nemo gemma to hf conversion

scripts/checkpoint_converters/convert_gemma_nemo_to_hf.py

@@ -0,0 +1,342 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.  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 os
+from argparse import ArgumentParser
+from collections import OrderedDict
+
+import torch
+from omegaconf import open_dict
+from pytorch_lightning import Trainer
+from transformers import AutoModelForCausalLM, GemmaTokenizer, GemmaTokenizerFast, convert_slow_tokenizer
+
+from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
+from nemo.collections.nlp.modules.common.megatron.utils import get_ltor_masks_and_position_ids
+from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy
+from nemo.utils import logging
+
+"""
+Script to convert a gemma checkpoint in nemo (mcore path) into a HuggingFace checkpoint.
+This script can be used to 1) generate only the HF weights, or 2) generate an entire HF model folder.
+This script is adapted from convert_llama_nemo_to_hf.py
+
+1) Generate only HF weights from a nemo file:
+
+    python convert_gemma_nemo_to_hf.py \
+    --input_name_or_path /workspace/pretrained/HF_TO_NEMO/gemma-2b-it \
+    --output_path /workspace/pretrained/NEMO_TO_HF/gemma-2b-it/pytorch_model.bin
+
+2) Generate the full HF model folder
+
+    python convert_gemma_nemo_to_hf.py \
+    --input_name_or_path /workspace/pretrained/HF_TO_NEMO/gemma-2b-it \
+    --output_path /workspace/pretrained/NEMO_TO_HF/gemma-2b-it/pytorch_model.bin \
+    --hf_input_path /workspace/pretrained/HF_MODELS/gemma-2b-it \
+    --hf_output_path /workspace/pretrained/NEMO_TO_HF/gemma-2b-it \
+    --input_tokenizer /workspace/pretrained/HF_MODELS/gemma-2b-it \
+    --hf_output_tokenizer /workspace/pretrained/NEMO_TO_HF/gemma-2b-it \
+    --precision 32
+
+    Use the --cpu-only flag if the model cannot fit in the GPU (e.g. Llama2 70b). 
+    However this option makes the conversion script significantly slower.
+"""
+
+
+def get_args():
+    parser = ArgumentParser()
+    parser.add_argument(
+        "--input_name_or_path",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to .nemo file or extracted folder",
+    )
+    parser.add_argument("--output_path", type=str, default=None, required=True, help="Path to HF .bin file")
+    parser.add_argument(
+        "--hf_input_path",
+        type=str,
+        default=None,
+        help="A HF model path, " "e.g. a folder containing https://huggingface.co/meta-llama/Llama-2-7b-hf/tree/main",
+    )
+    parser.add_argument(
+        "--hf_output_path",
+        type=str,
+        default=None,
+        help="Output HF model path, " "with the same format as above but user's own weights",
+    )
+    parser.add_argument(
+        "--input_tokenizer",
+        type=str,
+        default=None,
+        help="Path to tokenizer used for the input nemo model. (need to extract the .nemo file first)",
+    )
+    parser.add_argument(
+        "--hf_output_tokenizer",
+        type=str,
+        default=None,
+        help="Path to save the tokenizer used for the output HF model.",
+    )
+    parser.add_argument(
+        "--precision",
+        type=str,
+        default=None,
+        help="Precision of output weights."
+        "Defaults to precision of the input nemo weights (model.cfg.trainer.precision)",
+    )
+    parser.add_argument(
+        "--cpu-only",
+        action="store_true",
+        help="Load model in cpu only. Useful if the model cannot fit in GPU memory, "
+        "but this option makes the conversion script significantly slower.",
+    )
+    args = parser.parse_args()
+    return args
+
+
+def verify_forward(model_path, tokenizer_path, model_string):
+    logging.info(f"=" * 100)
+    logging.info(f"Verifying forward pass for {model_string}")
+
+    input_texts = [
+        'query: how much protein should an adult eat',
+    ]
+    logging.info(f"Running verifications {input_texts} ...")
