LLM: Add gguf falcon (#9801)

* init falcon

* update convert.py

* update style

python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF/README.md

@@ -7,6 +7,7 @@ In this directory, you will find examples on how to load GGUF model into `bigdl-
 - [Mixtral-8x7B-v0.1-GGUF](https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF)
 - [Baichuan2-7B-Chat-GGUF](https://huggingface.co/second-state/Baichuan2-7B-Chat-GGUF/tree/main)
 - [Bloomz-7b1-GGUF](https://huggingface.co/hzjane/bloomz-7b1-gguf)
+- [falcon-7b-quantized-gguf](https://huggingface.co/xaviviro/falcon-7b-quantized-gguf/tree/main)
 - [mpt-7b-chat-gguf](https://huggingface.co/maddes8cht/mosaicml-mpt-7b-chat-gguf/tree/main)
 
 ## Requirements

python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF/README.md

@@ -7,6 +7,7 @@ In this directory, you will find examples on how to load GGUF model into `bigdl-
 - [Mixtral-8x7B-v0.1-GGUF](https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF)
 - [Baichuan2-7B-Chat-GGUF](https://huggingface.co/second-state/Baichuan2-7B-Chat-GGUF/tree/main)
 - [Bloomz-7b1-GGUF](https://huggingface.co/hzjane/bloomz-7b1-gguf)
+- [falcon-7b-quantized-gguf](https://huggingface.co/xaviviro/falcon-7b-quantized-gguf/tree/main)
 - [mpt-7b-chat-gguf](https://huggingface.co/maddes8cht/mosaicml-mpt-7b-chat-gguf/tree/main)
 
 ## Requirements

python/llm/src/bigdl/llm/transformers/convert.py

@@ -523,6 +523,7 @@ def _optimize_post(model, lightweight_bmm=False):
                         bloom_attention_forward
                         )
     elif "falcon" in model.config.model_type or "RefinedWeb" in model.config.model_type:
+        if model.config.architectures is not None:
             modeling_module_name = model.__class__.__module__
             module = importlib.import_module(modeling_module_name)
             if "RWForCausalLM" in model.config.architectures:

python/llm/src/bigdl/llm/transformers/gguf/api.py

@@ -57,6 +57,9 @@ def load_gguf_model(fpath: str, dtype: torch.dtype = torch.float):
         elif model_family == "bloom":
             from .models.bloom import load_gguf_bloom
             model, tokenizer = load_gguf_bloom(loader, dtype)
+        elif model_family == "falcon":
+            from .models.falcon import load_gguf_falcon
+            model, tokenizer = load_gguf_falcon(loader, dtype)
         elif model_family == "mpt":
             from .models.mpt import load_gguf_mpt
             model, tokenizer = load_gguf_mpt(loader, dtype)

