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Initial NPU support for MiniCPM-V-2_6 (#11966)

Browse source 
* initial pr

* update npu model

* fix

* fix kv cache type

* fix

* small fix

* fix style

* fix model id

* change inter_pp=4

* address comment

* fix

* fix style

* fix

* rebase
This commit is contained in:
Ruonan Wang 2024-08-30 01:34:35 -07:00 committed by GitHub
parent 158289d205
commit 60aa1a2c0f
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6 changed files with 129 additions and 20 deletions
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92
python/llm/example/NPU/HF-Transformers-AutoModels/Multimodal/minicpm_v_2_6.py Normal file
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@ -0,0 +1,92 @@
#
# 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 os
import time
import argparse
import requests
from PIL import Image
from ipex_llm.transformers.npu_model import AutoModel
from transformers import AutoTokenizer
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Predict Tokens using `chat()` API for openbmb/MiniCPM-V-2_6 model')
parser.add_argument('--repo-id-or-model-path', type=str, default="openbmb/MiniCPM-V-2_6",
help='The huggingface repo id for the openbmb/MiniCPM-V-2_6 model to be downloaded'
', or the path to the huggingface checkpoint folder')
parser.add_argument('--image-url-or-path', type=str,
default='http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg',
help='The URL or path to the image to infer')
parser.add_argument('--prompt', type=str, default="What is in this image?",
help='Prompt to infer')
parser.add_argument("--n-predict", type=int, default=32, help="Max tokens to predict")
parser.add_argument("--max-output-len", type=int, default=1024)
parser.add_argument("--max-prompt-len", type=int, default=960)
parser.add_argument("--disable-transpose-value-cache", action="store_true", default=False)
parser.add_argument("--intra-pp", type=int, default=2)
parser.add_argument("--inter-pp", type=int, default=2)
args = parser.parse_args()
model_path = args.repo_id_or_model_path
image_path = args.image_url_or_path
model = AutoModel.from_pretrained(model_path,
torch_dtype=torch.float32,
trust_remote_code=True,
attn_implementation="eager",
load_in_low_bit="sym_int4",
optimize_model=True,
max_output_len=args.max_output_len,
max_prompt_len=args.max_prompt_len,
intra_pp=args.intra_pp,
inter_pp=args.inter_pp,
transpose_value_cache=not args.disable_transpose_value_cache,
modules_to_not_convert=['vpm', 'resampler']
)
tokenizer = AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True)
model.eval()
query = args.prompt
if os.path.exists(image_path):
image = Image.open(image_path).convert('RGB')
else:
image = Image.open(requests.get(image_path, stream=True).raw).convert('RGB')
# Generate predicted tokens
# here the prompt tuning refers to https://huggingface.co/openbmb/MiniCPM-V-2_6/blob/main/README.md
msg = [{'role': 'user', 'content': args.prompt}]
st = time.time()
with torch.inference_mode():
res = model.chat(
image=image,
msgs=msg,
context=None,
tokenizer=tokenizer,
sampling=True,
)
end = time.time()
print(f'Inference time: {end-st} s')
print('-'*20, 'Input', '-'*20)
print(image_path)
print('-'*20, 'Prompt', '-'*20)
print(args.prompt)
output_str = res
print('-'*20, 'Output', '-'*20)
print(output_str)

23
python/llm/src/ipex_llm/transformers/npu_model.py
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@ -113,7 +113,6 @@ class _BaseAutoModelClass:
ignore_argument(kwargs, "cpu_embedding")
ignore_argument(kwargs, "embedding_qtype")
ignore_argument(kwargs, "enable_mp")
ignore_argument(kwargs, "modules_to_not_convert")
ignore_argument(kwargs, "quantization_config")
ignore_argument(kwargs, "speculative")
ignore_argument(kwargs, "pipeline_parallel_stages")
@ -123,6 +122,7 @@ class _BaseAutoModelClass:
inter_pp = kwargs.pop("inter_pp", None)
intra_pp = kwargs.pop("intra_pp", None)
transpose_value_cache = kwargs.pop("transpose_value_cache", True)
modules_to_not_convert = kwargs.pop("modules_to_not_convert", [])
_args = copy.deepcopy(args)
_kwargs = copy.deepcopy(kwargs)
@ -152,17 +152,14 @@ class _BaseAutoModelClass:
)
from ipex_llm.transformers.npu_models.convert_mp import optimize_llm, optimize_llm_pre
if model.config.model_type == "minicpmv":
if hasattr(model, "llm"):
llm = model.llm
if llm.config.hidden_size == 4096 and llm.config.vocab_size == 128256:
# MiniCPM-llama3-V2.5
llm.config.model_type = "llama"
else:
llm = model
with torch.no_grad():
optimize_llm_pre(llm, qtype)
cls.load_convert(qtype, llm, "cpu", *args, **kwargs)
optimize_llm_pre(model, qtype)
cls.load_convert(qtype, model, "cpu", modules_to_not_convert, *args, **kwargs)
create_npu_kernels(llm)
model = model.eval()
logger.info(f"Finish to convert model")
@ -181,8 +178,11 @@ class _BaseAutoModelClass:
from ipex_llm.transformers.npu_models.convert import optimize_llm
optimize_llm(model)
with torch.no_grad():
cls.load_convert(qtype, model, "cpu", *args, **kwargs)
create_npu_kernels(model)
cls.load_convert(qtype, model, "cpu", modules_to_not_convert, *args, **kwargs)
if hasattr(model, "llm"):
create_npu_kernels(model.llm)
else:
create_npu_kernels(model)
model = model.eval()
logger.info(f"Finish to convert model")
model.config.update({"bigdl_transformers_low_bit": qtype})
@ -192,10 +192,11 @@ class _BaseAutoModelClass:
return model
@classmethod
def load_convert(cls, q_k, optimize_model, device, *arg, **kwarg):
def load_convert(cls, q_k, optimize_model, device, modules_to_not_convert, *arg, **kwarg):
from ipex_llm.transformers.npu_models.convert import replace_with_QuantizedLinear
replace_with_QuantizedLinear(optimize_model, q_k, device=device)
replace_with_QuantizedLinear(optimize_model, q_k, device=device,
modules_to_not_convert=modules_to_not_convert)
@classmethod
@patch("transformers.dynamic_module_utils.get_imports", patch_flash_attn_import)

