t t_start_us = ggml_time_us();
+
+ if (!gpt2_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<gpt_vocab::id> embd_inp = ::gpt_tokenize(vocab, params.prompt);
+
+ params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
+
+ printf("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
+ printf("%s: number of tokens in prompt = %zu, first 8 tokens: ", __func__, embd_inp.size());
+ for (int i = 0; i < std::min(8, (int) embd_inp.size()); i++) {
+ printf("%d ", embd_inp[i]);
+ }
+ printf("\n\n");
+
+ // submit the input prompt token-by-token
+ // this reduces the memory usage during inference, at the cost of a bit of speed at the beginning
+ std::vector<gpt_vocab::id> embd;
+
+ // determine the required inference memory per token:
+ size_t mem_per_token = 0;
+ gpt2_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 (!gpt2_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) { //TODO: this is only for starcoder
+ 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;
+}
overview / pkg / ggml / sources / ggml / commitdiff