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Remove all ipex usage (#12666)

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Yishuo Wang 2025-01-08 10:31:18 +08:00 committed by GitHub
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9 changed files with 39 additions and 553 deletions
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9
python/llm/dev/benchmark/all-in-one/run-stress-test.py
View file

@ -148,7 +148,7 @@ def run_transformer_int4_gpu(repo_id,
num_beams, num_beams,
low_bit): low_bit):
from ipex_llm.transformers import AutoModel, AutoModelForCausalLM from ipex_llm.transformers import AutoModel, AutoModelForCausalLM
from transformers import AutoTokenizer, GPTJForCausalLM, LlamaTokenizer from transformers import AutoTokenizer, LlamaTokenizer
import intel_extension_for_pytorch as ipex import intel_extension_for_pytorch as ipex
reserved_mem_list = [] reserved_mem_list = []
model_path = get_model_path(repo_id, local_model_hub) model_path = get_model_path(repo_id, local_model_hub)
@ -170,9 +170,6 @@ def run_transformer_int4_gpu(repo_id,
trust_remote_code=True, use_cache=True).eval() trust_remote_code=True, use_cache=True).eval()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = model.to('xpu') model = model.to('xpu')
if isinstance(model, GPTJForCausalLM):
# For gpt-j model family, this optimization can provide a better performance.
model = ipex.optimize(model.eval(), inplace=True)
end = time.perf_counter() end = time.perf_counter()
print(">> loading of model costs {}s".format(end - st)) print(">> loading of model costs {}s".format(end - st))
reserved_mem_list.append(torch.xpu.memory.memory_reserved()/(1024**3)) reserved_mem_list.append(torch.xpu.memory.memory_reserved()/(1024**3))
@ -227,7 +224,7 @@ if __name__ == '__main__':
today = date.today() today = date.today()
if 'exclude' in conf: if 'exclude' in conf:
excludes = conf['exclude'] excludes = conf['exclude']
import pandas as pd import pandas as pd
for api in conf.test_api: for api in conf.test_api:
for model in conf.repo_id: for model in conf.repo_id:
@ -240,7 +237,7 @@ if __name__ == '__main__':
run_model(model, api, in_out_pairs, conf['local_model_hub'], conf['warm_up'], conf['num_trials'], conf['num_beams'], run_model(model, api, in_out_pairs, conf['local_model_hub'], conf['warm_up'], conf['num_trials'], conf['num_beams'],
conf['low_bit'], conf['cpu_embedding']) conf['low_bit'], conf['cpu_embedding'])
df = pd.DataFrame(results, columns=['model', '1st token avg latency (ms)', '2+ avg latency (ms/token)', 'encoder time (ms)', df = pd.DataFrame(results, columns=['model', '1st token avg latency (ms)', '2+ avg latency (ms/token)', 'encoder time (ms)',
'input/output tokens', 'actual input/output tokens', 'num_beams', 'low_bit', 'cpu_embedding', 'input/output tokens', 'actual input/output tokens', 'num_beams', 'low_bit', 'cpu_embedding',
'peak mem (GB)']) 'peak mem (GB)'])
df.to_csv(f'{current_dir}/{api}-results-{today}.csv') df.to_csv(f'{current_dir}/{api}-results-{today}.csv')

42
python/llm/dev/benchmark/all-in-one/run.py
View file

@ -138,8 +138,8 @@ def preprocess_prompt(tokenizer, in_len, task):
elif in_len == 4096: elif in_len == 4096:
input_str = open(f"prompt/QA/orca_497.txt", 'r', encoding='utf-8').read() input_str = open(f"prompt/QA/orca_497.txt", 'r', encoding='utf-8').read()
else: else:
raise ValueError("No corresponding prompt available now, will be added later.") raise ValueError("No corresponding prompt available now, will be added later.")
