Create convert_mistral_moe_weights_to_hf.py

convert_mistral_moe_weights_to_hf.py

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+# 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 transformers import (
+    LlamaTokenizer
+)
+
+from .modeling_moe_mistral import MixtralForCausalLM
+from .configuration_moe_mistral import MixtralConfig
+
+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, model_size, tokenizer_path=None, safe_serialization=True):
+    # for backward compatibility, before you needed the repo to be called `my_repo/model_size`
+    if not os.path.isfile(os.path.join(input_base_path, "params.json")):
+        input_base_path = os.path.join(input_base_path, 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 = NUM_SHARDS[model_size]
+
+    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", 100000.0)
+    inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
+    max_position_embeddings = 4096 * 8
+    num_experts_per_token = params["moe"]["num_experts_per_tok"]
+    num_experts = params["moe"]["num_experts"]
+    
+
+    if tokenizer_path is not None:
+        tokenizer = tokenizer_class(tokenizer_path)
+        tokenizer.save_pretrained(model_path)
+    vocab_size = tokenizer.vocab_size if tokenizer_path is not None else 32000
+
+    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
+    else:  # compatibility with other checkpoints
+        num_key_value_heads = n_heads
+        num_local_key_value_heads = n_heads_per_shard
+        key_value_dim = dim
+
+    # permute for sliced rotary
+    def permute(w, n_heads=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
+    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(dim, 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
+        )
+
+        for expert in range(num_experts):
+            state_dict[f"model.layers.{layer_i}.mlp.experts.{expert}.w1.weight"] = loaded[0][f"layers.{layer_i}.feed_forward.experts.{expert}.w1.weight"]
+            state_dict[f"model.layers.{layer_i}.mlp.experts.{expert}.w2.weight"] = loaded[0][f"layers.{layer_i}.feed_forward.experts.{expert}.w2.weight"]
+            state_dict[f"model.layers.{layer_i}.mlp.experts.{expert}.w3.weight"] = loaded[0][f"layers.{layer_i}.feed_forward.experts.{expert}.w3.weight"] 
+
+        state_dict[f"model.layers.{layer_i}.mlp.gate.weight"] = loaded[0][f"layers.{layer_i}.feed_forward.gate.weight"]
+
+        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()
+        print(param_count)
+    torch.save(state_dict, os.path.join(tmp_model_path, filename))
+
+    index_dict["metadata"] = {"total_size": param_count * 2}
+    write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))
+    config = MixtralConfig(
+        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"],
+        num_key_value_heads=num_key_value_heads,
+        vocab_size=vocab_size,
+        rope_theta=base,
+        max_position_embeddings=max_position_embeddings,
+        num_experts=num_experts,
+        num_experts_per_token=num_experts_per_token
+    )
+    config.save_pretrained(tmp_model_path)
+
+    del state_dict
+    del loaded
+    gc.collect()
+
+    print("Loading the checkpoint in a Mistral model.")
+    model = MixtralForCausalLM.from_pretrained(tmp_model_path, torch_dtype=torch.float16, 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)
+
+
+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(
+        "--model_size",
+        choices=["7B", "tokenizer_only"],
+        help="'f' models correspond to the finetuned versions, and are specific to the Mistral2 official release. For more details on Mistral2, checkout the original repo: https://huggingface.co/meta-mistral",
+    )
+    parser.add_argument(
+        "--output_dir",
+        help="Location to write HF model and tokenizer",
+    )
+    parser.add_argument("--safe_serialization", type=bool, help="Whether or not to save using `safetensors`.")
+    args = parser.parse_args()
+    spm_path = os.path.join(args.input_dir, "tokenizer.model")
+    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,
+            tokenizer_path=spm_path,
+        )
+    else:
+        write_tokenizer(args.output_dir, spm_path)
+
+
+if __name__ == "__main__":
+    main()