This was hacked in an evening - I have no idea if it works correctly.
-So far, I've tested just the 7B model.
-Here is a typical run:
+Here is a typical run using LLaMA-7B:
```java
-make -j && ./main -m ../LLaMA-4bit/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -t 8 -n 512
-I llama.cpp build info:
+make -j && ./main -m ./models/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -t 8 -n 512
+I llama.cpp build info:
I UNAME_S: Darwin
I UNAME_P: arm
I UNAME_M: arm64
make: Nothing to be done for `default'.
main: seed = 1678486056
-llama_model_load: loading model from '../LLaMA-4bit/7B/ggml-model-q4_0.bin' - please wait ...
+llama_model_load: loading model from './models/7B/ggml-model-q4_0.bin' - please wait ...
llama_model_load: n_vocab = 32000
llama_model_load: n_ctx = 512
llama_model_load: n_embd = 4096
## Usage
+Here are the step for the LLaMA-7B model:
+
```bash
# build this repo
git clone https://github.com/ggerganov/llama.cpp
./main -m ./models/7B/ggml-model-q4_0.bin -t 8 -n 128
```
+For the bigger models, there are a few extra quantization steps. For example, for LLaMA-13B, converting to FP16 format
+will create 2 ggml files, instead of one:
+
+```bash
+ggml-model-f16.bin
+ggml-model-f16.bin.1
+```
+
+You need to quantize each of them separately like this:
+
+```bash
+./quantize ./models/13B/ggml-model-f16.bin ./models/13B/ggml-model-q4_0.bin 2
+./quantize ./models/13B/ggml-model-f16.bin.1 ./models/13B/ggml-model-q4_0.bin.1 2
+```
+
+Everything else is the same. Simply run:
+
+```bash
+./main -m ./models/13B/ggml-model-q4_0.bin -t 8 -n 128
+```
+
+The number of files generated for each model is as follows:
+
+```
+7B -> 1 file
+13B -> 2 files
+33B -> 4 files
+65B -> 8 files
+```
+
+When running the larger models, make sure you have enough disk space to store all the intermediate files.
+
## Limitations
-- Currently, only LLaMA-7B is supported since I haven't figured out how to merge the tensors of the bigger models. However, in theory, you should be able to run 65B on a 64GB MacBook
- Not sure if my tokenizer is correct. There are a few places where we might have a mistake:
- https://github.com/ggerganov/llama.cpp/blob/26c084662903ddaca19bef982831bfb0856e8257/convert-pth-to-ggml.py#L79-L87
- https://github.com/ggerganov/llama.cpp/blob/26c084662903ddaca19bef982831bfb0856e8257/utils.h#L65-L69
# output in the same directory as the model
dir_model = sys.argv[1]
-fname_out = sys.argv[1] + "/ggml-model.bin"
fname_hparams = sys.argv[1] + "/params.json"
-fname_model = sys.argv[1] + "/consolidated.00.pth"
fname_tokenizer = sys.argv[1] + "/../tokenizer.model"
+def get_n_parts(dim):
+ if dim == 4096:
+ return 1
+ elif dim == 5120:
+ return 2
+ elif dim == 6656:
+ return 4
+ elif dim == 8192:
+ return 8
+ else:
+ print("Invalid dim: " + str(dim))
+ sys.exit(1)
+
# possible data types
# ftype == 0 -> float32
# ftype == 1 -> float16
hparams.update({"vocab_size": tokenizer.vocab_size()})
+n_parts = get_n_parts(hparams["dim"])
+
print(hparams)
+print('n_parts = ', n_parts)
-model = torch.load(fname_model, map_location="cpu")
-
-fout = open(fname_out, "wb")
-
-fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
-fout.write(struct.pack("i", hparams["vocab_size"]))
-fout.write(struct.pack("i", hparams["dim"]))
-fout.write(struct.pack("i", hparams["multiple_of"]))
-fout.write(struct.pack("i", hparams["n_heads"]))
-fout.write(struct.pack("i", hparams["n_layers"]))
-fout.write(struct.pack("i", hparams["dim"] // hparams["n_heads"])) # rot (obsolete)
-fout.write(struct.pack("i", ftype))
-
-# Is this correct??