input_ids = tokenizer.encode(input_str, return_tensors="pt") input_ids = tokenizer.encode(input_str, return_tensors="pt")
return input_ids return input_ids
def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1, num_trials=3, num_beams=1, low_bit='sym_int4', cpu_embedding=False, batch_size=1, streaming=False, use_fp16_torch_dtype=False, lookahead=False, task='continuation', optimize_model=False, transpose_value_cache=True, group_size=64): def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1, num_trials=3, num_beams=1, low_bit='sym_int4', cpu_embedding=False, batch_size=1, streaming=False, use_fp16_torch_dtype=False, lookahead=False, task='continuation', optimize_model=False, transpose_value_cache=True, group_size=64):
@ -222,7 +222,7 @@ def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1,
streaming if 'win' in test_api else 'N/A', streaming if 'win' in test_api else 'N/A',
use_fp16_torch_dtype if 'pipeline_parallel_gpu' in test_api else 'N/A', use_fp16_torch_dtype if 'pipeline_parallel_gpu' in test_api else 'N/A',
group_size if any(keyword in test_api for keyword in ['transformers_int4_npu_win', 'transformers_int4_npu_pipeline_win']) else 'N/A'], group_size if any(keyword in test_api for keyword in ['transformers_int4_npu_win', 'transformers_int4_npu_pipeline_win']) else 'N/A'],
) )
def get_model_path(repo_id, local_model_hub): def get_model_path(repo_id, local_model_hub):
@ -475,7 +475,7 @@ def run_transformer_int4_gpu(repo_id,
lookahead=False, lookahead=False,
task='continuation'): task='continuation'):
from ipex_llm.transformers import AutoModel, AutoModelForCausalLM from ipex_llm.transformers import AutoModel, AutoModelForCausalLM
from transformers import AutoTokenizer, GPTJForCausalLM, LlamaTokenizer from transformers import AutoTokenizer, LlamaTokenizer
model_path = get_model_path(repo_id, local_model_hub) model_path = get_model_path(repo_id, local_model_hub)
# Load model in 4 bit, # Load model in 4 bit,
# which convert the relevant layers in the model into INT4 format # which convert the relevant layers in the model into INT4 format
@ -490,7 +490,7 @@ def run_transformer_int4_gpu(repo_id,
model = AutoModel.load_low_bit(model_path, optimize_model=True, model = AutoModel.load_low_bit(model_path, optimize_model=True,
trust_remote_code=True, use_cache=True, trust_remote_code=True, use_cache=True,
cpu_embedding=cpu_embedding, cpu_embedding=cpu_embedding,
torch_dtype=torch_dtype).eval() torch_dtype=torch_dtype).eval()
else: else:
model = AutoModel.from_pretrained(model_path, load_in_low_bit=low_bit, optimize_model=True, model = AutoModel.from_pretrained(model_path, load_in_low_bit=low_bit, optimize_model=True,
trust_remote_code=True, use_cache=True, trust_remote_code=True, use_cache=True,
@ -507,7 +507,7 @@ def run_transformer_int4_gpu(repo_id,
model = AutoModelForCausalLM.from_pretrained(model_path, optimize_model=True, load_in_low_bit=low_bit, model = AutoModelForCausalLM.from_pretrained(model_path, optimize_model=True, load_in_low_bit=low_bit,
_attn_implementation="eager", _attn_implementation="eager",
modules_to_not_convert=["vision_embed_tokens"], modules_to_not_convert=["vision_embed_tokens"],
trust_remote_code=True, use_cache=True, trust_remote_code=True, use_cache=True,
cpu_embedding=cpu_embedding, torch_dtype=torch_dtype).eval() cpu_embedding=cpu_embedding, torch_dtype=torch_dtype).eval()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = model.to('xpu') model = model.to('xpu')
@ -632,14 +632,14 @@ def transformers_int4_npu_win(repo_id,
st = time.perf_counter() st = time.perf_counter()
if repo_id in MINICPM_V_IDS: if repo_id in MINICPM_V_IDS:
model = AutoModel.from_pretrained(model_path, load_in_low_bit=low_bit, optimize_model=optimize_model, model = AutoModel.from_pretrained(model_path, load_in_low_bit=low_bit, optimize_model=optimize_model,
