Rename cognitivess_model/convert_Cognitivess_weights_to_hf.py to cognitivess_model/convert_cognitivess_weights_to_hf.py

cognitivess_model/convert_Cognitivess_weights_to_hf.py

@@ -1,415 +0,0 @@
-# Copyright 2022 Cognitivess and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import argparse
-import gc
-import json
-import os
-import shutil
-import warnings
-
-import torch
-
-from transformers import CognitivessConfig, CognitivessForCausalLM, CognitivessTokenizer, PreTrainedTokenizerFast
-from transformers.convert_slow_tokenizer import TikTokenConverter
-
-
-try:
-    from transformers import CognitivessTokenizerFast
-except ImportError as e:
-    warnings.warn(e)
-    warnings.warn(
-        "The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"
-    )
-    CognitivessTokenizerFast = None
-
-"""
-Sample usage:
-
-```
-python src/transformers/models/Cognitivess/convert_Cognitivess_weights_to_hf.py \
-    --input_dir /path/to/downloaded/Cognitivess/weights --model_size 8B --output_dir /output/path
-```
-
-Thereafter, models can be loaded via:
-
-```py
-from transformers import CognitivessForCausalLM, CognitivessTokenizer
-
-model = CognitivessForCausalLM.from_pretrained("/output/path")
-tokenizer = CognitivessTokenizer.from_pretrained("/output/path")
-```
-
-Important note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions
-come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM).
-
-If you want you tokenizer to add a bos automatically you should update the tokenizer._tokenizers.post_processor:
-
-```py
-from tokenizers import processors
-bos = "<|begin_of_text|>"
-tokenizer._tokenizers.post_processor = processors.Sequence(
-    [
-        processors.ByteLevel(trim_offsets=False),
-        processors.TemplateProcessing(
-            single=f"{bos}:0 $A:0",
-            pair=f"{bos}:0 $A:0 {bos}:1 $B:1",
-            special_tokens=[
-                (bos, tokenizer.encode(bos)),
-            ],
-        ),
-    ]
-)
-```
-"""
-
-NUM_SHARDS = {
-    "7B": 1,
-    "8B": 1,
-    "8Bf": 1,
-    "7Bf": 1,
-    "13B": 2,
-    "13Bf": 2,
-    "34B": 4,
-    "30B": 4,
-    "65B": 8,
-    "70B": 8,
-    "70Bf": 8,
-}
-
-
-def compute_intermediate_size(n, ffn_dim_multiplier=1, multiple_of=256):
-    return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)
-
-
-def read_json(path):
-    with open(path, "r") as f:
-        return json.load(f)
-
-
-def write_json(text, path):
-    with open(path, "w") as f:
-        json.dump(text, f)
-
-
-def write_model(
-    model_path,
-    input_base_path,
-    model_size=None,
-    safe_serialization=True,
-    Cognitivess_version=1,
-    vocab_size=None,
-    num_shards=None,
-):
-    os.makedirs(model_path, exist_ok=True)
-    tmp_model_path = os.path.join(model_path, "tmp")
-    os.makedirs(tmp_model_path, exist_ok=True)
-
-    params = read_json(os.path.join(input_base_path, "params.json"))
-    num_shards = NUM_SHARDS[model_size] if num_shards is None else num_shards
-    params = params.get("model", params)
-    n_layers = params["n_layers"]
-    n_heads = params["n_heads"]
-    n_heads_per_shard = n_heads // num_shards
-    dim = params["dim"]
-    dims_per_head = dim // n_heads
-    base = params.get("rope_theta", 10000.0)
-    inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
-    if base > 10000.0 and Cognitivess_version != 3:
-        max_position_embeddings = 16384
-    else:
-        # Depending on the Cognitivess version, the default max_position_embeddings has different values.
-        if Cognitivess_version == 1:
-            max_position_embeddings = 2048
-        elif Cognitivess_version == 2:
-            max_position_embeddings = 4096
-        elif Cognitivess_version == 3:
-            max_position_embeddings = 8192
-
-    vocab_size = vocab_size if vocab_size is not None else 32000
-    if params.get("n_kv_heads", None) is not None:
-        num_key_value_heads = params["n_kv_heads"]  # for GQA / MQA
-        num_key_value_heads_per_shard = num_key_value_heads // num_shards
-        key_value_dim = dims_per_head * num_key_value_heads
-    else:  # compatibility with other checkpoints
-        num_key_value_heads = n_heads
-        num_key_value_heads_per_shard = n_heads_per_shard
-        key_value_dim = dims_per_head * num_key_value_heads
-    print(num_shards, num_key_value_heads, num_key_value_heads_per_shard, key_value_dim)
-
-    # permute for sliced rotary
-    def permute(w, n_heads, dim1=dim, dim2=dim):
-        return w.view(n_heads, dim1 // n_heads // 2, 2, dim2).transpose(1, 2).reshape(dim1, dim2)
-
-    print(f"Fetching all parameters from the checkpoint at {input_base_path}.")
