78ca9838ee
This is a breaking change that's going to give you three benefits: 1. Your inference commands should load 100x faster 2. You may be able to safely load models 2x larger 3. You can run many concurrent inference processes This was accomplished by changing the file format so we can mmap() weights directly into memory without having to read() or copy them thereby ensuring the kernel can make its file cache pages directly accessible to our inference processes; and secondly, that the file cache pages are much less likely to get evicted (which would force loads to hit disk) because they're no longer competing with memory pages that were needlessly created by gigabytes of standard i/o. The new file format supports single-file models like LLaMA 7b, and it also supports multi-file models like LLaMA 13B. Our Python tool now merges the foo.1, foo.2, etc. files back into a single file so that the C++ code which maps it doesn't need to reshape data every time. That's made llama.cpp so much simpler. Much of its load code has now been deleted. Furthermore, this change ensures that tensors are aligned properly on a 32-byte boundary. That opens the door to seeing if we can get additional performance gains on some microprocessors, by using ops that require memory alignment. Lastly note that both POSIX and the Windows platform are supported Fixes #91
173 lines
6.1 KiB
Python
173 lines
6.1 KiB
Python
# Convert a GPTQ quantized LLaMA model to a ggml compatible file
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# Based on: https://github.com/qwopqwop200/GPTQ-for-LLaMa
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#
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import os
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import re
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import sys
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import json
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import struct
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import numpy as np
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import torch
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from sentencepiece import SentencePieceProcessor
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if len(sys.argv) != 4:
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print("Usage: convert-gptq-to-ggml.py llamaXXb-4bit.pt tokenizer.model out.bin\n")
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sys.exit(1)
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fname_model = sys.argv[1]
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fname_tokenizer = sys.argv[2]
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dir_out = sys.argv[3]
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model = torch.load(fname_model, map_location="cpu")
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n_vocab, n_embd = model['model.embed_tokens.weight'].shape
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n_layer = 1 + max(int(m.group(1)) for name in model
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if (m := re.match(r'model\.layers\.([0-9]+)', name)))
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# hardcoded:
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n_mult = 256
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n_head = {32: 32, 40: 40, 60: 52, 80: 64}[n_layer]
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tokenizer = SentencePieceProcessor(fname_tokenizer)
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assert tokenizer.vocab_size() == n_vocab
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fname_out = sys.argv[3]
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fout = open(fname_out, "wb")
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fout.write(struct.pack("i", 0x67676d66)) # magic: ggmf in hex
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fout.write(struct.pack("i", 1)) # file version
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fout.write(struct.pack("i", n_vocab))
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fout.write(struct.pack("i", n_embd))
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fout.write(struct.pack("i", n_mult))
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fout.write(struct.pack("i", n_head))
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fout.write(struct.pack("i", n_layer))
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fout.write(struct.pack("i", n_embd // n_head)) # rot (obsolete)
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fout.write(struct.pack("i", 4))
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# This loop unchanged from convert-pth-to-ggml.py:
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for i in range(tokenizer.vocab_size()):
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if tokenizer.is_unknown(i):
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text = " \u2047 ".encode("utf-8")
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elif tokenizer.is_control(i):
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text = b""
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elif tokenizer.is_byte(i):
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piece = tokenizer.id_to_piece(i)
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if len(piece) != 6:
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print(f"Invalid token: {piece}")
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sys.exit(1)
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byte_value = int(piece[3:-1], 16)
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text = struct.pack("B", byte_value)
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else:
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text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8")
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fout.write(struct.pack("i", len(text)))
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fout.write(text)
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fout.write(struct.pack("f", tokenizer.get_score(i)))
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def write_header(shape, dst_name, ftype_cur):
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sname = dst_name.encode('utf-8')
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fout.write(struct.pack("iii", len(shape), len(sname), ftype_cur))
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fout.write(struct.pack("i" * len(shape), *shape[::-1]))
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fout.write(sname)
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# ensure tensor data is aligned
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tensor_data_offset = fout.tell()
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tensor_data_offset = (tensor_data_offset + 31) & -32
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fout.seek(tensor_data_offset)
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def convert_non_q4(src_name, dst_name):
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v = model[src_name]
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shape = v.shape
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print("Processing non-Q4 variable: " + src_name + " with shape: ", shape, " and type: ", v.dtype)
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if len(shape) == 1:
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print(" Converting to float32")
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v = v.to(torch.float32)
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ftype_cur = {torch.float16: 1, torch.float32: 0}[v.dtype]
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# header
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write_header(shape, dst_name, ftype_cur)
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# data
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v.numpy().tofile(fout)
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def convert_q4(src_name, dst_name, permute=False):
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zeros = model[f"{src_name}.zeros"].numpy()
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scales = model[f"{src_name}.scales"].numpy()
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bias = model[f"{src_name}.bias"].numpy()
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qweight = model[f"{src_name}.qweight"].numpy().T # transpose
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# Q4_1 does not support bias; good thing the bias is always all zeros.