trust_remote_code=True, use_cache=True, max_context_len=max_context_len, max_prompt_len=int(in_out_len[0]), trust_remote_code=True, use_cache=True, max_context_len=max_context_len, max_prompt_len=int(in_out_len[0]),
quantization_group_size=npu_group_size, transpose_value_cache=transpose_value_cache, quantization_group_size=npu_group_size, transpose_value_cache=transpose_value_cache,
save_directory=save_directory, attn_implementation="eager", torch_dtype=torch.float16).eval() save_directory=save_directory, attn_implementation="eager", torch_dtype=torch.float16).eval()
model = model.llm model = model.llm
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
else: else:
model = AutoModelForCausalLM.from_pretrained(model_path, load_in_low_bit=low_bit, trust_remote_code=True, torch_dtype=torch.float16, model = AutoModelForCausalLM.from_pretrained(model_path, load_in_low_bit=low_bit, trust_remote_code=True, torch_dtype=torch.float16,
optimize_model=optimize_model, max_context_len=max_context_len, max_prompt_len=int(in_out_len[0]), optimize_model=optimize_model, max_context_len=max_context_len, max_prompt_len=int(in_out_len[0]),
quantization_group_size=npu_group_size, transpose_value_cache=transpose_value_cache, quantization_group_size=npu_group_size, transpose_value_cache=transpose_value_cache,
save_directory=save_directory, use_cache=True, attn_implementation="eager").eval() save_directory=save_directory, use_cache=True, attn_implementation="eager").eval()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
@ -707,7 +707,7 @@ def transformers_int4_npu_pipeline_win(repo_id,
st = time.perf_counter() st = time.perf_counter()
model = AutoModelForCausalLM.from_pretrained(model_path, load_in_low_bit=low_bit, trust_remote_code=True, pipeline=True, torch_dtype=torch.float16, model = AutoModelForCausalLM.from_pretrained(model_path, load_in_low_bit=low_bit, trust_remote_code=True, pipeline=True, torch_dtype=torch.float16,
optimize_model=optimize_model, max_context_len=max_context_len, max_prompt_len=int(in_out_len[0]), optimize_model=optimize_model, max_context_len=max_context_len, max_prompt_len=int(in_out_len[0]),
quantization_group_size=npu_group_size, transpose_value_cache=transpose_value_cache, quantization_group_size=npu_group_size, transpose_value_cache=transpose_value_cache,
use_cache=True, attn_implementation="eager", use_cache=True, attn_implementation="eager",
save_directory=save_directory).eval() save_directory=save_directory).eval()
@ -843,7 +843,7 @@ def run_transformers_openvino(repo_id,
ov_config = {"PERFORMANCE_HINT": "LATENCY", ov_config = {"PERFORMANCE_HINT": "LATENCY",
"NUM_STREAMS": "1", "CACHE_DIR": ""} "NUM_STREAMS": "1", "CACHE_DIR": ""}
config_dict = dict(pretrained_model_name_or_path=model_path, config_dict = dict(pretrained_model_name_or_path=model_path,
trust_remote_code=True, trust_remote_code=True,
use_cache=True, low_cpu_mem_usage=True) use_cache=True, low_cpu_mem_usage=True)
@ -906,7 +906,7 @@ def run_optimize_model_gpu(repo_id,
num_beams, num_beams,
low_bit, low_bit,
batch_size): batch_size):
from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer, GPTJForCausalLM, LlamaTokenizer from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer
from ipex_llm import optimize_model from ipex_llm import optimize_model
model_path = get_model_path(repo_id, local_model_hub) model_path = get_model_path(repo_id, local_model_hub)
# Load model in 4 bit, # Load model in 4 bit,
@ -986,7 +986,7 @@ def run_ipex_fp16_gpu(repo_id,
num_beams, num_beams,
batch_size): batch_size):
from transformers import AutoModel, AutoModelForCausalLM from transformers import AutoModel, AutoModelForCausalLM
from transformers import AutoTokenizer, GPTJForCausalLM, LlamaTokenizer from transformers import AutoTokenizer, LlamaTokenizer
model_path = get_model_path(repo_id, local_model_hub) model_path = get_model_path(repo_id, local_model_hub)
st = time.perf_counter() st = time.perf_counter()
if repo_id in CHATGLM_IDS: if repo_id in CHATGLM_IDS:
@ -1051,7 +1051,7 @@ def run_bigdl_fp16_gpu(repo_id,