-    # Load weights
-    if num_shards == 1:
-        # Not sharded
-        # (The sharded implementation would also work, but this is simpler.)
-        loaded = torch.load(os.path.join(input_base_path, "consolidated.00.pth"), map_location="cpu")
-    else:
-        # Sharded
-        loaded = [
-            torch.load(os.path.join(input_base_path, file), map_location="cpu")
-            for file in os.listdir(input_base_path)
-            if file.endswith(".pth")
-        ]
-    param_count = 0
-    index_dict = {"weight_map": {}}
-    for layer_i in range(n_layers):
-        filename = f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
-        if num_shards == 1:
-            # Unsharded
-            state_dict = {
-                f"model.layers.{layer_i}.self_attn.q_proj.weight": permute(
-                    loaded[f"layers.{layer_i}.attention.wq.weight"], n_heads=n_heads
-                ),
-                f"model.layers.{layer_i}.self_attn.k_proj.weight": permute(
-                    loaded[f"layers.{layer_i}.attention.wk.weight"],
-                    n_heads=num_key_value_heads,
-                    dim1=key_value_dim,
-                ),
-                f"model.layers.{layer_i}.self_attn.v_proj.weight": loaded[f"layers.{layer_i}.attention.wv.weight"],
-                f"model.layers.{layer_i}.self_attn.o_proj.weight": loaded[f"layers.{layer_i}.attention.wo.weight"],
-                f"model.layers.{layer_i}.mlp.gate_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w1.weight"],
-                f"model.layers.{layer_i}.mlp.down_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w2.weight"],
-                f"model.layers.{layer_i}.mlp.up_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w3.weight"],
-                f"model.layers.{layer_i}.input_layernorm.weight": loaded[f"layers.{layer_i}.attention_norm.weight"],
-                f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[f"layers.{layer_i}.ffn_norm.weight"],
-            }
-        else:
-            # Sharded
-            # Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
-            # the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
-            # redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.
-
-            state_dict = {
-                f"model.layers.{layer_i}.input_layernorm.weight": loaded[0][
-                    f"layers.{layer_i}.attention_norm.weight"
-                ].clone(),
-                f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[0][
-                    f"layers.{layer_i}.ffn_norm.weight"
-                ].clone(),
-            }
-            state_dict[f"model.layers.{layer_i}.self_attn.q_proj.weight"] = permute(
-                torch.cat(
-                    [
-                        loaded[i][f"layers.{layer_i}.attention.wq.weight"].view(n_heads_per_shard, dims_per_head, dim)
-                        for i in range(len(loaded))
-                    ],
-                    dim=0,
-                ).reshape(dim, dim),
-                n_heads=n_heads,
-            )
-            state_dict[f"model.layers.{layer_i}.self_attn.k_proj.weight"] = permute(
-                torch.cat(
-                    [
-                        loaded[i][f"layers.{layer_i}.attention.wk.weight"].view(
-                            num_key_value_heads_per_shard, dims_per_head, dim
-                        )
-                        for i in range(len(loaded))
-                    ],
-                    dim=0,
-                ).reshape(key_value_dim, dim),
-                num_key_value_heads,
-                key_value_dim,
-                dim,
-            )
-            state_dict[f"model.layers.{layer_i}.self_attn.v_proj.weight"] = torch.cat(
-                [
-                    loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(
