mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2025-01-06 02:48:57 +01:00
gguf : single pass for writing tensors + refactoring writer
This commit is contained in:
M. Yusuf Sarıgöz
parent
42f8fe1927
commit
f31e9230ad
@ -18,12 +18,16 @@ NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
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# reverse HF permute back to original pth layout
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# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py
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def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: Optional[int] = None) -> NDArray:
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if n_kv_head is not None and n_head != n_kv_head: n_head //= n_kv_head
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if n_kv_head is not None and n_head != n_kv_head:
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n_head //= n_kv_head
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return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
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.swapaxes(1, 2)
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.reshape(weights.shape))
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def count_model_parts(dir_model: str) -> int:
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num_parts = 0
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for filename in os.listdir(dir_model):
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@ -34,6 +38,7 @@ def count_model_parts(dir_model: str) -> int:
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print("gguf: found " + str(num_parts) + " model parts")
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return num_parts
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if len(sys.argv) < 3:
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print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
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print(" ftype == 0 -> float32")
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@ -74,12 +79,11 @@ if hparams["architectures"][0] != "LlamaForCausalLM":
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# get number of model parts
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num_parts = count_model_parts(dir_model)
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gguf_writer = gguf.GGUFWriter.open(fname_out)
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gguf_writer = gguf.GGUFWriter(fname_out, architecture="llama")
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print("gguf: get model metadata")
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llm_arch = "llama"
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block_count = hparams["num_hidden_layers"]
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head_count = hparams["num_attention_heads"]
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@ -91,7 +95,7 @@ else:
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if "_name_or_path" in hparams:
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hf_repo = hparams["_name_or_path"]
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else:
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hf_repo=""
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hf_repo = ""
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if "max_sequence_length" in hparams:
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ctx_length = hparams["max_sequence_length"]
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@ -102,19 +106,19 @@ else:
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sys.exit()
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gguf_writer.add_architecture(llm_arch)
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gguf_writer.add_architecture()
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gguf_writer.add_name(last_dir)
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gguf_writer.add_file_type("All tensors F32" if ftype == 0 else "Most tensors F16, some F32")
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gguf_writer.add_source_hf_repo(hf_repo)
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gguf_writer.add_tensor_data_layout(llm_arch, "Meta AI original pth")
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gguf_writer.add_context_length(llm_arch, ctx_length)
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gguf_writer.add_embedding_length(llm_arch, hparams["hidden_size"])
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gguf_writer.add_block_count(llm_arch, block_count)
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gguf_writer.add_feed_forward_length(llm_arch, hparams["intermediate_size"])
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gguf_writer.add_rope_dimension_count(llm_arch, hparams["hidden_size"] // hparams["num_attention_heads"])
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gguf_writer.add_head_count(llm_arch, head_count)
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gguf_writer.add_head_count_kv(llm_arch, head_count_kv)
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gguf_writer.add_layer_norm_rms_eps(llm_arch, hparams["rms_norm_eps"])
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gguf_writer.add_tensor_data_layout("Meta AI original pth")
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gguf_writer.add_context_length(ctx_length)
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gguf_writer.add_embedding_length(hparams["hidden_size"])
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gguf_writer.add_block_count(block_count)
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gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
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gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
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gguf_writer.add_head_count(head_count)
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gguf_writer.add_head_count_kv(head_count_kv)
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gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
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# TOKENIZATION
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@ -140,15 +144,19 @@ if Path(dir_model + "/tokenizer.model").is_file():
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score = tokenizer.get_score(i)
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toktype = 1 # defualt to normal token type
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if tokenizer.is_unknown(i): toktype = 2
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if tokenizer.is_control(i): toktype = 3
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if tokenizer.is_unknown(i):
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toktype = 2
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if tokenizer.is_control(i):
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toktype = 3
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# TODO: How to determinate if a token is user defined?