+
+    tokenizer = GemmaTokenizer.from_pretrained(tokenizer_path, local_files_only=True)
+    tokenizer.pad_token = tokenizer.eos_token
+    batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors="pt")
+    batch_dict_cuda = {k: v.cuda() for k, v in batch_dict.items()}
+
+    if model_string == "hf":
+        model = AutoModelForCausalLM.from_pretrained(model_path, local_files_only=True)
+        model = model.cuda().eval()
+        outputs = model(**batch_dict_cuda, output_hidden_states=True)
+        next_token = outputs.logits[0, -1].argmax()
+    elif model_string == 'nemo':
+        dummy_trainer = Trainer(devices=1, accelerator='auto', strategy=NLPDDPStrategy())
+        model_config = MegatronGPTModel.restore_from(model_path, trainer=dummy_trainer, return_config=True)
+        model_config.tensor_model_parallel_size = 1
+        model_config.pipeline_model_parallel_size = 1
+        model = MegatronGPTModel.restore_from(
+            model_path, trainer=dummy_trainer, override_config_path=model_config, map_location=None
+        )
+
+        ids = batch_dict_cuda['input_ids']
+        id_tensors = [torch.unsqueeze(torch.LongTensor(id_list), dim=0) for id_list in ids.cpu()]
+        masks_and_position_ids = [
+            get_ltor_masks_and_position_ids(id_tensor, tokenizer.eos_token, False, False, False)
+            for id_tensor in id_tensors
+        ]
+
+        for tokens, attn_mask_and_pos_ids in zip(id_tensors, masks_and_position_ids):
+            attn_mask, _, pos_ids = attn_mask_and_pos_ids
+
+            outputs = model(
+                tokens=tokens, text_position_ids=pos_ids.cuda(), attention_mask=attn_mask.cuda(), labels=None
+            )
+        next_token = outputs.squeeze()[-1].argmax()
+    else:
+        raise ValueError(f"Model string {model_string} not recognized.")
+
+    logging.info(f"{model_string} predicted next token is: '{tokenizer.convert_ids_to_tokens([next_token])}'.")
+    logging.info(f"=" * 100)
+
+
+def convert(input_nemo_file, output_hf_file, precision=None, cpu_only=False) -> None:
+    """
+    Convert NeMo weights to HF weights
+    """
+    dummy_trainer = Trainer(devices=1, accelerator='cpu', strategy=NLPDDPStrategy())
+    model_config = MegatronGPTModel.restore_from(input_nemo_file, trainer=dummy_trainer, return_config=True)
+    model_config.tensor_model_parallel_size = 1
+    model_config.pipeline_model_parallel_size = 1
+    if cpu_only:
+        map_location = torch.device('cpu')
+        model_config.use_cpu_initialization = True
+    else:
+        map_location = None
+
+    if cpu_only:
+        logging.info("******** Loading model on CPU. This will take a significant amount of time.")
