vbatts/llama.cpp
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Fix wrong type size for Q8_K

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Better handling of original style metadata
This commit is contained in:
KerfuffleV2 2023-08-21 04:19:17 -06:00
parent 996aaca1d4
commit f68aef5473
1 changed files with 32 additions and 22 deletions
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54
convert-llama-ggmlv3-to-gguf.py
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@ -22,12 +22,19 @@ GGML_QUANT_SIZES = {
gguf.GGMLQuantizationType.Q4_K : (256, 2 + 2 + QK_K // 2 + 12),
gguf.GGMLQuantizationType.Q5_K : (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12),
gguf.GGMLQuantizationType.Q6_K : (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16),
gguf.GGMLQuantizationType.Q8_K : (256, 2 + QK_K + QK_K // 8),
gguf.GGMLQuantizationType.Q8_K : (256, 4 + QK_K + QK_K // 8),
}
class Hyperparameters:
def __init__(self):
self.n_vocab = self.n_embd = self.n_mult = self.n_head = self.n_layer = self.n_rot = self.ftype = 0
self.n_ff = 0
def set_n_ff(self, model):
ff_tensor_idx = model.tensor_map.get(b'layers.0.feed_forward.w1.weight')
assert ff_tensor_idx is not None, 'Missing layer 0 FF tensor'
ff_tensor = model.tensors[ff_tensor_idx]
self.n_ff = ff_tensor.dims[1]
def load(self, data, offset):
(
@ -42,7 +49,7 @@ class Hyperparameters:
return 4 * 7
def __str__(self):
return f'<Hyperparameters: n_vocab={self.n_vocab}, n_embd={self.n_embd}, n_mult={self.n_mult}, n_head={self.n_head}, n_layer={self.n_layer}, n_rot={self.n_rot}, ftype={self.ftype}>'
return f'<Hyperparameters: n_vocab={self.n_vocab}, n_embd={self.n_embd}, n_mult={self.n_mult}, n_head={self.n_head}, n_layer={self.n_layer}, n_rot={self.n_rot}, n_ff={self.n_ff}, ftype={self.ftype}>'
class Vocab:
def __init__(self):
@ -122,6 +129,7 @@ class GGMLV3Model:
self.vocab = vocab
self.tensors = tensors
self.tensor_map = tensor_map
hp.set_n_ff(self)
return offset
class GGMLToGGUF:
@ -132,10 +140,6 @@ class GGMLToGGUF:
self.cfg = cfg
self.params_override = params_override
self.vocab_override = vocab_override
ff_tensor_idx = ggml_model.tensor_map.get(b'layers.0.feed_forward.w1.weight')
assert ff_tensor_idx is not None, 'Missing layer 0 FF tensor'
ff_tensor = ggml_model.tensors[ff_tensor_idx]
self.ff_length = ff_tensor.dims[1]
if params_override is not None:
n_kv_head = params_override.n_head_kv
else:
@ -196,7 +200,7 @@ class GGMLToGGUF:
gguf_writer.add_context_length(cfg.context_length)
gguf_writer.add_embedding_length(hp.n_embd)
gguf_writer.add_block_count(hp.n_layer)
gguf_writer.add_feed_forward_length(self.ff_length)
gguf_writer.add_feed_forward_length(hp.n_ff)
gguf_writer.add_rope_dimension_count(hp.n_embd // hp.n_head)
gguf_writer.add_head_count(hp.n_head)
gguf_writer.add_head_count_kv(self.n_kv_head)
@ -267,18 +271,24 @@ class GGMLToGGUF:
# print(f'+ {tensor.name} | {mapped_name} {tensor.dims} :: {tempdims}')
gguf_writer.add_tensor(mapped_name, data[tensor.start_offset:tensor.start_offset + tensor.len_bytes], raw_shape = tempdims, raw_dtype = tensor.dtype)
def handle_metadata(cfg):
def handle_metadata(cfg, hp):
import convert
assert cfg.model_metadata_dir.is_dir(), 'Metadata dir is not a directory'
hf_config_path = cfg.model_metadata_dir / "config.json"
orig_config_path = cfg.model_metadata_dir / "params.json"
# Passing None to these load functions is not kosher but it should
# currently work for HF and only fail for original mode if
# n_vocab or n_ff is missing in params.json
# We pass a fake model here. "original" mode will check the shapes of some
# tensors if information is missing in the .json file: other than that, the
# model data isn't used so this should be safe (at least for now).
fakemodel = {
'tok_embeddings.weight': convert.LazyTensor.__new__(convert.LazyTensor),
'layers.0.feed_forward.w1.weight': convert.LazyTensor.__new__(convert.LazyTensor),
}
fakemodel['tok_embeddings.weight'].shape = [hp.n_vocab]
fakemodel['layers.0.feed_forward.w1.weight'].shape = [hp.n_ff]
if hf_config_path.exists():
params = convert.Params.loadHFTransformerJson(None, hf_config_path)
params = convert.Params.loadHFTransformerJson(fakemodel, hf_config_path)
elif orig_config_path.exists():
params = convert.Params.loadOriginalParamsJson(None, orig_config_path)
params = convert.Params.loadOriginalParamsJson(fakemodel, orig_config_path)
else:
raise ValueError('Unable to load metadata')
vocab = convert.load_vocab(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir, cfg.vocabtype)
@ -303,20 +313,20 @@ def main():
cfg = handle_args()
print(f'* Using config: {cfg}')
print('\n=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===\n')
vocab_override = None
params_override = None
if cfg.model_metadata_dir is not None:
(params_override, vocab_override) = handle_metadata(cfg)
print('!! Note: When overriding params the --gqa, --eps and --context-length options are ignored.')
print(f'* Overriding params: {params_override}')
print(f'* Overriding vocab: {vocab_override}')
else:
print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n')
data = np.memmap(cfg.input, mode = 'r')
model = GGMLV3Model()
print('* Scanning GGML input file')
offset = model.load(data, 0)
print(f'* GGML model hyperparameters: {model.hyperparameters}')
vocab_override = None
params_override = None
if cfg.model_metadata_dir is not None:
(params_override, vocab_override) = handle_metadata(cfg, model.hyperparameters)
print('!! Note: When overriding params the --gqa, --eps and --context-length options are ignored.')
print(f'* Overriding params: {params_override}')
print(f'* Overriding vocab: {vocab_override}')
else:
print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n')
converter = GGMLToGGUF(model, data, cfg, params_override = params_override, vocab_override = vocab_override)
converter.save()
print(f'* Successful completion. Output saved to: {cfg.output}')