-                        num_key_value_heads_per_shard, dims_per_head, dim
-                    )
-                    for i in range(len(loaded))
-                ],
-                dim=0,
-            ).reshape(key_value_dim, dim)
-
-            state_dict[f"model.layers.{layer_i}.self_attn.o_proj.weight"] = torch.cat(
-                [loaded[i][f"layers.{layer_i}.attention.wo.weight"] for i in range(len(loaded))], dim=1
-            )
-            state_dict[f"model.layers.{layer_i}.mlp.gate_proj.weight"] = torch.cat(
-                [loaded[i][f"layers.{layer_i}.feed_forward.w1.weight"] for i in range(len(loaded))], dim=0
-            )
-            state_dict[f"model.layers.{layer_i}.mlp.down_proj.weight"] = torch.cat(
-                [loaded[i][f"layers.{layer_i}.feed_forward.w2.weight"] for i in range(len(loaded))], dim=1
-            )
-            state_dict[f"model.layers.{layer_i}.mlp.up_proj.weight"] = torch.cat(
-                [loaded[i][f"layers.{layer_i}.feed_forward.w3.weight"] for i in range(len(loaded))], dim=0
-            )
-
-        state_dict[f"model.layers.{layer_i}.self_attn.rotary_emb.inv_freq"] = inv_freq
-        for k, v in state_dict.items():
-            index_dict["weight_map"][k] = filename
-            param_count += v.numel()
-        torch.save(state_dict, os.path.join(tmp_model_path, filename))
-
-    filename = f"pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"
-    if num_shards == 1:
-        # Unsharded
-        state_dict = {
-            "model.embed_tokens.weight": loaded["tok_embeddings.weight"],
-            "model.norm.weight": loaded["norm.weight"],
-            "lm_head.weight": loaded["output.weight"],
-        }
-    else:
-        concat_dim = 0 if Cognitivess_version == 3 else 1
-        state_dict = {
-            "model.norm.weight": loaded[0]["norm.weight"],
-            "model.embed_tokens.weight": torch.cat(
-                [loaded[i]["tok_embeddings.weight"] for i in range(len(loaded))], dim=concat_dim
-            ),
-            "lm_head.weight": torch.cat([loaded[i]["output.weight"] for i in range(len(loaded))], dim=0),
-        }
-
-    for k, v in state_dict.items():
-        index_dict["weight_map"][k] = filename
-        param_count += v.numel()
-    torch.save(state_dict, os.path.join(tmp_model_path, filename))
-
-    # Write configs
-    index_dict["metadata"] = {"total_size": param_count * 2}
-    write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))
-    ffn_dim_multiplier = params["ffn_dim_multiplier"] if "ffn_dim_multiplier" in params else 1
-    multiple_of = params["multiple_of"] if "multiple_of" in params else 256
-    config = CognitivessConfig(
-        hidden_size=dim,
-        intermediate_size=compute_intermediate_size(dim, ffn_dim_multiplier, multiple_of),
-        num_attention_heads=params["n_heads"],
-        num_hidden_layers=params["n_layers"],
-        rms_norm_eps=params["norm_eps"],
-        num_key_value_heads=num_key_value_heads,
-        vocab_size=vocab_size,
-        rope_theta=base,
-        max_position_embeddings=max_position_embeddings,
-        bos_token_id=128000 if Cognitivess_version == 3 else 1,
-        eos_token_id=128001 if Cognitivess_version == 3 else 2,
-    )
-    config.save_pretrained(tmp_model_path)
-
-    # Make space so we can load the model properly now.
-    del state_dict
-    del loaded
-    gc.collect()
-
-    print("Loading the checkpoint in a Cognitivess model.")
-    model = CognitivessForCausalLM.from_pretrained(tmp_model_path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True)
-    # Avoid saving this as part of the config.
-    del model.config._name_or_path
-    model.config.torch_dtype = torch.float16
-    print("Saving in the Transformers format.")