+    model = MegatronGPTModel.restore_from(
+        input_nemo_file, trainer=dummy_trainer, override_config_path=model_config, map_location=map_location
+    )
+    if precision is None:
+        precision = model.cfg.precision
+    if precision in [32, "32"]:
+        dtype = torch.float32
+    elif precision in [16, "16", "16-mixed"]:
+        dtype = torch.float16
+    elif precision in ["bf16", "bf16-mixed"]:
+        dtype = torch.bfloat16
+    else:
+        logging.warning(f"Precision string {precision} is not recognized, falling back to fp32")
+        dtype = torch.float32  # fallback
+    logging.info(f"Using precision {dtype}")
+
+    param_to_weights = lambda param: param.to(dtype)
+    checkpoint = OrderedDict()
+
+    hidden_size = model.cfg.hidden_size
+    head_num = model.cfg.num_attention_heads
+    num_layers = model.cfg.num_layers
+    ffn_hidden_size = model.cfg.ffn_hidden_size
+    num_query_groups = model.cfg.get("num_query_groups", head_num)  # different num_query_groups for 70B
+
+    head_size = hidden_size // head_num
+    heads_per_group = head_num // num_query_groups
+    qkv_total_dim = head_num + 2 * num_query_groups
+
+    # Embedding
+    embed_weight = model.state_dict()[f'model.embedding.word_embeddings.weight']
+    embed_weights_base_name = f'model.embed_tokens.weight'
+    checkpoint[embed_weights_base_name] = param_to_weights(embed_weight)
+    for l in range(int(num_layers)):
+        print(f"converting layer {l}")
+
+        qkv_weights = model.state_dict()[f'model.decoder.layers.{l}.self_attention.linear_qkv.weight']
+        qkv_weights = qkv_weights.reshape([qkv_total_dim, head_size, hidden_size])
+
+        q_slice = torch.cat(
+            [
+                torch.arange((heads_per_group + 2) * i, (heads_per_group + 2) * i + heads_per_group)
+                for i in range(num_query_groups)
+            ]
+        )
+        k_slice = torch.arange(heads_per_group, qkv_total_dim, (heads_per_group + 2))
+        v_slice = torch.arange(heads_per_group + 1, qkv_total_dim, (heads_per_group + 2))
+        ## Example of slices
+        ## 7b: num_query_groups = head_num = 32,
+        ## q_slice = [0, 3, 6, 9 , ... 90, 93]
+        ## k_slice = [1, 4, 7, 10, ... 91, 94]
+        ## v_slice = [2, 5, 8, 11, ... 92, 95]
+        ## 70b (with GQA): num_query_groups = 8, head_num = 64
+        ## q_slice = [0, 1, .. 6, 7, 10, 11, .. 16, 17, 20, 21, .. 67, 70, ... 76, 77]
+        ## k_slice = [8, 18, 28, ... 68, 78]
+        ## v_slice = [9, 19, 29, ... 69, 79]
+
+        q_weights_base_name = f'model.layers.{l}.self_attn.q_proj.weight'
+        k_weights_base_name = f'model.layers.{l}.self_attn.k_proj.weight'
+        v_weights_base_name = f'model.layers.{l}.self_attn.v_proj.weight'
+
+        checkpoint[q_weights_base_name] = param_to_weights(qkv_weights[q_slice].reshape(-1, hidden_size))
+        checkpoint[k_weights_base_name] = param_to_weights(qkv_weights[k_slice].reshape(-1, hidden_size))
+        checkpoint[v_weights_base_name] = param_to_weights(qkv_weights[v_slice].reshape(-1, hidden_size))
+
+        # attention dense
+        o_weight = model.state_dict()[f'model.decoder.layers.{l}.self_attention.linear_proj.weight']
+        o_weight_base_name = f'model.layers.{l}.self_attn.o_proj.weight'
+        checkpoint[o_weight_base_name] = param_to_weights(o_weight)
+
+        # mlp
+        mlp_weights = model.state_dict()[f'model.decoder.layers.{l}.mlp.linear_fc1.weight']
+        mlp_down_proj_weight = mlp_weights[:ffn_hidden_size, :]
+        mlp_gate_proj_weight = mlp_weights[ffn_hidden_size:, :]
+
+        mlp_down_proj_base_name = f'model.layers.{l}.mlp.gate_proj.weight'
+        mlp_gate_proj_base_name = f'model.layers.{l}.mlp.up_proj.weight'
+
+        checkpoint[mlp_down_proj_base_name] = param_to_weights(mlp_down_proj_weight)