python/llm/src/bigdl/llm/transformers/gguf/models/falcon.py

@@ -0,0 +1,112 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# 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
+import torch
+from accelerate import init_empty_weights
+from accelerate.utils import set_module_tensor_to_device
+from transformers import FalconConfig, FalconForCausalLM, PreTrainedTokenizerFast
+
+from ..gguf import GGUFFileLoader
+
+
+def load_gguf_falcon(loader: GGUFFileLoader, dtype: torch.dtype = torch.float):
+    config = loader.config
+
+    falcon_config = FalconConfig(
+        vocab_size=len(config['tokenizer.ggml.tokens']),
+        hidden_size=config['falcon.embedding_length'],
+        num_hidden_layers=config['falcon.block_count'],
+        num_attention_heads=config['falcon.attention.head_count'],
+        num_kv_heads=config['falcon.attention.head_count_kv'],
+        max_position_embeddings=config['falcon.context_length'],
+        layer_norm_epsilon=config['falcon.attention.layer_norm_epsilon'],
+        use_cache=True,
+        bos_token_id=config['tokenizer.ggml.bos_token_id'],
+        eos_token_id=config['tokenizer.ggml.eos_token_id'],
+        # architectures="FalconForCausalLM",
+    )
+
+    ckpt = loader.tensors(dtype)
+    n_head = config['falcon.attention.head_count']
+    n_head_kv = config['falcon.attention.head_count_kv']
+    head_dim = config['falcon.embedding_length'] // n_head
+    ckpt = restore_falcon_weight(ckpt, n_head, n_head_kv, head_dim)
+
+    state_dict = {}
+    state_dict['transformer.word_embeddings.weight'] = ckpt['token_embd.weight']
+    state_dict['transformer.ln_f.weight'] = ckpt['output_norm.weight']
+    state_dict['transformer.ln_f.bias'] = ckpt['output_norm.bias']
+    state_dict['lm_head.weight'] = ckpt['output.weight']
+    for i in range(config['falcon.block_count']):
+        state_dict[f'transformer.h.{i}.self_attention.query_key_value.weight'] = \
+            ckpt[f'blk.{i}.attn_qkv.weight']
+        state_dict[f'transformer.h.{i}.self_attention.dense.weight'] = \
+            ckpt[f'blk.{i}.attn_output.weight']
+        state_dict[f'transformer.h.{i}.mlp.dense_h_to_4h.weight'] = \
+            ckpt[f'blk.{i}.ffn_up.weight']
+        state_dict[f'transformer.h.{i}.mlp.dense_4h_to_h.weight'] = \
+            ckpt[f'blk.{i}.ffn_down.weight']
+        state_dict[f'transformer.h.{i}.input_layernorm.weight'] = \
+            ckpt[f'blk.{i}.attn_norm.weight']
+        state_dict[f'transformer.h.{i}.input_layernorm.bias'] = \
+            ckpt[f'blk.{i}.attn_norm.bias']
+
+    with init_empty_weights():
+        model = FalconForCausalLM(falcon_config)
+
+    for name, weight in state_dict.items():
+        set_module_tensor_to_device(model, name, "cpu", weight, dtype=dtype)
+
+    model = model.cpu()
+
+    pieces, merges = loader.tokenizer_pieces()
+
+    current_directory = os.path.dirname(os.path.abspath(__file__))
+    token_file = current_directory + "/model_implement/falcon/tokenizer.json"
+    import json
+    with open(token_file, 'r') as file:
+        data = json.load(file)
+    vocab = {}
+    # load and replace vocab and merges
+    for i in range(len(pieces)):
+        token = pieces[i].piece
+        score = int(pieces[i].score)
+        vocab[token] = score
+    data['model']['merges'] = merges
+    data['model']['vocab'] = vocab
+
+    with open(token_file, 'w') as file:
+        json.dump(data, file, indent=4)
+    tokenizer = PreTrainedTokenizerFast(tokenizer_file=token_file)
+    return model, tokenizer
+
+
+def restore_falcon_weight(ckpt: dict, n_head: int, n_head_kv: int, head_dim: int):
+    # see https://github.com/ggerganov/llama.cpp/blob/
+    # master/convert-hf-to-gguf.py#L666
+    import numpy as np
+    for name, weight in ckpt.items():
+        if name.endswith("attn_qkv.weight"):
+            part1, part2, part3 = np.split(weight.reshape(-1, head_dim * n_head),
+                                           [n_head * head_dim, (n_head + n_head_kv) * head_dim],
+                                           axis=0)
+            part1 = part1.reshape((n_head_kv, n_head // n_head_kv, head_dim, head_dim * n_head))
+            part2 = part2.reshape((n_head_kv, 1, head_dim, head_dim * n_head))
+            part3 = part3.reshape((n_head_kv, 1, head_dim, head_dim * n_head))
+            data = torch.cat([part1, part2, part3], dim=1)
+            ckpt[name] = data.reshape(-1, head_dim * n_head)
+    return ckpt

python/llm/src/bigdl/llm/transformers/gguf/models/model_implement/falcon/tokenizer.json

