Delete Mistral-NeMo-12B-Instruct-HF/convert_mistral_weights_to_hf.py

Mistral-NeMo-12B-Instruct-HF/convert_mistral_weights_to_hf.py

@@ -1,260 +0,0 @@
-# Copyright 2023 Mistral AI 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 safetensors.torch import load_file as safe_load_file
-
-from transformers import (
-    LlamaTokenizer,
-    MistralConfig,
-    MistralForCausalLM,
-)
-
-
-try:
-    from transformers import LlamaTokenizerFast
-
-    tokenizer_class = LlamaTokenizerFast
-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"
-    )
-    tokenizer_class = LlamaTokenizer
-
-"""
-Sample usage:
-
-```
-python src/transformers/models/mistral/convert_mistral_weights_to_hf.py \
-    --input_dir /path/to/downloaded/mistral/weights --model_size 7B --output_dir /output/path
-```
-
-Thereafter, models can be loaded via:
-
-```py
-from transformers import MistralForCausalLM, LlamaTokenizer
-
-model = MistralForCausalLM.from_pretrained("/output/path")
-tokenizer = LlamaTokenizer.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).
-"""
-
-NUM_SHARDS = {"7B": 1}
-
-
-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, safe_serialization=True, is_v3=False):
-    # for backward compatibility, before you needed the repo to be called `my_repo/model_size`
-    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 = 1
-
-    n_layers = params["n_layers"]
-    n_heads = params["n_heads"]
-    n_heads_per_shard = n_heads // num_shards
-    dim = params["dim"]
-    dims_per_head = params["head_dim"]
-    base = params.get("rope_theta", 10000.0)
-    inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
-    max_position_embeddings = 128000 * 8
-
-    vocab_size = params["vocab_size"]
-
-    if "n_kv_heads" in params:
-        num_key_value_heads = params["n_kv_heads"]  # for GQA / MQA
-        num_local_key_value_heads = num_key_value_heads // num_shards
-        key_value_dim = dims_per_head * num_local_key_value_heads
-
-    # permute for sliced rotary
-    def permute(w, n_heads=n_heads, dim1=dims_per_head * n_heads, 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 - for v3 models the consolidated weights are in a single file format in safetensors
-    if is_v3:
-        loaded = [safe_load_file(os.path.join(input_base_path, "consolidated.safetensors"))]
-    else:
-        loaded = [
-            torch.load(os.path.join(input_base_path, f"consolidated.{i:02d}.pth"), map_location="cpu")
-            for i in range(num_shards)
-        ]
-    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"
-
-        # 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(num_shards)
-                ],
-                dim=0,
-            ).reshape(n_heads_per_shard * dims_per_head, dim)
-        )
-        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_local_key_value_heads, dims_per_head, dim
-                    )
-                    for i in range(num_shards)
-                ],
-                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_local_key_value_heads, dims_per_head, dim)
-                for i in range(num_shards)
-            ],
-            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(num_shards)], 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(num_shards)], 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(num_shards)], 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(num_shards)], 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"
-    state_dict = {
-        "model.norm.weight": loaded[0]["norm.weight"],
-        "model.embed_tokens.weight": torch.cat([loaded[i]["tok_embeddings.weight"] for i in range(num_shards)], dim=1),
-        "lm_head.weight": torch.cat([loaded[i]["output.weight"] for i in range(num_shards)], 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"))
-    config = MistralConfig(
-        hidden_size=dim,
-        intermediate_size=params["hidden_dim"],
-        num_attention_heads=params["n_heads"],
-        num_hidden_layers=params["n_layers"],
-        rms_norm_eps=params["norm_eps"],
-        kv_channels=params["head_dim"],
-        num_key_value_heads=num_key_value_heads,
-        vocab_size=vocab_size,
-        rope_theta=base,
-        max_position_embeddings=max_position_embeddings,
-        sliding_window=None,
-    )
-    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 Mistral model.")
-    model = MistralForCausalLM.from_pretrained(tmp_model_path, torch_dtype=torch.bfloat16)
-    # 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)
-
-
-def write_tokenizer(tokenizer_path, input_tokenizer_path):
-    # Initialize the tokenizer based on the `spm` model
-    print(f"Saving a {tokenizer_class.__name__} to {tokenizer_path}.")
-    tokenizer = tokenizer_class(input_tokenizer_path)
-    tokenizer.save_pretrained(tokenizer_path)
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--input_dir",
-        help="Location of Mistral weights, which contains tokenizer.model and model folders",
-    )
-    parser.add_argument(
-        "--output_dir",
-        help="Location to write HF model and tokenizer",
-    )
-    args = parser.parse_args()
-    write_model(
-        model_path=args.output_dir,
-        input_base_path=args.input_dir,
-        safe_serialization=True,
-        is_v3=True,
-    )
-
-
-if __name__ == "__main__":
-    main()