LLM: add GGUF-IQ2 examples

python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF-IQ2/README.md

@@ -0,0 +1,81 @@
+# GGUF-IQ2
+
+This example shows how to run INT2 models using the IQ2 mechanism (first implemented by llama.cpp) in BigDL-LLM on Intel GPU.
+
+## Verified Models
+
+- [Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf), using [llama-v2-7b.imatrix](https://huggingface.co/datasets/ikawrakow/imatrix-from-wiki-train/resolve/main/llama-v2-7b.imatrix)
+- [Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf), using [llama-v2-7b.imatrix](https://huggingface.co/datasets/ikawrakow/imatrix-from-wiki-train/resolve/main/llama-v2-7b.imatrix)
+- [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2), using [mistral-7b-instruct-v0.2.imatrix](https://huggingface.co/datasets/ikawrakow/imatrix-from-wiki-train/resolve/main/mistral-7b-instruct-v0.2.imatrix)
+- [Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1), using [mixtral-8x7b.imatrix](https://huggingface.co/datasets/ikawrakow/imatrix-from-wiki-train/resolve/main/mixtral-8x7b.imatrix)
+- [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1), using [mixtral-8x7b-instruct-v0.1.imatrix](https://huggingface.co/datasets/ikawrakow/imatrix-from-wiki-train/resolve/main/mixtral-8x7b-instruct-v0.1.imatrix)
+
+## Requirements
+
+To run these examples with BigDL-LLM, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information.
+
+## Example: Predict Tokens using `generate()` API
+
+In the example [generate.py](./generate.py), we show a basic use case for a GGUF-IQ2 model to predict the next N tokens using `generate()` API, with BigDL-LLM optimizations.
+
+### 1. Install
+
+We suggest using conda to manage environment:
+
+```bash
+conda create -n llm python=3.9
+conda activate llm
+# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
+pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
+pip install transformers==4.35.0
+```
+**Note: For Mixtral model, please use transformers 4.36.0:**
+```bash
+pip install transformers==4.36.0
+```
+
+### 2. Configures OneAPI environment variables
+
+```bash
+source /opt/intel/oneapi/setvars.sh
+```
+
+### 3. Run
+
+For optimal performance on Arc, it is recommended to set several environment variables.
+
+```bash
+export USE_XETLA=OFF
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+```
+
+```
+python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
+```
+
+Arguments info:
+
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the model (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'meta-llama/Llama-2-7b-chat-hf'`.
+- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`.
+- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
+
+#### 2.3 Sample Output
+
+#### [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
+
+```log
+Inference time: xxxx s
+-------------------- Prompt --------------------
+### HUMAN:
+What is AI?
+
+### RESPONSE:
+
+-------------------- Output --------------------
+### HUMAN:
+What is AI?
+
+### RESPONSE:
+
+Artificial intelligence (AI) refers to the ability of machines to perform tasks that would typically require human intelligence, such as learning, problem-solving
+```

python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF-IQ2/generate.py

@@ -0,0 +1,83 @@
+#
+# 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 torch
+import time
+import argparse
+from bigdl.llm.transformers import AutoModelForCausalLM
+from transformers import AutoTokenizer
+import warnings
+
+# you could tune the prompt based on your own model,
+# here the prompt tuning refers to https://huggingface.co/georgesung/llama2_7b_chat_uncensored#prompt-style
+PROMPT_FORMAT = """### HUMAN:
+{prompt}
+
+### RESPONSE:
+"""
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for LLM model')
+    parser.add_argument('--repo-id-or-model-path', type=str, default="meta-llama/Llama-2-7b-chat-hf",
+                        help='The huggingface repo id'
+                             ', or the path to the huggingface checkpoint folder')
+    parser.add_argument('--prompt', type=str, default="What is AI?",
+                        help='Prompt to infer')
+    parser.add_argument('--n-predict', type=int, default=32,
+                        help='Max tokens to predict')
+
+    args = parser.parse_args()
+    model_path = args.repo_id_or_model_path
+
+    warnings.warn("iq2 quantization may need several minutes, please wait a moment, "
+                  "or have a cup of coffee now : )")
+
+    # Load model in 2 bit,
+    # which convert the relevant layers in the model into gguf_iq2_xxs format.
+    # GGUF-IQ2 quantization needs imatrix file to assist in quantization
+    # and improve generation quality, and different model may need different
+    # imtraix file, you can find and download imatrix file from 
+    # https://huggingface.co/datasets/ikawrakow/imatrix-from-wiki-train/tree/main.
+    model = AutoModelForCausalLM.from_pretrained(model_path,
+                                                 load_in_low_bit='gguf_iq2_xxs',
+                                                 trust_remote_code=True,
+                                                 imatrix='llama-v2-7b.imatrix').to("xpu")
+
+    # Load tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+    # Generate predicted tokens
+    with torch.inference_mode():
+        prompt = PROMPT_FORMAT.format(prompt=args.prompt)
+        input_ids = tokenizer.encode(prompt, return_tensors="pt").to("xpu")
+        # ipex model needs a warmup, then inference time can be accurate
+        output = model.generate(input_ids,
+                                max_new_tokens=args.n_predict)
+        st = time.time()
+        # if your selected model is capable of utilizing previous key/value attentions
+        # to enhance decoding speed, but has `"use_cache": false` in its model config,
+        # it is important to set `use_cache=True` explicitly in the `generate` function
+        # to obtain optimal performance with BigDL-LLM Low Bit optimizations
+        output = model.generate(input_ids,
+                                max_new_tokens=args.n_predict,
+                                repetition_penalty=1.1)
+        end = time.time()
+        output_str = tokenizer.decode(output[0], skip_special_tokens=True)
+        print(f'Inference time: {end-st} s')
+        print('-'*20, 'Prompt', '-'*20)
+        print(prompt)
+        print('-'*20, 'Output', '-'*20)
+        print(output_str)