@@ -0,0 +1,160 @@
+{
+    "version": "1.0",
+    "truncation": null,
+    "padding": null,
+    "added_tokens": [
+        {
+            "id": 0,
+            "content": ">>TITLE<<",
+            "single_word": false,
+            "lstrip": false,
+            "rstrip": false,
+            "normalized": false,
+            "special": true
+        },
+        {
+            "id": 1,
+            "content": ">>ABSTRACT<<",
+            "single_word": false,
+            "lstrip": false,
+            "rstrip": false,
+            "normalized": false,
+            "special": true
+        },
+        {
+            "id": 2,
+            "content": ">>INTRODUCTION<<",
+            "single_word": false,
+            "lstrip": false,
+            "rstrip": false,
+            "normalized": false,
+            "special": true
+        },
+        {
+            "id": 3,
+            "content": ">>SUMMARY<<",
+            "single_word": false,
+            "lstrip": false,
+            "rstrip": false,
+            "normalized": false,
+            "special": true
+        },
+        {
+            "id": 4,
+            "content": ">>COMMENT<<",
+            "single_word": false,
+            "lstrip": false,
+            "rstrip": false,
+            "normalized": false,
+            "special": true
+        },
+        {
+            "id": 5,
+            "content": ">>ANSWER<<",
+            "single_word": false,
+            "lstrip": false,
+            "rstrip": false,
+            "normalized": false,
+            "special": true
+        },
+        {
+            "id": 6,
+            "content": ">>QUESTION<<",
+            "single_word": false,
+            "lstrip": false,
+            "rstrip": false,
+            "normalized": false,
+            "special": true
+        },
+        {
+            "id": 7,
+            "content": ">>DOMAIN<<",
+            "single_word": false,
+            "lstrip": false,
+            "rstrip": false,
+            "normalized": false,
+            "special": true
+        },
+        {
+            "id": 8,
+            "content": ">>PREFIX<<",
+            "single_word": false,
+            "lstrip": false,
+            "rstrip": false,
+            "normalized": false,
+            "special": true
+        },
+        {
+            "id": 9,
+            "content": ">>SUFFIX<<",
+            "single_word": false,
+            "lstrip": false,
+            "rstrip": false,
+            "normalized": false,
+            "special": true
+        },
+        {
+            "id": 10,
+            "content": ">>MIDDLE<<",
+            "single_word": false,
+            "lstrip": false,
+            "rstrip": false,
+            "normalized": false,
+            "special": true
+        },
+        {
+            "id": 11,
+            "content": "<|endoftext|>",
+            "single_word": false,
+            "lstrip": false,
+            "rstrip": false,
+            "normalized": false,
+            "special": true
+        }
+    ],
+    "normalizer": null,
+    "pre_tokenizer": {
+        "type": "Sequence",
+        "pretokenizers": [
+            {
+                "type": "Punctuation",
+                "behavior": "Contiguous"
+            },
+            {
+                "type": "ByteLevel",
+                "add_prefix_space": false,
+                "trim_offsets": true,
+                "use_regex": true
+            },
+            {
+                "type": "Digits",
+                "individual_digits": false
+            },
+            {
+                "type": "Split",
+                "pattern": {
+                    "Regex": "[0-9][0-9][0-9]"
+                },
+                "behavior": "Isolated",
+                "invert": false
+            }
+        ]
+    },
+    "post_processor": null,
+    "decoder": {
+        "type": "ByteLevel",
+        "add_prefix_space": true,
+        "trim_offsets": true,
+        "use_regex": true
+    },
+    "model": {
+        "type": "BPE",
+        "dropout": null,
+        "unk_token": null,
+        "continuing_subword_prefix": null,
+        "end_of_word_suffix": null,
+        "fuse_unk": false,
+        "vocab": null,
+        "merges": null
+    }
+}