-for i in range(32000):
- # TODO: this is probably wrong - not sure how this tokenizer works
- text = tokenizer.decode([29889, 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 k, v in model.items():
- name = k
- shape = v.shape
-
- # skip layers.X.attention.inner_attention.rope.freqs
- if name[-5:] == "freqs":
- continue
-
- print("Processing variable: " + name + " with shape: ", shape, " and type: ", v.dtype)
-
- #data = tf.train.load_variable(dir_model, name).squeeze()
- data = v.numpy().squeeze()
- n_dims = len(data.shape);
-
- # for efficiency - transpose some matrices
- # "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_proj/w" or \
- # name[-11:] == "/mlp/c_fc/w" or \
- # name[-13:] == "/mlp/c_proj/w":
- # print(" Transposing")
- # data = data.transpose()
-
- dshape = data.shape
-
- # default type is fp16
- ftype_cur = 1
- if ftype == 0 or n_dims == 1:
- 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", dshape[n_dims - 1 - i]))
- fout.write(str);
-
- # data
- data.tofile(fout)
-
-fout.close()
-
-print("Done. Output file: " + fname_out)
-print("")
+for p in range(n_parts):
+ print('Processing part ', p)
+
+ #fname_model = sys.argv[1] + "/consolidated.00.pth"
+ fname_model = sys.argv[1] + "/consolidated.0" + str(p) + ".pth"
+ fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin"
+ if (p > 0):
+ fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin" + "." + str(p)
+
+ model = torch.load(fname_model, map_location="cpu")
+
+ fout = open(fname_out, "wb")
+
+ fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
+ fout.write(struct.pack("i", hparams["vocab_size"]))
+ fout.write(struct.pack("i", hparams["dim"]))
+ fout.write(struct.pack("i", hparams["multiple_of"]))
+ fout.write(struct.pack("i", hparams["n_heads"]))
+ fout.write(struct.pack("i", hparams["n_layers"]))
+ fout.write(struct.pack("i", hparams["dim"] // hparams["n_heads"])) # rot (obsolete)
+ fout.write(struct.pack("i", ftype))
+
+ # Is this correct??
+ for i in range(32000):
+ # TODO: this is probably wrong - not sure how this tokenizer works
+ text = tokenizer.decode([29889, 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 k, v in model.items():
+ name = k
+ shape = v.shape
+
+ # skip layers.X.attention.inner_attention.rope.freqs
+ if name[-5:] == "freqs":
+ continue
+
+ print("Processing variable: " + name + " with shape: ", shape, " and type: ", v.dtype)
+
+ #data = tf.train.load_variable(dir_model, name).squeeze()
+ data = v.numpy().squeeze()
+ n_dims = len(data.shape);
+
+ # for efficiency - transpose some matrices
+ # "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_proj/w" or \
+ # name[-11:] == "/mlp/c_fc/w" or \
+ # name[-13:] == "/mlp/c_proj/w":
+ # print(" Transposing")
+ # data = data.transpose()
+
+ dshape = data.shape
+
+ # default type is fp16
+ ftype_cur = 1
+ if ftype == 0 or n_dims == 1:
+ print(" Converting to float32")
+ data = data.astype(np.float32)
+ ftype_cur = 0
+
+ # header
+ sname = name.encode('utf-8')
+ fout.write(struct.pack("iii", n_dims, len(sname), ftype_cur))
+ for i in range(n_dims):
+ fout.write(struct.pack("i", dshape[n_dims - 1 - i]))
+ fout.write(sname);
+
+ # data
+ data.tofile(fout)
+
+ # I hope this deallocates the memory ..