num_beams, num_beams,
batch_size): batch_size):
from ipex_llm.transformers import AutoModel, AutoModelForCausalLM from ipex_llm.transformers import AutoModel, AutoModelForCausalLM
from transformers import AutoTokenizer, GPTJForCausalLM, LlamaTokenizer from transformers import AutoTokenizer, LlamaTokenizer
model_path = get_model_path(repo_id, local_model_hub) model_path = get_model_path(repo_id, local_model_hub)
st = time.perf_counter() st = time.perf_counter()
if repo_id in CHATGLM_IDS: if repo_id in CHATGLM_IDS:
@ -1209,7 +1209,7 @@ def run_transformer_int4_gpu_win(repo_id,
batch_size, batch_size,
streaming): streaming):
from ipex_llm.transformers import AutoModel, AutoModelForCausalLM from ipex_llm.transformers import AutoModel, AutoModelForCausalLM
from transformers import AutoTokenizer, GPTJForCausalLM, LlamaTokenizer, TextStreamer from transformers import AutoTokenizer, LlamaTokenizer, TextStreamer
model_path = get_model_path(repo_id, local_model_hub) model_path = get_model_path(repo_id, local_model_hub)
# Load model in 4 bit, # Load model in 4 bit,
# which convert the relevant layers in the model into INT4 format # which convert the relevant layers in the model into INT4 format
@ -1338,7 +1338,7 @@ def run_transformer_int4_fp16_gpu_win(repo_id,
batch_size, batch_size,
streaming): streaming):
from ipex_llm.transformers import AutoModel, AutoModelForCausalLM from ipex_llm.transformers import AutoModel, AutoModelForCausalLM
from transformers import AutoTokenizer, GPTJForCausalLM, LlamaTokenizer, TextStreamer from transformers import AutoTokenizer, LlamaTokenizer, TextStreamer
model_path = get_model_path(repo_id, local_model_hub) model_path = get_model_path(repo_id, local_model_hub)
# Load model in 4 bit, # Load model in 4 bit,
# which convert the relevant layers in the model into INT4 format # which convert the relevant layers in the model into INT4 format
@ -1475,7 +1475,7 @@ def run_transformer_int4_loadlowbit_gpu_win(repo_id,
batch_size, batch_size,
streaming): streaming):
from ipex_llm.transformers import AutoModel, AutoModelForCausalLM from ipex_llm.transformers import AutoModel, AutoModelForCausalLM
from transformers import AutoTokenizer, GPTJForCausalLM, LlamaTokenizer, TextStreamer from transformers import AutoTokenizer, LlamaTokenizer, TextStreamer
model_path = get_model_path(repo_id, local_model_hub) model_path = get_model_path(repo_id, local_model_hub)
# Load BigDL-LLM optimized low bit model # Load BigDL-LLM optimized low bit model
st = time.perf_counter() st = time.perf_counter()
@ -1585,7 +1585,7 @@ def run_transformer_int4_fp16_loadlowbit_gpu_win(repo_id,
batch_size, batch_size,
streaming): streaming):
from ipex_llm.transformers import AutoModel, AutoModelForCausalLM from ipex_llm.transformers import AutoModel, AutoModelForCausalLM
from transformers import AutoTokenizer, GPTJForCausalLM, LlamaTokenizer, TextStreamer from transformers import AutoTokenizer, LlamaTokenizer, TextStreamer
model_path = get_model_path(repo_id, local_model_hub) model_path = get_model_path(repo_id, local_model_hub)
# Load BigDL-LLM optimized low bit model # Load BigDL-LLM optimized low bit model
st = time.perf_counter() st = time.perf_counter()
@ -1972,7 +1972,7 @@ def run_deepspeed_optimize_model_gpu(repo_id,
os.environ["WORLD_SIZE"] = str(world_size) os.environ["WORLD_SIZE"] = str(world_size)
os.environ["MASTER_PORT"] = os.environ.get("MASTER_PORT", "29500") os.environ["MASTER_PORT"] = os.environ.get("MASTER_PORT", "29500")
from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer, GPTJForCausalLM, LlamaTokenizer from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer
from ipex_llm import optimize_model from ipex_llm import optimize_model
import deepspeed import deepspeed
from deepspeed.accelerator.cpu_accelerator import CPU_Accelerator from deepspeed.accelerator.cpu_accelerator import CPU_Accelerator
@ -2013,7 +2013,7 @@ def run_deepspeed_optimize_model_gpu(repo_id,
