From 48edda30ee545fdac2e7a33d505382888f748bbf Mon Sep 17 00:00:00 2001 From: cebtenzzre Date: Thu, 5 Oct 2023 15:00:34 -0400 Subject: [PATCH] convert : update Falcon script for new HF config (#3448) Also adds Falcon-180B support. Closes #3049 Co-authored-by: jb --- convert-falcon-hf-to-gguf.py | 117 ++++++++++++++++++++--------------- 1 file changed, 66 insertions(+), 51 deletions(-) diff --git a/convert-falcon-hf-to-gguf.py b/convert-falcon-hf-to-gguf.py index cb79586d6..9252e1c46 100755 --- a/convert-falcon-hf-to-gguf.py +++ b/convert-falcon-hf-to-gguf.py @@ -4,6 +4,7 @@ from __future__ import annotations import argparse +import contextlib import json import os import struct @@ -20,10 +21,10 @@ if 'NO_LOCAL_GGUF' not in os.environ: import gguf -def count_model_parts(dir_model: Path) -> int: +def count_model_parts(dir_model: Path, prefix: str) -> int: num_parts = 0 for filename in os.listdir(dir_model): - if filename.startswith("pytorch_model-"): + if filename.startswith(prefix): num_parts += 1 if num_parts > 0: @@ -77,20 +78,26 @@ print("gguf: loading model "+dir_model.name) with open(dir_model / "config.json", "r", encoding="utf-8") as f: hparams = json.load(f) -if hparams["architectures"][0] != "RWForCausalLM": +if hparams["architectures"][0] != "FalconForCausalLM": print("Model architecture not supported: " + hparams["architectures"][0]) sys.exit(1) # get number of model parts -num_parts = count_model_parts(dir_model) +num_parts = count_model_parts(dir_model, "model-00") +if num_parts: + is_safetensors = True + from safetensors import safe_open +else: + is_safetensors = False + num_parts = count_model_parts(dir_model, "pytorch_model-") ARCH=gguf.MODEL_ARCH.FALCON gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) print("gguf: get model metadata") -block_count = hparams["n_layer"] +block_count = hparams["num_hidden_layers"] gguf_writer.add_name("Falcon") gguf_writer.add_context_length(2048) # not in config.json @@ -98,9 +105,9 @@ gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform gguf_writer.add_embedding_length(hparams["hidden_size"]) gguf_writer.add_feed_forward_length(4 * hparams["hidden_size"]) gguf_writer.add_block_count(block_count) -gguf_writer.add_head_count(hparams["n_head"]) -if "n_head_kv" in hparams: - gguf_writer.add_head_count_kv(hparams["n_head_kv"]) +gguf_writer.add_head_count(hparams["num_attention_heads"]) +if "num_kv_heads" in hparams: + gguf_writer.add_head_count_kv(hparams["num_kv_heads"]) else: gguf_writer.add_head_count_kv(1) gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"]) @@ -146,8 +153,8 @@ special_vocab.add_to_gguf(gguf_writer) tensor_map = gguf.get_tensor_name_map(ARCH,block_count) # params for qkv transform -n_head = hparams["n_head"] -n_head_kv = hparams["n_head_kv"] if "n_head_kv" in hparams else 1 +n_head = hparams["num_attention_heads"] +n_head_kv = hparams["num_kv_heads"] if "num_kv_heads" in hparams else 1 head_dim = hparams["hidden_size"] // n_head @@ -156,6 +163,10 @@ print("gguf: get tensor metadata") if num_parts == 0: part_names = iter(("pytorch_model.bin",)) +elif is_safetensors: + part_names = ( + f"model-{n:05}-of-{num_parts:05}.safetensors" for n in range(1, num_parts + 1) + ) else: part_names = ( f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) @@ -165,60 +176,64 @@ for part_name in part_names: if args.vocab_only: break print("gguf: loading model part '" + part_name + "'") - model_part = torch.load(dir_model / part_name, map_location="cpu") + if is_safetensors: + ctx = safe_open(dir_model / part_name, framework="pt", device="cpu") + else: + ctx = contextlib.nullcontext(torch.load(dir_model / part_name, map_location="cpu")) - for name in model_part.keys(): - data = model_part[name] + with ctx as model_part: + for name in model_part.keys(): + data = model_part.get_tensor(name) if is_safetensors else model_part[name] - old_dtype = data.dtype + old_dtype = data.dtype - # convert any unsupported data types to float32 - if data.dtype != torch.float16 and data.dtype != torch.float32: - data = data.to(torch.float32) + # convert any unsupported data types to float32 + if data.dtype != torch.float16 and data.dtype != torch.float32: + data = data.to(torch.float32) - # QKV tensor transform - # The original query_key_value tensor contains n_head_kv "kv groups", - # each consisting of n_head/n_head_kv query weights followed by one key - # and one value weight (shared by all query heads in the kv group). - # This layout makes it a big pain to work with in GGML. - # So we rearrange them here,, so that we have n_head query weights - # followed by n_head_kv key weights followed by n_head_kv value weights, - # in contiguous fashion. - # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py + # QKV tensor transform + # The original query_key_value tensor contains n_head_kv "kv groups", + # each consisting of n_head/n_head_kv query weights followed by one key + # and one value weight (shared by all query heads in the kv group). + # This layout makes it a big pain to work with in GGML. + # So we rearrange them here,, so that we have n_head query weights + # followed by n_head_kv key weights followed by n_head_kv value weights, + # in contiguous fashion. + # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py - if "query_key_value" in name: - qkv = data.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head) - q = qkv[:, :-2 ].reshape(n_head * head_dim, head_dim * n_head) - k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head) - v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head) - data = torch.cat((q,k,v)).reshape_as(data) + if "query_key_value" in name: + qkv = data.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head) + q = qkv[:, :-2 ].reshape(n_head * head_dim, head_dim * n_head) + k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head) + v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head) + data = torch.cat((q,k,v)).reshape_as(data) - data = data.squeeze().numpy() + data = data.squeeze().numpy() - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) - if new_name is None: - print("Can not map tensor '" + name + "'") - sys.exit() + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) + if new_name is None: + print("Can not map tensor '" + name + "'") + sys.exit() - n_dims = len(data.shape) - data_dtype = data.dtype + n_dims = len(data.shape) + data_dtype = data.dtype - # if f32 desired, convert any float16 to float32 - if ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) + # if f32 desired, convert any float16 to float32 + if ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if 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 + if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) - print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) + print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - gguf_writer.add_tensor(new_name, data) + gguf_writer.add_tensor(new_name, data) print("gguf: write header")