+ model = None
+
+ fout.close()
+
+ print("Done. Output file: " + fname_out + ", (part ", p, ")")
+ print("")
assert(k % QK == 0);
const int nb = k / QK;
+ const size_t bs = sizeof(float) + QK/2;
- float * restrict pd = (float *) (y);
- uint8_t * restrict pb = (uint8_t *) (pd + nb);
+ uint8_t * restrict pd = (uint8_t *) (y + 0*bs);
+ uint8_t * restrict pb = (uint8_t *) (y + 0*bs + sizeof(float));
uint8_t pp[QK/2];
const float d = amax / ((1 << 3) - 1);
const float id = d ? 1.0/d : 0.0;
- pd[i] = d;
+ *(float *)pd = d;
+ pd += bs;
for (int l = 0; l < 8; l++) {
const float32x4_t v = vmulq_n_f32(srcv[l], id);
pp[2*l + 1] = vgetq_lane_s32(vi, 2) | (vgetq_lane_s32(vi, 3) << 4);
}
- memcpy(pb + i*16, pp, sizeof(pp));
+ memcpy(pb, pp, sizeof(pp));
+ pb += bs;
}
#else
#error "not implemented for QK"
const float d = amax / ((1 << 3) - 1);
const float id = d ? 1.0/d : 0.0;
- pd[i] = d;
+ *(float *)pd = d;
+ pd += bs;
for (int l = 0; l < 8; l++) {
const v128_t v = wasm_f32x4_mul(srcv[l], wasm_f32x4_splat(id));
pp[2*l + 1] = wasm_i32x4_extract_lane(vi, 2) | (wasm_i32x4_extract_lane(vi, 3) << 4);
}
- memcpy(pb + i*16, pp, sizeof(pp));
+ memcpy(pb, pp, sizeof(pp));
+ pb += bs;
}
#else
#error "not implemented for QK"
const float d = amax / ((1 << 3) - 1);
const float id = d ? 1.0f/d : 0.0f;
- pd[i] = d;
+ *(float *)pd = d;
+ pd += bs;
for (int l = 0; l < QK; l += 2) {
const float v0 = x[i*QK + l + 0]*id;
pp[l/2] = vi0 | (vi1 << 4);
}
- memcpy(pb + i*QK/2, pp, sizeof(pp));
+ memcpy(pb, pp, sizeof(pp));
+ pb += bs;
}
#endif
}
assert(k % QK == 0);
const int nb = k / QK;
+ const size_t bs = sizeof(float) + QK/2;
- const float * restrict pd = (const float *) (x);
- const uint8_t * restrict pb = (const uint8_t *) (pd + nb);
+ const uint8_t * restrict pd = (const uint8_t *) (x + 0*bs);
+ const uint8_t * restrict pb = (const uint8_t *) (x + 0*bs + sizeof(float));
// scalar
for (int i = 0; i < nb; i++) {
- const float d = pd[i];
+ const float d = *(const float *) (pd + i*bs);
- const uint8_t * restrict pp = pb + i*QK/2;
+ const uint8_t * restrict pp = pb + i*bs;
for (int l = 0; l < QK; l += 2) {
const uint8_t vi = pp[l/2];
const float v0 = (vi0 - 8)*d;
const float v1 = (vi1 - 8)*d;
+ //printf("d = %f, vi = %d, vi0 = %d, vi1 = %d, v0 = %f, v1 = %f\n", d, vi, vi0, vi1, v0, v1);
+
y[i*QK + l + 0] = v0;
y[i*QK + l + 1] = v1;
assert(n % QK == 0);
assert(nb % 2 == 0);
- const float * restrict pd0 = (const float *) x;
- const float * restrict pd1 = (const float *) y;
+ const size_t bs = sizeof(float) + QK/2;
- const uint8_t * restrict pb0 = (const uint8_t *) (pd0 + nb);
- const uint8_t * restrict pb1 = (const uint8_t *) (pd1 + nb);
+ const uint8_t * restrict pd0 = (const uint8_t *) (x + 0*bs);
+ const uint8_t * restrict pd1 = (const uint8_t *) (y + 0*bs);
+
+ const uint8_t * restrict pb0 = (const uint8_t *) (x + 0*bs + sizeof(float));
+ const uint8_t * restrict pb1 = (const uint8_t *) (y + 0*bs + sizeof(float));
float sumf = 0.0;
float sum1 = 0.0f;
for (int i = 0; i < nb; i += 2) {
- const float d0_0 = pd0[i + 0];
- const float d1_0 = pd1[i + 0];
- const float d0_1 = pd0[i + 1];
- const float d1_1 = pd1[i + 1];
+ const float d0_0 = *(const float *) (pd0 + i*bs);
+ const float d1_0 = *(const float *) (pd1 + i*bs);
+ const float d0_1 = *(const float *) (pd0 + (i + 1)*bs);
+ const float d1_1 = *(const float *) (pd1 + (i + 1)*bs);