# Move model back to xpu # Move model back to xpu
model = model.to(f'xpu:{local_rank}') model = model.to(f'xpu:{local_rank}')
# Modify backend related settings # Modify backend related settings
if world_size > 1: if world_size > 1:
get_accelerator().set_device(local_rank) get_accelerator().set_device(local_rank)
dist_backend = get_accelerator().communication_backend_name() dist_backend = get_accelerator().communication_backend_name()
@ -2215,7 +2215,7 @@ def run_pipeline_parallel_gpu(repo_id,
cpu_embedding, cpu_embedding,
fp16=False): fp16=False):
from ipex_llm.transformers import AutoModel, AutoModelForCausalLM, init_pipeline_parallel from ipex_llm.transformers import AutoModel, AutoModelForCausalLM, init_pipeline_parallel
from transformers import AutoTokenizer, GPTJForCausalLM, LlamaTokenizer from transformers import AutoTokenizer, LlamaTokenizer
init_pipeline_parallel() init_pipeline_parallel()
model_path = get_model_path(repo_id, local_model_hub) model_path = get_model_path(repo_id, local_model_hub)
pipeline_parallel_stages = torch.distributed.get_world_size() pipeline_parallel_stages = torch.distributed.get_world_size()
@ -2311,7 +2311,7 @@ if __name__ == '__main__':
transpose_value_cache = True transpose_value_cache = True
if 'transpose_value_cache' in conf: if 'transpose_value_cache' in conf:
transpose_value_cache = conf['transpose_value_cache'] transpose_value_cache = conf['transpose_value_cache']
import pandas as pd import pandas as pd
for api in conf.test_api: for api in conf.test_api:
global csv_name global csv_name

28
python/llm/src/ipex_llm/transformers/convert.py
View file

@ -680,18 +680,9 @@ def _replace_with_low_bit_linear(model, qtype, modules_to_not_convert=None,
optimize_lm_head=optimize_lm_head optimize_lm_head=optimize_lm_head
) )
device = module.weight.data.device device = module.weight.data.device
from ipex_llm.transformers.utils import get_ipex_version new_linear._parameters['weight'] = nn.Parameter(module.weight)
if get_ipex_version() < "2.1.10+xpu":
new_linear._parameters['weight'] = nn.Parameter(module.weight)
else:
# only from 2.1, ipex provides matmul_bias_out
# so we need to transpose weight
new_weight = module.weight.transpose(0, 1).contiguous()
new_linear._parameters['weight'] = nn.Parameter(new_weight)
new_linear.weight_type = 2
if module.bias is not None: if module.bias is not None:
new_linear._parameters['bias'] = nn.Parameter(module.bias.data)\ new_linear._parameters['bias'] = nn.Parameter(module.bias.data).to(device)
.to(device)
elif qtype == ggml_tensor_qtype["bf16"]: elif qtype == ggml_tensor_qtype["bf16"]:
module.to(torch.bfloat16) module.to(torch.bfloat16)
if _USE_VLLM: if _USE_VLLM:
@ -1452,21 +1443,6 @@ def _optimize_post(model):
module.MultiheadAttention, module.MultiheadAttention,
mpt_multihead_attention_forward mpt_multihead_attention_forward
) )
elif "gptj" in model.config.model_type:
# dolly-v1-6b
modeling_module_name = model.__class__.__module__
module = importlib.import_module(modeling_module_name)
from ipex_llm.transformers.models.gptj import gptj_attention_forward, gptj_model_forward,\
gptj_block_forward
convert_forward(model,
module.GPTJAttention,
gptj_attention_forward)
convert_forward(model,
module.GPTJModel,
gptj_model_forward)
convert_forward(model,
module.GPTJBlock,
gptj_block_forward)
elif "bloom" in model.config.model_type: elif "bloom" in model.config.model_type:
modeling_module_name = model.__class__.__module__ modeling_module_name = model.__class__.__module__
module = importlib.import_module(modeling_module_name) module = importlib.import_module(modeling_module_name)

2
python/llm/src/ipex_llm/transformers/loader.py
View file

@ -22,7 +22,7 @@ import time
from datetime import date from datetime import date
import argparse import argparse
from ipex_llm.utils.common import invalidInputError from ipex_llm.utils.common import invalidInputError
