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convert-llama-h5-to-gguf.py : no need to convert tensors twice

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klosax 2023-08-12 21:50:24 +02:00 committed by GitHub
parent 8f09157ec9
commit 4cef57c81a
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1 changed files with 33 additions and 32 deletions
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convert-llama-h5-to-gguf.py
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@ -32,7 +32,6 @@ if len(sys.argv) < 3:
# output in the same directory as the model # output in the same directory as the model
dir_model = sys.argv[1] dir_model = sys.argv[1]
fname_out = sys.argv[1] + "/ggml-model.bin"
last_dir = os.path.basename(os.path.normpath(dir_model)) last_dir = os.path.basename(os.path.normpath(dir_model))
@ -49,7 +48,8 @@ if len(sys.argv) > 2:
if ftype < 0 or ftype > 1: if ftype < 0 or ftype > 1:
print("Invalid ftype: " + str(ftype)) print("Invalid ftype: " + str(ftype))
sys.exit(1) sys.exit(1)
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
print("gguf: loading model "+last_dir) print("gguf: loading model "+last_dir)
@ -72,8 +72,7 @@ llm_arch = "llama"
head_count = hparams["num_attention_heads"] head_count = hparams["num_attention_heads"]
block_count = hparams["num_hidden_layers"] block_count = hparams["num_hidden_layers"]
gguf_writer.add_name("llama2-7b") gguf_writer.add_name(last_dir)
gguf_writer.add_description("gguf test model")
gguf_writer.add_architecture(llm_arch) gguf_writer.add_architecture(llm_arch)
gguf_writer.add_context_length(llm_arch, hparams["max_position_embeddings"]) gguf_writer.add_context_length(llm_arch, hparams["max_position_embeddings"])
gguf_writer.add_embedding_length(llm_arch, hparams["hidden_size"]) gguf_writer.add_embedding_length(llm_arch, hparams["hidden_size"])
@ -186,22 +185,30 @@ for name in list_vars.keys():
sys.exit() sys.exit()
n_dims = len(data.shape) n_dims = len(data.shape)
data_dtype = data.dtype
# ftype == 0 -> float32, ftype == 1 -> float16 # print( name + " dims " + str(n_dims) + " dtype " + str(data.dtype) )
ftype_cur = 0
if ftype != 0:
if name.endswith(".weight") and n_dims == 2:
data = data.astype(np.float16)
ftype_cur = 1
else:
data = data.astype(np.float32)
ftype_cur = 0
else:
if data.dtype != np.float32:
data = data.astype(np.float32)
ftype_cur = 0
gguf_writer.add_tensor_info(name, data) if data.dtype != np.float16 and data.dtype != np.float32:
# convert any unsupported data types to float32
data_dtype = np.float32
elif ftype == 1 and data.dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
# if f16 desired, convert any float32 2-dim weight tensors to float16
data_dtype = np.float16
nelements = 1
for i in range(n_dims):
nelements *= data.shape[n_dims - 1 - i]
data_nbytes = 0
if data_dtype == np.float16:
data_nbytes = nelements * 2
elif data_dtype == np.float32:
data_nbytes = nelements * 4
gguf_writer.add_tensor_info(name, data.shape, data_dtype, data_nbytes)
print("gguf: write header") print("gguf: write header")
@ -212,7 +219,7 @@ print("gguf: write tensor metadata")
gguf_writer.write_ti_data_to_file() gguf_writer.write_ti_data_to_file()
# tensor data # tensor data
print("gguf: write tensor data") print("gguf: convert and write tensor data")
for name in list_vars.keys(): for name in list_vars.keys():
data = list_vars[name].squeeze().numpy() data = list_vars[name].squeeze().numpy()
@ -226,20 +233,14 @@ for name in list_vars.keys():
data = permute(data, head_count) data = permute(data, head_count)
n_dims = len(data.shape) n_dims = len(data.shape)
data_dtype = data.dtype
# ftype == 0 -> float32, ftype == 1 -> float16 if data_dtype != np.float16 and data_dtype != np.float32:
ftype_cur = 0 # convert any unsupported data types to float32
if ftype != 0: data = data.astype(np.float32)
if name.endswith(".weight") and n_dims == 2: elif ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data = data.astype(np.float16) # if f16 desired, convert any float32 2-dim weight tensors to float16
ftype_cur = 1 data = data.astype(np.float16)
else:
data = data.astype(np.float32)
ftype_cur = 0
else:
if data.dtype != np.float32:
data = data.astype(np.float32)
ftype_cur = 0
gguf_writer.write_tensor_to_file(data) gguf_writer.write_tensor_to_file(data)