//printf("d0_0: %f, d1_0: %f, d0_1: %f, d1_1: %f\n", d0_0, d1_0, d0_1, d1_1);
- const uint8_t * restrict p0 = pb0 + i*16;
- const uint8_t * restrict p1 = pb1 + i*16;
+ const uint8_t * restrict p0 = pb0 + i*bs;
+ const uint8_t * restrict p1 = pb1 + i*bs;
const uint8x16_t m4b = vdupq_n_u8(0xf);
const int8x16_t s8b = vdupq_n_s8(0x8);
const uint8x16_t v0_0 = vld1q_u8(p0);
const uint8x16_t v1_0 = vld1q_u8(p1);
- const uint8x16_t v0_1 = vld1q_u8(p0 + 16);
- const uint8x16_t v1_1 = vld1q_u8(p1 + 16);
+ const uint8x16_t v0_1 = vld1q_u8(p0 + bs);
+ const uint8x16_t v1_1 = vld1q_u8(p1 + bs);
// 4-bit -> 8-bit
const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8(v0_0, m4b));
float sum1 = 0.0f;
for (int i = 0; i < nb; i += 2) {
- const float d0_0 = pd0[i + 0];
- const float d0_1 = pd0[i + 1];
- const float d1_0 = pd1[i + 0];
- const float d1_1 = pd1[i + 1];
+ const float d0_0 = *(const float *) (pd0 + i*bs);
+ const float d1_0 = *(const float *) (pd1 + i*bs);
+ const float d0_1 = *(const float *) (pd0 + (i + 1)*bs);
+ const float d1_1 = *(const float *) (pd1 + (i + 1)*bs);
- const uint8_t * restrict p0 = pb0 + i*16;
- const uint8_t * restrict p1 = pb1 + i*16;
+ const uint8_t * restrict p0 = pb0 + i*bs;
+ const uint8_t * restrict p1 = pb1 + i*bs;
const v128_t m4b = wasm_u8x16_splat(0xf);
const v128_t s8b = wasm_i8x16_splat(0x8);
const v128_t v0_0 = wasm_v128_load(p0);
- const v128_t v0_1 = wasm_v128_load(p0 + 16);
+ const v128_t v0_1 = wasm_v128_load(p0 + bs);
const v128_t v1_0 = wasm_v128_load(p1);
- const v128_t v1_1 = wasm_v128_load(p1 + 16);
+ const v128_t v1_1 = wasm_v128_load(p1 + bs);
// 4-bit -> 8-bit
const v128_t v0_0l = wasm_v128_and(v0_0, m4b);
#else
// scalar
for (int i = 0; i < nb; i++) {
- const float d0 = pd0[i];
- const float d1 = pd1[i];
+ const float d0 = *(const float *) (pd0 + i*bs);
+ const float d1 = *(const float *) (pd1 + i*bs);
- const uint8_t * restrict p0 = pb0 + i*QK/2;
- const uint8_t * restrict p1 = pb1 + i*QK/2;
+ const uint8_t * restrict p0 = pb0 + i*bs;
+ const uint8_t * restrict p1 = pb1 + i*bs;
for (int j = 0; j < QK/2; j++) {
const uint8_t v0 = p0[j];
assert(n % QK == 0);
const int nb = n / QK;
+ const size_t bs = sizeof(float) + QK/2;
- const float * restrict pd = (const float *) (x);
- const uint8_t * restrict pb = (const uint8_t *) (pd + nb);
+ const uint8_t * restrict pd = (const uint8_t *) (x + 0*bs);
+ const uint8_t * restrict pb = (const uint8_t *) (x + 0*bs + sizeof(float));
#if __ARM_NEON
#if QK == 32
for (int i = 0; i < nb; ++i) {
- const float d0 = pd[i]*v;
+ const float d0 = v*(*(const float *) (pd + i*bs));
- const uint8_t * restrict pp = pb + i*16;
+ const uint8_t * restrict pp = pb + i*bs;
const uint8x8_t m4b = vdup_n_u8(0xf);
const int8x8_t s8b = vdup_n_s8(0x8);
#else
// scalar
for (int i = 0; i < nb; i++) {
- const float d = pd[i];
+ const float d = *(const float *) (pd + i*bs);
- const uint8_t * restrict pp = pb + i*QK/2;
+ const uint8_t * restrict pp = pb + i*bs;
for (int l = 0; l < QK; l += 2) {
const uint8_t vi = pp[l/2];
#include <string>
#include <vector>
+// determine number of model parts based on the dimension
+static const std::map<int, int> LLAMA_N_PARTS = {
+ { 4096, 1 },
+ { 5120, 2 },
+ { 6656, 4 },
+ { 8192, 8 },
+};
+
// default hparams (LLaMA 7B)
struct llama_hparams {
int32_t n_vocab = 32000;