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# ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto
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# if tokenizer.is_user_defined(i): toktype = 4
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if tokenizer.is_unused(i): toktype = 5
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if tokenizer.is_byte(i): toktype = 6
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if tokenizer.is_unused(i):
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toktype = 5
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if tokenizer.is_byte(i):
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toktype = 6
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tokens.append(text)
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scores.append(score)
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@ -212,7 +220,7 @@ else:
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)
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for part_name in part_names:
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print("gguf: loading model part '"+ part_name + "'")
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print("gguf: loading model part '" + part_name + "'")
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model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
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for name in model_part.keys():
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@ -238,11 +246,12 @@ for part_name in part_names:
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elif name.endswith(".bias") and name[:-5] in tensor_map:
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name = tensor_map[name[:-5]] + ".bias"
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else:
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print( "Can not map tensor '" + name + "'" )
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print("Can not map tensor '" + name + "'")
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sys.exit()
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n_dims = len(data.shape)
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data_dtype = data.dtype
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old_dtype = data_dtype
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# if f32 desired, convert any float16 to float32
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if ftype == 0 and data.dtype == np.float16:
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@ -256,17 +265,19 @@ for part_name in part_names:
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if ftype == 1 and data.dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
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data_dtype = np.float16
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data_nbytes = data.size * 2 if data_dtype == np.float16 else data.size * 4
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data = data.astype(data_dtype)
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gguf_writer.add_tensor_info(name, data.shape, data_dtype, data_nbytes)
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print(name + ", n_dims = " + n_dims + ", " + str(old_dtype) + " --> " + str(data.dtype))
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gguf_writer.add_tensor(name, data)
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print("gguf: write header")
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gguf_writer.write_header_to_file()
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print("gguf: write metadata")
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gguf_writer.write_kv_data_to_file()
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print("gguf: write tensor metadata")
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gguf_writer.write_ti_data_to_file()
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print("gguf: write tensors")
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gguf_writer.write_tensors_to_file()
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# tensor data
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print("gguf: convert and write tensor data")
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@ -279,7 +290,7 @@ else:
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)
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for part_name in part_names:
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print("gguf: loading model part '"+ part_name + "'")
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print("gguf: loading model part '" + part_name + "'")
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model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
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for name in model_part.keys():
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@ -307,7 +318,7 @@ for part_name in part_names:
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elif name.endswith(".bias") and name[:-5] in tensor_map:
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name = tensor_map[name[:-5]] + ".bias"
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else:
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print( "Can not map tensor '" + name + "'" )
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print("Can not map tensor '" + name + "'")
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sys.exit()
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n_dims = len(data.shape)
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@ -325,8 +336,6 @@ for part_name in part_names:
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if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
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data = data.astype(np.float16)
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print(name + ", shape " + str(len(data.shape)) + ", " + str(old_dtype) + " --> " + str(data.dtype))
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gguf_writer.write_tensor_to_file(data)
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gguf_writer.close()
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138
gguf.py
138
gguf.py
@ -1,11 +1,7 @@
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"""TODOs
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1. Implement writers for known architectures, LLaMA in particular.
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2. Add docstrings from the format specs.
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3. After development is done, Convert it to a proper pip-installable Python package, and possibly move it to its own repo under ggml-org.
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"""
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import shutil
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import sys
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import struct
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import tempfile
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import numpy as np
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from enum import IntEnum
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@ -70,7 +66,8 @@ KEY_TOKENIZER_RWKV = "tokenizer.rwkv.world"
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# recommended mapping of model tensor names for storage in gguf
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#
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def get_tensor_name_map(n_blocks : int):
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def get_tensor_name_map(n_blocks: int):
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tensor_map = {}
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# Token embeddings
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mapped_to = "token_embd"
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@ -95,7 +92,7 @@ def get_tensor_name_map(n_blocks : int):
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tensor_map["lm_head"] = mapped_to # gpt2 mpt falcon llama-hf
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tensor_map["output"] = mapped_to # llama-pth