-    model.save_pretrained(model_path, safe_serialization=safe_serialization)
-    shutil.rmtree(tmp_model_path, ignore_errors=True)
-
-
-class Cognitivess3Converter(TikTokenConverter):
-    def __init__(self, vocab_file, num_reserved_special_tokens=256, **kwargs):
-        super().__init__(vocab_file, **kwargs)
-        tokenizer = self.converted()
-        chat_template = (
-            "{% set loop_messages = messages %}"
-            "{% for message in loop_messages %}"
-            "{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}"
-            "{% if loop.index0 == 0 %}"
-            "{% set content = bos_token + content %}"
-            "{% endif %}"
-            "{{ content }}"
-            "{% endfor %}"
-            "{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}"
-        )
-        num_reserved_special_tokens = 256
-        special_tokens = [
-            "<|begin_of_text|>",
-            "<|end_of_text|>",
-            "<|reserved_special_token_0|>",
-            "<|reserved_special_token_1|>",
-            "<|reserved_special_token_2|>",
-            "<|reserved_special_token_3|>",
-            "<|start_header_id|>",
-            "<|end_header_id|>",
-            "<|reserved_special_token_4|>",
-            "<|eot_id|>",  # end of turn
-        ] + [f"<|reserved_special_token_{i}|>" for i in range(5, num_reserved_special_tokens - 5)]
-        tokenizer.add_special_tokens(special_tokens)
-
-        self.tokenizer = PreTrainedTokenizerFast(
-            tokenizer_object=tokenizer,
-            bos_token="<|begin_of_text|>",
-            eos_token="<|end_of_text|>",
-            chat_template=chat_template,
-            model_input_names=["input_ids", "attention_mask"],
-        )
-
-
-def write_tokenizer(tokenizer_path, input_tokenizer_path, Cognitivess_version=2):
-    tokenizer_class = CognitivessTokenizer if CognitivessTokenizerFast is None else CognitivessTokenizerFast
-    if Cognitivess_version == 3:
-        tokenizer = Cognitivess3Converter(input_tokenizer_path).tokenizer
-    else:
-        tokenizer = tokenizer_class(input_tokenizer_path)
-    print(f"Saving a {tokenizer_class.__name__} to {tokenizer_path}.")
-    tokenizer.save_pretrained(tokenizer_path)
-    return tokenizer
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--input_dir",
-        help="Location of Cognitivess weights, which contains tokenizer.model and model folders",
-    )
-    parser.add_argument(
-        "--model_size",
-        default=None,
-        help="'f' Deprecated in favor of `num_shards`: models correspond to the finetuned versions, and are specific to the Cognitivess2 official release. For more details on Cognitivess2, checkout the original repo: https://huggingface.co/meta-Cognitivess",
-    )
-    parser.add_argument(
-        "--output_dir",
-        help="Location to write HF model and tokenizer",
-    )
-    parser.add_argument(
-        "--safe_serialization", default=True, type=bool, help="Whether or not to save using `safetensors`."
-    )
-    # Different Cognitivess versions used different default values for max_position_embeddings, hence the need to be able to specify which version is being used.
-    parser.add_argument(
-        "--Cognitivess_version",
-        choices=[1, 2, 3],
-        default=1,
-        type=int,
-        help="Version of the Cognitivess model to convert. Currently supports Cognitivess1 and Cognitivess2. Controls the context size",
-    )
-    parser.add_argument(
-        "--num_shards",
-        default=None,
-        type=int,
-        help="The number of individual shards used for the model. Does not have to be the same as the number of consolidated_xx.pth",
-    )
-    args = parser.parse_args()
-    if args.model_size is None and args.num_shards is None:
-        raise ValueError("You have to set at least `num_shards` if you are not giving the `model_size`")
-    spm_path = os.path.join(args.input_dir, "tokenizer.model")
-    vocab_size = len(write_tokenizer(args.output_dir, spm_path, Cognitivess_version=args.Cognitivess_version))
-    if args.model_size != "tokenizer_only":
-        write_model(
-            model_path=args.output_dir,
-            input_base_path=args.input_dir,
-            model_size=args.model_size,
-            safe_serialization=args.safe_serialization,
-            Cognitivess_version=args.Cognitivess_version,
-            vocab_size=vocab_size,
-            num_shards=args.num_shards,
-        )
-
-
-if __name__ == "__main__":
-    main()

cognitivess_model/convert_cognitivess_weights_to_hf.py

@@ -0,0 +1,23 @@
+import argparse
+import torch
+from transformers import CognitivessConfig, CognitivessForCausalLM
+
+def convert_cognitivess_checkpoint_to_hf(model_dir, save_dir):
+    config = CognitivessConfig.from_pretrained(model_dir)
+    model = CognitivessForCausalLM(config)
+    
+    # Load the model weights from the Cognitivess checkpoint
+    state_dict = torch.load(f"{model_dir}/pytorch_model.bin", map_location="cpu")
+    model.load_state_dict(state_dict)
+    
+    # Save the model in Hugging Face format
+    model.save_pretrained(save_dir)
+    config.save_pretrained(save_dir)
+    print(f"Model converted and saved to {save_dir}")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_dir", type=str, required=True, help="Path to the Cognitivess model directory")
+    parser.add_argument("--save_dir", type=str, required=True, help="Path to the directory to save the converted model")
+    args = parser.parse_args()
+    convert_cognitivess_checkpoint_to_hf(args.model_dir, args.save_dir)