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assert not np.any(bias)
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# Each int32 item is actually 8 int4 items packed together, and it's transposed.
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shape = (qweight.shape[0], qweight.shape[1] * 8)
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print("Processing Q4 variable: " + src_name + " with shape: ", shape)
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# The output format has the int4 weights in groups of 32 rather than 8.
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# It looks like this:
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# For each row:
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# For each group of 32 columns:
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# - addend (float32, 4 bytes)
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# - scale (float32, 4 bytes)
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# - weights (int4 * 32, 16 bytes)
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# Note that in the input, the scales and addends are shared between all
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# the columns in a row, so we end up wasting quite a bit of memory with
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# repeated scales and addends.
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addends = -zeros # flip sign
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# Since the output format is mixed between integers and floats, we have
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# to hackily view the floats as int32s just so numpy will let us
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# concatenate them.
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addends_view = addends.view(dtype=np.int32)
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scales_view = scales.view(dtype=np.int32)
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# Split into groups of 4 columns (i.e. 32 columns of quantized data):
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grouped = qweight.reshape([qweight.shape[0], qweight.shape[1] // 4, 4])
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# Repeat addends and scales:
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addends_rep = np.atleast_3d(addends_view).repeat(grouped.shape[1], axis=1)
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scales_rep = np.atleast_3d(scales_view).repeat(grouped.shape[1], axis=1)
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blob = np.concatenate([scales_rep, addends_rep, grouped], axis=2, casting='no')
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if permute:
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# Permute some rows to undo the permutation done by convert_llama_weights_to_hf.py.
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# This can be done after the above conversion because it doesn't affect column order/layout.
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blob = (blob.reshape(n_head, 2, shape[0] // n_head // 2, *blob.shape[1:])
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.swapaxes(1, 2)
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.reshape(blob.shape))
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# header
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write_header(shape, dst_name, 3) # ftype = Q4_1
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# data
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blob.tofile(fout)
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convert_non_q4("model.embed_tokens.weight", "tok_embeddings.weight")
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convert_non_q4("model.norm.weight", "norm.weight")
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convert_non_q4("lm_head.weight", "output.weight")
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for i in range(n_layer):
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convert_q4(f"model.layers.{i}.self_attn.q_proj", f"layers.{i}.attention.wq.weight", permute=True)
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convert_q4(f"model.layers.{i}.self_attn.k_proj", f"layers.{i}.attention.wk.weight", permute=True)
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convert_q4(f"model.layers.{i}.self_attn.v_proj", f"layers.{i}.attention.wv.weight")
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convert_q4(f"model.layers.{i}.self_attn.o_proj", f"layers.{i}.attention.wo.weight")
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convert_q4(f"model.layers.{i}.mlp.gate_proj", f"layers.{i}.feed_forward.w1.weight")
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convert_q4(f"model.layers.{i}.mlp.down_proj", f"layers.{i}.feed_forward.w2.weight")
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convert_q4(f"model.layers.{i}.mlp.up_proj", f"layers.{i}.feed_forward.w3.weight")
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convert_non_q4(f"model.layers.{i}.input_layernorm.weight", f"layers.{i}.attention_norm.weight")
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convert_non_q4(f"model.layers.{i}.post_attention_layernorm.weight", f"layers.{i}.ffn_norm.weight")
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fout.close()
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print("Done. Output file: " + fname_out)
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print("")
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