17
python/llm/src/ipex_llm/transformers/npu_models/convert.py
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@ -31,7 +31,7 @@ def module_optimization(func) -> torch.nn.Module:
torch.nn.Module: optimized module
"""
def wrapper(model: torch.nn.Module, qtype, device, *args, **kwargs):
def wrapper(model: torch.nn.Module, qtype, device, modules_to_not_convert, *args, **kwargs):
"""Recursively apply the optimization function.
Args:
@ -41,18 +41,19 @@ def module_optimization(func) -> torch.nn.Module:
"""
for name, layer in model.named_children():
new_layer = func(layer, qtype, device, *args, **kwargs)
if new_layer:
model.add_module(name, new_layer)
wrapper(new_layer, qtype, device, *args, **kwargs)
else:
wrapper(layer, qtype, device, *args, **kwargs)
if name not in modules_to_not_convert:
new_layer = func(layer, qtype, device, modules_to_not_convert, *args, **kwargs)
if new_layer:
model.add_module(name, new_layer)
wrapper(new_layer, qtype, device, modules_to_not_convert, *args, **kwargs)
else:
wrapper(layer, qtype, device, modules_to_not_convert, *args, **kwargs)
return wrapper
@module_optimization
def replace_with_QuantizedLinear(layer, qtype, device):
def replace_with_QuantizedLinear(layer, qtype, device, modules_to_not_convert):
from ipex_llm.transformers.low_bit_linear import ggml_convert_qtype
from ipex_llm.ggml.quantize import ggml_tensor_qtype
iqtype = ggml_tensor_qtype[qtype]

10
python/llm/src/ipex_llm/transformers/npu_models/convert_mp.py
View file

@ -42,6 +42,16 @@ def optimize_llm_pre(model: torch.nn.Module, qtype):
from ipex_llm.transformers.models.baichuan import pre_compute_inv_freq
model.apply(pre_compute_inv_freq)
if model.config.model_type == "minicpmv" and hasattr(model, "llm"):
# MiniCPM-V
if model.config.hidden_size == 2304 and model.config.vocab_size == 122753:
model.llm.config.model_type = "minicpm"
elif model.config.hidden_size == 3584 and model.config.vocab_size == 151666:
model.llm.config.model_type = "qwen2"
elif model.config.hidden_size == 4096 and model.config.vocab_size == 128256:
model.llm.config.model_type = "llama"
model = model.llm
# lm_head to cpu optimization
if os.environ.get("IPEX_LLM_CPU_LM_HEAD", "0") != "0":
# disable the optimization by default

3
python/llm/src/ipex_llm/transformers/npu_models/kv.py
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@ -173,7 +173,8 @@ class DynamicFusedNormalCache(DynamicCache):
head_dim,
0,
max_len,
key_states.dtype,
# key_states.dtype,
torch.float16,
key_states.device,
tranpose_value=transpose_value,
)

4
python/llm/src/ipex_llm/transformers/npu_models/qwen2_mp.py
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@ -197,7 +197,9 @@ class LowBitQwenMultiDecoderlayer(LLMBaseNNFactory):
new_key_states = self.convert_to_fp16(curr_key_values[i][0])
new_value_states = self.convert_to_fp16(curr_key_values[i][1])
print("start compiling")
self.compile()
print("end compiling")
def mlp(self, hidden_states):
mm1 = self.linear(
@ -862,6 +864,8 @@ class PrefillRunner:
self.p.daemon = True
self.p.start()
output = self.prefill_result_queue.get()
print(Fore.GREEN + f"prefill process output: {output}")
print(Style.RESET_ALL)
def forward(
self,