}
int n_ff = 0;
+ int n_parts = 0;
// load hparams
{
hparams.n_ctx = n_ctx;
n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
+ n_parts = LLAMA_N_PARTS.at(hparams.n_embd);
printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
printf("%s: n_rot = %d\n", __func__, hparams.n_rot);
printf("%s: f16 = %d\n", __func__, hparams.f16);
printf("%s: n_ff = %d\n", __func__, n_ff);
+ printf("%s: n_parts = %d\n", __func__, n_parts);
}
// load vocab
model.layers.resize(n_layer);
- model.tok_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
+ model.tok_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
model.norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
model.output = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
for (int i = 0; i < n_layer; ++i) {
auto & layer = model.layers[i];
- layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+ layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
- layer.wq = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
- layer.wk = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
- layer.wv = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
- layer.wo = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
+ layer.wq = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
+ layer.wk = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
+ layer.wv = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
+ layer.wo = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
- layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+ layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
layer.w1 = ggml_new_tensor_2d(ctx, wtype, n_embd, n_ff);
layer.w2 = ggml_new_tensor_2d(ctx, wtype, n_ff, n_embd);
printf("%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
}
- // load weights
- {
- int n_tensors = 0;
- size_t total_size = 0;
+ const size_t file_offset = fin.tellg();
- printf("%s: ", __func__);
+ fin.close();
- while (true) {
- int32_t n_dims;
- int32_t length;
- int32_t ftype;
+ std::vector<uint8_t> tmp;
- fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
- fin.read(reinterpret_cast<char *>(&length), sizeof(length));
- fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
+ for (int i = 0; i < n_parts; ++i) {
+ const int part_id = i;
+ //const int part_id = n_parts - i - 1;
- if (fin.eof()) {
- break;
- }
+ std::string fname_part = fname;
+ if (i > 0) {
+ fname_part += "." + std::to_string(i);
+ }
- 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];
- }
+ printf("%s: loading model part %d/%d from '%s'\n", __func__, i+1, n_parts, fname_part.c_str());
- std::string name(length, 0);
- fin.read(&name[0], length);
+ fin = std::ifstream(fname_part, std::ios::binary);
+ fin.seekg(file_offset);
- if (model.tensors.find(name.data()) == model.tensors.end()) {
- fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
- return false;
- }
+ // load weights
+ {
+ int n_tensors = 0;
+ size_t total_size = 0;
- 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;
- }
+ printf("%s: ", __func__);
- if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
- fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
- __func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
- return false;
- }
+ while (true) {
+ int32_t n_dims;
+ int32_t length;
+ int32_t ftype;
- if (0) {
- static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
- printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ftype_str[ftype], ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));
- }
+ fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
+ fin.read(reinterpret_cast<char *>(&length), sizeof(length));
+ fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
+
+ 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];
+ }
- size_t bpe = 0;
+ std::string name(length, 0);
+ fin.read(&name[0], length);
- switch (ftype) {
- case 0: bpe = ggml_type_size(GGML_TYPE_F32); break;
- case 1: bpe = ggml_type_size(GGML_TYPE_F16); break;
- case 2: bpe = ggml_type_size(GGML_TYPE_Q4_0); assert(ne[0] % 64 == 0); break;
- case 3: bpe = ggml_type_size(GGML_TYPE_Q4_1); assert(ne[0] % 64 == 0); break;
- default:
- {
- fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype);
+ if (model.tensors.find(name.data()) == model.tensors.end()) {
+ fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
+ return false;
+ }
+
+ // split_type = 0: split by columns
+ // split_type = 1: split by rows
+ int split_type = 0;
+
+ // split_type = 0:
+ // regex:
+ // - tok_embeddings.*
+ // - layers.*.attention.wo.weight
+ // - layers.*.feed_forward.w2.weight
+
+ // split_type = 1:
+ // regex:
+ // - output.*
+ // - layers.*.attention.wq.weight
+ // - layers.*.attention.wk.weight
+ // - layers.*.attention.wv.weight
+ // - layers.*.feed_forward.w1.weight
+ // - layers.*.feed_forward.w3.weight
+ if (name.find("tok_embeddings") != std::string::npos) {
+ split_type = 0;
+ } else if (name.find("layers") != std::string::npos) {
+ if (name.find("attention.wo.weight") != std::string::npos) {
+ split_type = 0;
+ } else if (name.find("feed_forward.w2.weight") != std::string::npos) {
+ split_type = 0;
+ } else {
+ split_type = 1;
+ }
+ } else if (name.find("output") != std::string::npos) {
+ split_type = 1;
+ }
+
+ auto tensor = model.tensors[name.data()];
+
+ if (n_dims == 1) {
+ if (ggml_nelements(tensor) != nelements) {
+ fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
+ return false;
+ }
+ } else {
+ if (ggml_nelements(tensor)/n_parts != nelements) {
+ fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
+ return false;
+ }
+ }
+
+ if (n_dims == 1) {
+ if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
+ fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
+ __func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
+ return false;
+ }
+ } else {
+ if (split_type == 0) {
+ if (tensor->ne[0]/n_parts != ne[0] || tensor->ne[1] != ne[1]) {
+ fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
+ __func__, name.data(), tensor->ne[0]/n_parts, tensor->ne[1], ne[0], ne[1]);
+ return false;
+ }
+ } else {
+ if (tensor->ne[0] != ne[0] || tensor->ne[1]/n_parts != ne[1]) {
+ fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
+ __func__, name.data(), tensor->ne[0], tensor->ne[1]/n_parts, ne[0], ne[1]);
return false;
}
- };
+ }
+ }
- 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;
- }
+ if (0) {
+ static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
+ printf("%24s - [%5d, %5d], type = %6s, split = %d\n", name.data(), ne[0], ne[1], ftype_str[ftype], split_type);
+ }
+
+ size_t bpe = 0;
+
+ switch (ftype) {
+ case 0: bpe = ggml_type_size(GGML_TYPE_F32); break;
+ case 1: bpe = ggml_type_size(GGML_TYPE_F16); break;