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# Attention and fee-forward layer blocks
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for i in range(0,n_blocks):
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for i in range(0, n_blocks):
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# Attention norm
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mapped_to = "blk."+str(i)+".attn_norm"
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tensor_map["gpt_neox.layers."+str(i)+".input_layernorm"] = mapped_to # gptneox
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@ -168,6 +165,7 @@ def get_tensor_name_map(n_blocks : int):
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# implementation
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#
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class GGMLQuantizationType(IntEnum):
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F32 = 0
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F16 = 1
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@ -203,8 +201,9 @@ class GGUFValueType(IntEnum):
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class GGUFWriter:
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def __init__(self, fout: IO):
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self.fout = fout
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def __init__(self, path: str, architecture: str):
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self.fout = open(path, "wb")
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self.arch = architecture
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self.offset_tensor = 0
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self.data_alignment = GGUF_DEFAULT_ALIGNMENT
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self.kv_data = b""
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@ -228,11 +227,6 @@ class GGUFWriter:
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self.fout.write(self.ti_data)
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self.flush()
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@classmethod
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def open(cls, path: str) -> "GGUFWriter":
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f = open(path, "wb")
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return cls(f)
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def add_key(self, key: str):
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self.add_val(key, GGUFValueType.STRING, add_vtype=False)
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@ -269,7 +263,8 @@ class GGUFWriter:
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self.add_val(val, GGUFValueType.BOOL)
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def add_string(self, key: str, val: str):
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if len(val) == 0: return
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if len(val) == 0:
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return
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self.add_key(key)
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self.add_val(val, GGUFValueType.STRING)
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@ -323,6 +318,8 @@ class GGUFWriter:
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return ((x + n - 1) // n) * n
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def add_tensor_info(self, name: str, tensor_shape: np.ndarray, tensor_dtype: np.dtype, tensor_nbytes: int):
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assert tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now"
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encoded_name = name.encode("utf8")
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self.ti_data += struct.pack("<I", len(encoded_name))
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self.ti_data += encoded_name
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@ -331,13 +328,25 @@ class GGUFWriter:
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for i in range(n_dims):
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self.ti_data += struct.pack("<I", tensor_shape[n_dims - 1 - i])
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assert tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now"
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dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
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self.ti_data += struct.pack("<I", dtype)
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self.ti_data += struct.pack("<Q", self.offset_tensor)
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self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment)
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self.ti_data_count += 1
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def add_tensor(self, name: str, tensor: np.ndarray):
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if not hasattr(self, "temp_file"):
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self.temp_file = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024)
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self.temp_file.seek(0)
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self.add_tensor_info(name, tensor.shape, tensor.dtype, tensor.nbytes)
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tensor.tofile(self.temp_file)
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pad = GGUFWriter.ggml_pad(tensor.nbytes, self.data_alignment) - tensor.nbytes
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if pad != 0:
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self.temp_file.write(bytes([0] * pad))
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def write_tensor_to_file(self, tensor: np.ndarray):
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pad = GGUFWriter.ggml_pad(self.fout.tell(), self.data_alignment) - self.fout.tell()
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if pad != 0:
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@ -349,21 +358,33 @@ class GGUFWriter:
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if pad != 0:
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self.fout.write(bytes([0] * pad))
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def write_tensors_to_file(self):
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self.write_ti_data_to_file()
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pad = GGUFWriter.ggml_pad(self.fout.tell(), self.data_alignment) - self.fout.tell()
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if pad != 0:
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self.fout.write(bytes([0] * pad))
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self.temp_file.seek(0)
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shutil.copyfileobj(self.temp_file, self.fout)
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self.flush()
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self.temp_file.close()
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def flush(self):
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self.fout.flush()
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def close(self):
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self.fout.close()
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def add_architecture(self, architecture: str):
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self.add_string(KEY_GENERAL_ARCHITECTURE,
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architecture)
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def add_architecture(self):
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self.add_string(KEY_GENERAL_ARCHITECTURE, self.arch)
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def add_author(self, author: str):
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self.add_string(KEY_GENERAL_AUTHOR, author)
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def add_tensor_data_layout(self, layout: str):
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self.add_string(KEY_LLM_TENSOR_DATA_LAYOUT , layout)
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self.add_string(KEY_LLM_TENSOR_DATA_LAYOUT.format(llm=self.arch), layout)