+        checkpoint[mlp_gate_proj_base_name] = param_to_weights(mlp_gate_proj_weight)
+
+        mlp_up_proj_weight = model.state_dict()[f'model.decoder.layers.{l}.mlp.linear_fc2.weight']
+        mlp_up_proj_base_name = f'model.layers.{l}.mlp.down_proj.weight'
+        checkpoint[mlp_up_proj_base_name] = param_to_weights(mlp_up_proj_weight)
+
+        # layernorm
+        input_ln_weight = model.state_dict()[f'model.decoder.layers.{l}.self_attention.linear_qkv.layer_norm_weight']
+        input_ln_base_name = f'model.layers.{l}.input_layernorm.weight'
+        checkpoint[input_ln_base_name] = param_to_weights(input_ln_weight - 1.0)
+
+        post_attn_ln_weight = model.state_dict()[f'model.decoder.layers.{l}.mlp.linear_fc1.layer_norm_weight']
+        post_attn_ln_base_name = f'model.layers.{l}.post_attention_layernorm.weight'
+        checkpoint[post_attn_ln_base_name] = param_to_weights(post_attn_ln_weight - 1.0)
+
+        print(f"done layer {l}")
+
+    final_ln_weight = model.state_dict()[f'model.decoder.final_layernorm.weight']
+    final_ln_base_name = f'model.norm.weight'
+    checkpoint[final_ln_base_name] = param_to_weights(final_ln_weight - 1.0)
+
+    # NOTE: Gemmas uses weight tying
+    output_layer_weight = model.state_dict()[
+        f'model.embedding.word_embeddings.weight'
+    ]  # model.state_dict()[f'model.output_layer.weight']
+    output_layer_base_name = f'lm_head.weight'
+    checkpoint[output_layer_base_name] = param_to_weights(output_layer_weight)
+
+    os.makedirs(os.path.dirname(output_hf_file), exist_ok=True)
+    torch.save(checkpoint, output_hf_file)
+    logging.info(f"Weights saved to {output_hf_file}")
+
+    return dtype
+
+
+def replace_hf_weights_and_tokenizer(
+    weights_file,
+    dtype,
+    input_hf_path,
+    output_hf_path,
+    tokenizer_path,
+    output_hf_tokenizer,
+):
+    model = AutoModelForCausalLM.from_pretrained(
+        input_hf_path,
+        local_files_only=True,
+        torch_dtype=dtype,
+    )
+    nemo_exported = torch.load(weights_file)
+
+    if tokenizer_path:
+        tokenizer = GemmaTokenizer.from_pretrained(
+            tokenizer_path,
+            local_files_only=True,
+            legacy=False,
+        )
+        tmp_tokenizer = convert_slow_tokenizer.convert_slow_tokenizer(tokenizer)
+        fast_tokenizer = GemmaTokenizerFast(tokenizer_object=tmp_tokenizer)
+        tokenizer_length = len(fast_tokenizer)
+        model.resize_token_embeddings(tokenizer_length)
+
+    model.load_state_dict(nemo_exported)
+    model.save_pretrained(output_hf_path)
+    logging.info(f"Full HF model saved to {output_hf_path}")
+
+    if tokenizer_path:
+        fast_tokenizer.save_pretrained(output_hf_tokenizer)
+        tokenizer.save_pretrained(output_hf_tokenizer)
+        logging.info(f"Tokenizer saved to {output_hf_tokenizer}")
+
+
+if __name__ == '__main__':
+    args = get_args()
+    if not args.hf_output_tokenizer and args.hf_output_path:
+        args.hf_output_tokenizer = args.hf_output_path
+    # dtype = convert(args.input_name_or_path, args.output_path, precision=args.precision, cpu_only=args.cpu_only)
+    if args.hf_input_path and args.hf_output_path:
+        """
+        replace_hf_weights_and_tokenizer(
+            args.output_path,
+            dtype,
+            args.hf_input_path,
+            args.hf_output_path,
+            args.input_tokenizer,
+            args.hf_output_tokenizer,
+        )
+        """
+        verify_forward(args.input_name_or_path, args.hf_output_tokenizer, "nemo")
+        verify_forward(args.hf_output_path, args.hf_output_tokenizer, "hf")
+    else:
+        logging.info("`hf_input_path` and/or `hf_output_path` not provided, not generating full HF model.")
+        logging.info(f".bin file is saved to {args.output_path}")