from transformers import AutoTokenizer, GPTJForCausalLM, LlamaTokenizer from transformers import AutoTokenizer, LlamaTokenizer
LLAMA_IDS = ['llama', 'vicuna', 'merged-baize'] LLAMA_IDS = ['llama', 'vicuna', 'merged-baize']

30
python/llm/src/ipex_llm/transformers/low_bit_linear.py
View file

@ -759,9 +759,9 @@ class FP16Linear(nn.Linear):
self.weight_length = self.out_len * self.in_len self.weight_length = self.out_len * self.in_len
self.qtype = ggml_tensor_qtype["fp16"] self.qtype = ggml_tensor_qtype["fp16"]
self.mp_group = mp_group self.mp_group = mp_group
# weigh_type = 1 means original weight # weight_type = 1 means original weight
# weigh_type = 2 means weight has been transposed # weight_type = 2 means weight has been transposed
# weigh_type = 3 means weight has been transposed by esimd method # weight_type = 3 means weight has been transposed by esimd method
self.weight_type = 1 self.weight_type = 1
self.optimize_lm_head = optimize_lm_head self.optimize_lm_head = optimize_lm_head
self.disable_fp16_opt = False self.disable_fp16_opt = False
@ -775,28 +775,14 @@ class FP16Linear(nn.Linear):
x = x.to(torch.float16) x = x.to(torch.float16)
if self.bias is not None and self.bias.dtype != x.dtype: if self.bias is not None and self.bias.dtype != x.dtype:
self.bias.data = self.bias.data.to(x.dtype) self.bias.data = self.bias.data.to(x.dtype)
if self.weight is not None and self.weight.dtype != x.dtype: if self.weight is not None and self.weight.dtype != x.dtype:
self.weight.data = self.weight.data.to(x.dtype) self.weight.data = self.weight.data.to(x.dtype)
if not self.use_esimd_kernel(x): if not self.use_esimd_kernel(x):
if ( invalidInputError(self.weight_type == 1, "weight_type should be 1")
get_ipex_version() < "2.1.10+xpu" result = F.linear(x, self.weight, self.bias)
or get_xpu_device_name(x.device) not in ["arc", "pvc"]
or self.disable_fp16_opt
):
if self.weight_type == 2:
self.weight = torch.nn.Parameter(self.weight.transpose(0, 1).contiguous(),
requires_grad=False)
self.weight_type = 1
result = F.linear(x, self.weight, self.bias)
else:
if self.weight_type == 1:
self.weight = torch.nn.Parameter(self.weight.transpose(0, 1).contiguous(),
requires_grad=False)
self.weight_type = 2
result = torch.ops.torch_ipex.matmul_bias_out(x.contiguous(),
self.weight, self.bias)
if self.mp_group is not None: if self.mp_group is not None:
if get_use_vllm(): if get_use_vllm():
result = self.mp_group.all_reduce(result) result = self.mp_group.all_reduce(result)
@ -852,7 +838,7 @@ class FP16Linear(nn.Linear):
if self.disable_fp16_opt: if self.disable_fp16_opt:
return False return False
# esimd kernel can only be used for Arc and Flex # esimd kernel can only be used for Arc and Flex
if gpu_type not in ["arc", "flex"]: if gpu_type not in ["arc"]:
return False return False
# now esimd kernel can only be used for specific cases (llama2-7b shape) # now esimd kernel can only be used for specific cases (llama2-7b shape)
if self.in_len == 11008 and self.out_features == 4096: if self.in_len == 11008 and self.out_features == 4096:

7
python/llm/src/ipex_llm/transformers/model.py
View file

@ -103,12 +103,6 @@ def save_low_bit(self, *args, **kwargs):
self.to(origin_device) self.to(origin_device)
def _load_pre():
from transformers import GPTJModel
from ipex_llm.transformers.models.gptj import gptj_model_new_init
GPTJModel.__init__ = gptj_model_new_init
class _BaseAutoModelClass: class _BaseAutoModelClass:
HF_MODEL = None HF_MODEL = None
@ -495,7 +489,6 @@ class _BaseAutoModelClass:
else: else:
if quant_config is not None: if quant_config is not None:
kwargs["quantization_config"] = quant_config kwargs["quantization_config"] = quant_config
_load_pre()
try: try:
# To handle the input CUDA setting (such as 'device_map={"":0}'), ignore it # To handle the input CUDA setting (such as 'device_map={"":0}'), ignore it
kwargs.pop('device_map', None) kwargs.pop('device_map', None)

441
python/llm/src/ipex_llm/transformers/models/gptj.py
View file