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def add_url(self, url: str):
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self.add_string(KEY_GENERAL_URL, url)
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@ -391,60 +412,60 @@ class GGUFWriter:
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self.data_alignment = alignment
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self.add_uint32(KEY_GENERAL_ALIGNMENT, alignment)
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def add_context_length(self, llm: str, length: int):
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def add_context_length(self, length: int):
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self.add_uint32(
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KEY_LLM_CONTEXT_LENGTH.format(llm=llm), length)
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KEY_LLM_CONTEXT_LENGTH.format(llm=self.arch), length)
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def add_embedding_length(self, llm: str, length: int):
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def add_embedding_length(self, length: int):
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self.add_uint32(
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KEY_LLM_EMBEDDING_LENGTH.format(llm=llm), length)
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KEY_LLM_EMBEDDING_LENGTH.format(llm=self.arch), length)
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def add_block_count(self, llm: str, length: int):
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def add_block_count(self, length: int):
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self.add_uint32(
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KEY_LLM_BLOCK_COUNT.format(llm=llm), length)
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KEY_LLM_BLOCK_COUNT.format(llm=self.arch), length)
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def add_feed_forward_length(self, llm: str, length: int):
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def add_feed_forward_length(self, length: int):
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self.add_uint32(
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KEY_LLM_FEED_FORWARD_LENGTH.format(llm=llm), length)
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KEY_LLM_FEED_FORWARD_LENGTH.format(llm=self.arch), length)
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def add_parallel_residual(self, llm: str, use: bool):
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def add_parallel_residual(self, use: bool):
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self.add_bool(
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KEY_LLM_USE_PARALLEL_RESIDUAL.format(llm=llm), use)
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KEY_LLM_USE_PARALLEL_RESIDUAL.format(llm=self.arch), use)
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def add_tensor_data_layout(self, llm: str, layout: str):
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def add_tensor_data_layout(self, layout: str):
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self.add_string(
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KEY_LLM_TENSOR_DATA_LAYOUT.format(llm=llm), layout)
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KEY_LLM_TENSOR_DATA_LAYOUT.format(llm=self.arch), layout)
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def add_head_count(self, llm: str, count: int):
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def add_head_count(self, count: int):
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self.add_uint32(
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KEY_ATTENTION_HEAD_COUNT.format(llm=llm), count)
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KEY_ATTENTION_HEAD_COUNT.format(llm=self.arch), count)
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def add_head_count_kv(self, llm: str, count: int):
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def add_head_count_kv(self, count: int):
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self.add_uint32(
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KEY_ATTENTION_HEAD_COUNT_KV.format(llm=llm), count)
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KEY_ATTENTION_HEAD_COUNT_KV.format(llm=self.arch), count)
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def add_max_alibi_bias(self, llm: str, bias: float):
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def add_max_alibi_bias(self, bias: float):
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self.add_float32(
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KEY_ATTENTION_MAX_ALIBI_BIAS.format(llm=llm), bias)
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KEY_ATTENTION_MAX_ALIBI_BIAS.format(llm=self.arch), bias)
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def add_clamp_kqv(self, llm: str, value: float):
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def add_clamp_kqv(self, value: float):
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self.add_float32(
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KEY_ATTENTION_CLAMP_KQV.format(llm=llm), value)
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KEY_ATTENTION_CLAMP_KQV.format(llm=self.arch), value)
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def add_layer_norm_eps(self, llm: str, value: float):
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def add_layer_norm_eps(self, value: float):
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self.add_float32(
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KEY_ATTENTION_LAYERNORM_EPS.format(llm=llm), value)
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KEY_ATTENTION_LAYERNORM_EPS.format(llm=self.arch), value)
|
||||
|
||||
def add_layer_norm_rms_eps(self, llm: str, value: float):
|
||||
def add_layer_norm_rms_eps(self, value: float):
|
||||
self.add_float32(
|
||||
KEY_ATTENTION_LAYERNORM_RMS_EPS.format(llm=llm), value)
|
||||
KEY_ATTENTION_LAYERNORM_RMS_EPS.format(llm=self.arch), value)
|
||||
|
||||
def add_rope_dimension_count(self, llm: str, count: int):
|
||||
def add_rope_dimension_count(self, count: int):
|
||||
self.add_uint32(
|
||||
KEY_ROPE_DIMENSION_COUNT.format(llm=llm), count)
|
||||
KEY_ROPE_DIMENSION_COUNT.format(llm=self.arch), count)
|
||||
|
||||
def add_rope_scale(self, llm: str, value: float):
|
||||
self.add_float32(KEY_ROPE_SCALE.format(llm=llm), value)
|
||||
def add_rope_scale(self, value: float):
|
||||
self.add_float32(KEY_ROPE_SCALE.format(llm=self.arch), value)
|
||||
|
||||
def add_tokenizer_model(self, model: str):
|
||||
self.add_string(KEY_TOKENIZER_MODEL, model)
|
||||
@ -476,24 +497,27 @@ class GGUFWriter:
|
||||
def add_pad_token_id(self, id: int):
|
||||
self.add_uint32(KEY_TOKENIZER_PAD_ID, id)
|
||||
|
||||
|
||||
# Example usage:
|
||||
if __name__ == "__main__":
|
||||
# Example usage with a file
|
||||
gguf_writer = GGUFWriter.open("example.gguf")
|
||||
gguf_writer = GGUFWriter("example.gguf", "llama")
|
||||
|
||||
gguf_writer.add_architecture("llama")
|
||||
gguf_writer.add_architecture()
|
||||
gguf_writer.add_block_count(12)
|
||||
gguf_writer.add_uint32("answer", 42) # Write a 32-bit integer
|
||||
gguf_writer.add_float32("answer_in_float", 42.0) # Write a 32-bit float
|
||||
gguf_writer.add_custom_alignment(64)
|
||||
tensor1 = np.ones((32,), dtype=np.float32) * 100.0
|
||||
tensor2 = np.ones((32,), dtype=np.float32) * 101.0
|
||||
gguf_writer.add_tensor_info("tensor0", tensor1)
|
||||
gguf_writer.add_tensor_info("tensor1", tensor2)
|
||||
tensor2 = np.ones((64,), dtype=np.float32) * 101.0
|
||||
tensor3 = np.ones((96,), dtype=np.float32) * 102.0
|
||||
|
||||
gguf_writer.add_tensor("tensor1", tensor1)
|
||||
gguf_writer.add_tensor("tensor2", tensor2)
|
||||
gguf_writer.add_tensor("tensor3", tensor3)
|
||||
|
||||
gguf_writer.write_header_to_file()
|
||||
gguf_writer.write_kv_data_to_file()
|
||||
gguf_writer.write_ti_data_to_file()
|
||||
gguf_writer.write_tensor_to_file(tensor1)
|
||||
gguf_writer.write_tensor_to_file(tensor2)
|
||||
gguf_writer.write_tensors_to_file()
|
||||
|
||||
gguf_writer.close()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user