Error while Serving for testing purpose

Need to convert llama weights to hugging face format. Give that a go
Hi, I tried with the 7B Llama weights and faced another error with the python script - NameError: name 'false' is not defined. Did you mean: 'False'?
I tested it both in google colab as well as my laptop, same error occured in the both the cases. I used the following command -
python convert_llama_weights_to_hf.py --input_dir 7B --model_size 7B --output_dir hf_output
Here -
python script was downloaded in the local folder
7B is folder containing all files related to 7B model of Llama
hf_output is the folder where i wanted the output files to be
Any solutions please. Thank you.
The complete error log is -
Traceback (most recent call last):
  File "/content/drive/MyDrive/llama/convert_llama_weights_to_hf.py", line 1, in <module>
    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ama","path":"src/transformers/models/llama","contentType":"directory"},{"name":"longformer","path":"src/transformers/models/longformer","contentType":"directory"},{"name":"longt5","path":"src/transformers/models/longt5","contentType":"directory"},{"name":"luke","path":"src/transformers/models/luke","contentType":"directory"},{"name":"lxmert","path":"src/transformers/models/lxmert","contentType":"directory"},{"name":"m2m_100","path":"src/transformers/models/m2m_100","contentType":"directory"},{"name":"marian","path":"src/transformers/models/marian","contentType":"directory"},{"name":"markuplm","path":"src/transformers/models/markuplm","contentType":"directory"},{"name":"mask2former","path":"src/transformers/models/mask2former","contentType":"directory"},{"name":"maskformer","path":"src/transformers/models/maskformer","contentType":"directory"},{"name":"mbart","path":"src/transformers/models/mbart","contentType":"directory"},{"name":"mbart50","path":"src/transformers/models/mbart50","contentType":"directory"},{"name":"mega","path":"src/transformers/models/mega","contentType":"directory"},{"name":"megatron_bert","path":"src/transformers/models/megatron_bert","contentType":"directory"},{"name":"megatron_gpt2","path":"src/transformers/models/megatron_gpt2","contentType":"directory"},{"name":"mgp_str","path":"src/transformers/models/mgp_str","contentType":"directory"},{"name":"mistral","path":"src/transformers/models/mistral","contentType":"directory"},{"name":"mluke","path":"src/transformers/models/mluke","contentType":"directory"},{"name":"mobilebert","path":"src/transformers/models/mobilebert","contentType":"directory"},{"name":"mobilenet_v1","path":"src/transformers/models/mobilenet_v1","contentType":"directory"},{"name":"mobilenet_v2","path":"src/transformers/models/mobilenet_v2","contentType":"directory"},{"name":"mobilevit","path":"src/transformers/models/mobilevit","contentType":"directory"},{"name":"mobilevitv2","path":"src/transformers/models/mobilevitv2","contentType":"directory"},{"name":"mpnet","path":"src/transformers/models/mpnet","contentType":"directory"},{"name":"mpt","path":"src/transformers/models/mpt","contentType":"directory"},{"name":"mra","path":"src/transformers/models/mra","contentType":"directory"},{"name":"mt5","path":"src/transformers/models/mt5","contentType":"directory"},{"name":"musicgen","path":"src/transformers/models/musicgen","contentType":"directory"},{"name":"mvp","path":"src/transformers/models/mvp","contentType":"directory"},{"name":"nat","path":"src/transformers/models/nat","contentType":"directory"},{"name":"nezha","path":"src/transformers/models/nezha","contentType":"directory"},{"name":"nllb","path":"src/transformers/models/nllb","contentType":"directory"},{"name":"nllb_moe","path":"src/transformers/models/nllb_moe","contentType":"directory"},{"name":"nougat","path":"src/transformers/models/nougat","contentType":"directory"},{"name":"nystromformer","path":"src/transformers/models/nystromformer","contentType":"directory"},{"name":"oneformer","path":"src/transformers/models/oneformer","contentType":"directory"},{"name":"openai","path":"src/transformers/models/openai","contentType":"directory"},{"name":"opt","path":"src/transformers/models/opt","contentType":"directory"},{"name":"owlv2","path":"src/transformers/models/owlv2","contentType":"directory"},{"name":"owlvit","path":"src/transformers/models/owlvit","contentType":"directory"},{"name":"pegasus","path":"src/transformers/models/pegasus","contentType":"directory"},{"name":"pegasus_x","path":"src/transformers/models/pegasus_x","contentType":"directory"},{"name":"perceiver","path":"src/transformers/models/perceiver","contentType":"directory"},{"name":"persimmon","path":"src/transformers/models/persimmon","contentType":"directory"},{"name":"phi","path":"src/transformers/models/phi","contentType":"directory"},{"name":"phobert","path":"src/transformers/models/phobert","contentType":"directory"},{"name":"pix2struct","path":"src/transformers/models/pix2struct","contentType":"directory"},{"name":"plbart","path":"src/transformers/models/plbart","contentType":"directory"},{"name":"poolformer","path":"src/transformers/models/poolformer","contentType":"directory"},{"name":"pop2piano","path":"src/transformers/models/pop2piano","contentType":"directory"},{"name":"prophetnet","path":"src/transformers/models/prophetnet","contentType":"directory"},{"name":"pvt","path":"src/transformers/models/pvt","contentType":"directory"},{"name":"qdqbert","path":"src/transformers/models/qdqbert","contentType":"directory"},{"name":"rag","path":"src/transformers/models/rag","contentType":"directory"},{"name":"realm","path":"src/transformers/models/realm","contentType":"directory"},{"name":"reformer","path":"src/transformers/models/reformer","contentType":"directory"},{"name":"regnet","path":"src/transformers/models/regnet","contentType":"directory"},{"name":"rembert","path":"src/transformers/models/rembert","contentType":"directory"},{"name":"resnet",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Copyright 2022 EleutherAI 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 LlamaConfig, LlamaForCausalLM, LlamaTokenizer","","","try:","    from transformers import 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\"","    )","    LlamaTokenizerFast = None","","\"\"\"","Sample usage:","","```","python src/transformers/models/llama/convert_llama_weights_to_hf.py \\","    --input_dir /path/to/downloaded/llama/weights --model_size 7B --output_dir /output/path","```","","Thereafter, models can be loaded via:","","```py","from transformers import LlamaForCausalLM, LlamaTokenizer","","model = LlamaForCausalLM.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,","    \"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, 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\", 10000.0)","    inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))","    if base > 10000.0:","        max_position_embeddings = 16384","    else:","        max_position_embeddings = 2048","","    tokenizer_class = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast","    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 = n_heads_per_shard // num_key_value_heads","        key_value_dim = dim // num_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","    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, 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\"","        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\"]","                ),","                f\"model.layers.{layer_i}.self_attn.k_proj.weight\": permute(","                    loaded[f\"layers.{layer_i}.attention.wk.weight\"]","                ),","                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(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","            )","            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\"","    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:","        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\"))","    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 = LlamaConfig(","        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,","    )","    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 Llama model.\")","    model = LlamaForCausalLM.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)","","","def write_tokenizer(tokenizer_path, input_tokenizer_path):","    # Initialize the tokenizer based on the `spm` model","    tokenizer_class = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast","    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 LLaMA weights, which contains tokenizer.model and model folders\",","    )","    parser.add_argument(","        \"--model_size\",","        choices=[\"7B\", \"7Bf\", \"13B\", \"13Bf\", \"30B\", \"34B\", \"65B\", \"70B\", \"70Bf\", \"tokenizer_only\"],","        help=\"'f' models correspond to the finetuned versions, and are specific to the Llama2 official release. 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microsoft / LLaVA-Med

Error while Serving for testing purpose #19