@ -1,441 +0,0 @@
#
# 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.
#
# This file is adapted from
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/gptj/modeling_gptj.py
#
import torch
from typing import Optional, Tuple, Union
from ipex_llm.transformers.models.utils import init_kv_cache, extend_kv_cache, \
apply_rotary_pos_emb, append_kv_cache, apply_ipex_rotate_every_two
from transformers.utils.import_utils import is_torch_fx_proxy
from transformers.modeling_outputs import BaseModelOutputWithPast
from transformers.models.gptj.modeling_gptj import GPTJModel
from ipex_llm.utils.common import invalidInputError
import os
KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
def _get_embed_positions(self, position_ids):
embed_positions = self.embed_positions
if embed_positions.device != position_ids.device:
embed_positions = embed_positions.to(position_ids.device)
self.embed_positions = embed_positions
return embed_positions.repeat(position_ids.shape[0], 1, 1)
def _attn(
self,
query,
key,
value,
attention_mask=None,
head_mask=None,
):
# compute causal mask from causal mask buffer
query_length, key_length = query.size(-2), key.size(-2)
causal_mask = self.bias[:, :, key_length - query_length: key_length, :key_length]
# Keep the attention weights computation in fp32 to avoid overflow issues
query = query.to(torch.float32)
key = key.to(torch.float32)
attn_weights = torch.matmul(query, key.transpose(-1, -2))
mask_value = torch.finfo(attn_weights.dtype).min
# Need to be a tensor, otherwise we get error:
# `RuntimeError: expected scalar type float but found double`.
# Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
attn_weights = torch.where(causal_mask, attn_weights, mask_value)
attn_weights = attn_weights / self.scale_attn
if attention_mask is not None:
# Apply the attention mask
attn_weights = attn_weights + attention_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
attn_weights = attn_weights.to(value.dtype)
attn_weights = self.attn_dropout(attn_weights)
# Mask heads if we want to
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = torch.matmul(attn_weights, value)
return attn_output, attn_weights
def gptj_attention_forward(
self,
hidden_states: torch.FloatTensor,
layer_past: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = False,
rotary_emb: Optional[Tuple]=None,
output_attentions: Optional[bool] = False,
) -> Union[
Tuple[torch.Tensor, Tuple[torch.Tensor]],
Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]],
]:
query = self.q_proj(hidden_states)
key = self.k_proj(hidden_states)
value = self.v_proj(hidden_states)
query = self._split_heads(query, self.num_attention_heads, self.head_dim, True)
key = self._split_heads(key, self.num_attention_heads, self.head_dim, True)
value = self._split_heads(value, self.num_attention_heads, self.head_dim, False)
sin, cos = rotary_emb
use_fuse_rope = hidden_states.device.type == "xpu" and not self.training
if self.rotary_dim is not None:
k_rot = key[:, :, :, : self.rotary_dim]
q_rot = query[:, :, :, : self.rotary_dim]
if use_fuse_rope:
apply_ipex_rotate_every_two(q_rot, k_rot, cos, sin)
else:
k_pass = key[:, :, :, self.rotary_dim:]
q_pass = query[:, :, :, self.rotary_dim:]
q_rot, k_rot = apply_rotary_pos_emb(q_rot, k_rot, cos, sin, position_ids, "gptj")
key = torch.cat([k_rot, k_pass], dim=-1)
query = torch.cat([q_rot, q_pass], dim=-1)
else:
if use_fuse_rope:
apply_ipex_rotate_every_two(query, key, cos, sin)
else:
query, key = apply_rotary_pos_emb(query, key, cos, sin, position_ids, "gptj")
batch_size, q_len, _ = hidden_states.size()
key = key.permute(0, 2, 1, 3).contiguous()
query = query.permute(0, 2, 1, 3).contiguous()
kv_seq_len = key.size(-2)
device = hidden_states.device
if layer_past is not None:
kv_seq_len += layer_past[0].size(2)
if layer_past is not None:
cache_k = layer_past[0]
cache_v = layer_past[1]
past_length = cache_k.size(2)
if cache_k.stride()[1] < kv_seq_len * cache_k.size(3):
new_cache_k, new_cache_v = extend_kv_cache(batch_size,
self.num_attention_heads,
self.head_dim,
past_length,
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
dtype=cache_v.dtype,
device=device)
new_cache_k[:] = cache_k
new_cache_v[:] = cache_v
cache_k = new_cache_k
cache_v = new_cache_v
key, value = append_kv_cache(cache_k, cache_v, key, value)
elif use_cache:
key_cache, value_cache = init_kv_cache(batch_size,
self.num_attention_heads,
self.head_dim,
kv_seq_len,
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
dtype=value.dtype,
device=device)
key_cache[:] = key
value_cache[:] = value
key = key_cache
value = value_cache
if use_cache is True:
present = (key, value)
else:
present = None
# compute self-attention: V x Softmax(QK^T)
attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_dim)