+ case 2: bpe = ggml_type_size(GGML_TYPE_Q4_0); assert(ne[0] % 64 == 0); break;
+ case 3: bpe = ggml_type_size(GGML_TYPE_Q4_1); assert(ne[0] % 64 == 0); break;
+ default:
+ {
+ fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype);
+ return false;
+ }
+ };
+
+ if (n_dims == 1 || n_parts == 1) {
+ if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
+ fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
+ __func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
+ return false;
+ }
+
+ if (part_id == 0) {
+ fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
+ } else {
+ fin.seekg(ggml_nbytes(tensor), std::ios::cur);
+ }
+
+ total_size += ggml_nbytes(tensor);
+ } else {
+ if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)/n_parts) {
+ fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
+ __func__, name.data(), ggml_nbytes(tensor)/n_parts, nelements*bpe);
+ return false;
+ }
+
+ if (split_type == 0) {
+ const int np0 = ne[0];
+
+ const size_t row_size = (tensor->ne[0]/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
+ assert(row_size == tensor->nb[1]);
+
+ for (int i1 = 0; i1 < ne[1]; ++i1) {
+ const size_t offset_row = i1*row_size;
+ const size_t offset = offset_row + ((part_id*np0)/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
+ fin.read(reinterpret_cast<char *>(tensor->data) + offset, row_size/n_parts);
+ }
+ } else {
+ const int np1 = ne[1];
- fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
+ const size_t row_size = (tensor->ne[0]/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
- //printf("%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
- total_size += ggml_nbytes(tensor);
- if (++n_tensors % 8 == 0) {
- printf(".");
- fflush(stdout);
+ for (int i1 = 0; i1 < ne[1]; ++i1) {
+ const size_t offset_row = (i1 + part_id*np1)*row_size;
+ fin.read(reinterpret_cast<char *>(tensor->data) + offset_row, row_size);
+ }
+ }
+
+ total_size += ggml_nbytes(tensor)/n_parts;
+ }
+
+ //printf("%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
+ if (++n_tensors % 8 == 0) {
+ printf(".");
+ fflush(stdout);
+ }
}
- }
- printf(" done\n");
+ printf(" done\n");
- printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
- }
+ printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
+ }
- fin.close();
+ fin.close();
+ }
return true;
}
size_t ggml_quantize_q4_0(float * src, void * dst, int n, int k, int qk, int64_t * hist) {
const int nb = k / qk;
- const size_t row_size = nb*(sizeof(float) + sizeof(uint8_t)*qk/2);
+ const size_t bs = (sizeof(float) + sizeof(uint8_t)*qk/2);
+ const size_t row_size = nb*bs;
assert(k % qk == 0);
char * pdst = (char *) dst;
for (int j = 0; j < n; j += k) {
- float * pd = (float *) (pdst + (j/k)*row_size);
- uint8_t * pb = (uint8_t *) (pd + nb);
+ uint8_t * pd = (uint8_t *) (pdst + (j/k)*row_size + 0*bs);
+ uint8_t * pb = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + sizeof(float));
for (int i = 0; i < nb; i++) {
float amax = 0.0f; // absolute max
const float d = amax / ((1 << 3) - 1);
const float id = d ? 1.0f/d : 0.0f;
- pd[i] = d;
+ *(float *) pd = d;
+ pd += bs;
for (int l = 0; l < qk; l += 2) {
const float v0 = (src[j + i*qk + l + 0])*id;
pp[l/2] = vi0 | (vi1 << 4);
}
- memcpy(pb + i*qk/2, pp, sizeof(pp));
+ memcpy(pb, pp, sizeof(pp));
+ pb += bs;
}
}
}
overview / pkg / ggml / sources / llama.cpp / commitdiff