python/llm/src/bigdl/llm/ggml/quantize.py

@@ -40,8 +40,8 @@
                      "fp8_e5m2": 19,      # fp8 in e5m2 format
                      "fp8": 19,           # fp8 in e5m2 format
                      "bf16": 20,
-                     "iq2_xxs": 21,
-                     "iq2_xs": 22,
+                     "gguf_iq2_xxs": 21,
+                     "gguf_iq2_xs": 22,
                      "q2_k": 23}
 
 _llama_quantize_type = {"q4_0": 2,

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

@@ -70,8 +70,8 @@
 MOFQ4 = ggml_tensor_qtype["mixed_fp4"]
 MOFQ8 = ggml_tensor_qtype["mixed_fp8"]
 FP8E5 = ggml_tensor_qtype["fp8_e5m2"]
-IQ2_XXS = ggml_tensor_qtype["iq2_xxs"]
-IQ2_XS = ggml_tensor_qtype["iq2_xs"]
+IQ2_XXS = ggml_tensor_qtype["gguf_iq2_xxs"]
+IQ2_XS = ggml_tensor_qtype["gguf_iq2_xs"]
 Q2_K = ggml_tensor_qtype["q2_k"]
 
 

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

@@ -110,7 +110,7 @@ def from_pretrained(cls,
         :param load_in_low_bit: str value, options are ``'sym_int4'``, ``'asym_int4'``,
                                 ``'sym_int5'``, ``'asym_int5'``, ``'sym_int8'``, ``'nf3'``,
                                 ``'nf4'``, ``'fp4'``, ``'fp8'``, ``'fp8_e4m3'``, ``'fp8_e5m2'``,
-                                ``'iq2_xxs'``, ``'iq2_xs'``, ``'fp16'`` or ``'bf16'``,
+                                ``'gguf_iq2_xxs'``, ``'gguf_iq2_xs'``, ``'fp16'`` or ``'bf16'``,
                                 ``'sym_int4'`` means symmetric int 4, ``'asym_int4'`` means
                                 asymmetric int 4, ``'nf4'`` means 4-bit NormalFloat, etc.
                                 Relevant low bit optimizations will be applied to the model.
@@ -278,12 +278,13 @@ def from_pretrained(cls,
                     kwargs["pretraining_tp"] = 1
             q_k = load_in_low_bit if load_in_low_bit else "sym_int4"
             imatrix_file = kwargs.pop("imatrix", None)
-            if q_k in ["iq2_xxs", "iq2_xs"]:
+            if q_k in ["gguf_iq2_xxs", "gguf_iq2_xs"]:
                 invalidInputError(imatrix_file is not None,
-                                  "For iq2_xxs and iq2_xs quantization, imatrix is needed.")
+                                  "For gguf_iq2_xxs and gguf_iq2_xs quantization,"
+                                  "imatrix is needed.")
             cpu_embedding = kwargs.get("cpu_embedding", False)
             # for 2bit, default use embedding_quantization
-            if q_k in ["iq2_xxs", "iq2_xs", "q2_k"] and not cpu_embedding and \
+            if q_k in ["gguf_iq2_xxs", "gguf_iq2_xs", "q2_k"] and not cpu_embedding and \
                     embedding_qtype is None:
                 embedding_qtype = "q2_k"
             if imatrix_file is not None:

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

@@ -269,7 +269,7 @@ def module_name_process(full_module_name):
 
 def get_cur_qtype_and_imatrix(qtype, full_module_name, imatrix_data, model_type=None):
     cur_qtype = qtype
-    if qtype in [ggml_tensor_qtype["iq2_xxs"], ggml_tensor_qtype["iq2_xs"]]:
+    if qtype in [ggml_tensor_qtype["gguf_iq2_xxs"], ggml_tensor_qtype["gguf_iq2_xs"]]:
         # For quantization which needs importance matrix
         new_module_name, layer, cur_module = module_name_process(full_module_name)
         # custom mixed quantization strategy