attn_output = self.out_proj(attn_output)
attn_output = self.resid_dropout(attn_output)
outputs = (attn_output, present)
if output_attentions:
outputs += (attn_weights,)
return outputs # a, present, (attentions)
def gptj_block_forward(
self,
hidden_states: Optional[torch.FloatTensor],
layer_past: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = False,
rotary_emb: Optional[Tuple]=None,
output_attentions: Optional[bool] = False,
) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_outputs = self.attn(
hidden_states=hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
use_cache=use_cache,
rotary_emb=rotary_emb,
output_attentions=output_attentions,
)
attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
outputs = attn_outputs[1:]
feed_forward_hidden_states = self.mlp(hidden_states)
hidden_states = attn_output + feed_forward_hidden_states + residual
if use_cache:
outputs = (hidden_states,) + outputs
else:
outputs = (hidden_states,) + outputs[1:]
return outputs # hidden_states, present, (attentions)
def create_sinusoidal_positions(num_pos: int, dim: int) -> torch.Tensor:
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2) / dim))
sinusoid_inp = torch.einsum("i , j -> i j",
torch.arange(num_pos, dtype=torch.float), inv_freq).float()
return torch.cat((torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)), dim=1)
old_init = GPTJModel.__init__
def gptj_model_new_init(self, config):
old_init(self, config)
embed_dim = config.hidden_size
rotary_dim = config.rotary_dim
pos_embd_dim = rotary_dim or embed_dim
max_positions = config.max_position_embeddings
self.embed_positions = create_sinusoidal_positions(max_positions, pos_embd_dim)
def get_new_embed_positions(position_ids, prev_embed_positions):
embed_positions = prev_embed_positions
if embed_positions.device != position_ids.device:
embed_positions = embed_positions.to(position_ids.device)
prev_embed_positions = embed_positions
return embed_positions.repeat(position_ids.shape[0], 1, 1), prev_embed_positions
def gptj_model_forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None \
else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None
else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
invalidInputError(False,
"You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
batch_size = input_ids.shape[0]
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size = inputs_embeds.shape[0]
else:
invalidInputError(False, "You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
if past_key_values is None:
past_length = 0
past_key_values = tuple([None] * len(self.h))
else:
past_length = past_key_values[0][0].size(-2)
if position_ids is None:
position_ids = torch.arange(past_length, input_shape[-1] + past_length,
dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0)
# Attention mask.
if attention_mask is not None:
if batch_size <= 0:
invalidInputError(False, "batch_size has to be defined and > 0")
attention_mask = attention_mask.view(batch_size, -1)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask = attention_mask[:, None, None, :]
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and the dtype's smallest value for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x num_attention_heads x N x N
# head_mask has shape n_layer x batch x num_attention_heads x N x N
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
hidden_states = inputs_embeds
if token_type_ids is not None:
token_type_embeds = self.wte(token_type_ids)
hidden_states = hidden_states + token_type_embeds
hidden_states = self.drop(hidden_states)
output_shape = (-1,) + input_shape[1:] + (hidden_states.size(-1),)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing."
"Setting `use_cache=False`..."
)
use_cache = False
presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
# Repeat cos sin here, call only once for each token.
# If put this to attension forward, it will generate too many times.
if is_torch_fx_proxy(position_ids) or torch.jit.is_tracing():
# The logic to conditionally copy to GPU could not be traced, so we do this
# every time in the torch.fx case
embed_positions = get_embed_positions(self.embed_positions, position_ids)
else:
embed_positions, self.embed_positions = get_new_embed_positions(position_ids,
self.embed_positions)
repeated_position_ids = position_ids.unsqueeze(-1).repeat(1, 1, embed_positions.shape[-1])
sincos = torch.gather(embed_positions, 1, repeated_position_ids)
sin, cos = torch.split(sincos, sincos.shape[-1] // 2, dim=-1)
sin = torch.repeat_interleave(sin[:, :, None, :], 2, 3)
cos = torch.repeat_interleave(cos[:, :, None, :], 2, 3)
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
# Model parallel
if self.model_parallel:
torch.cuda.set_device(hidden_states.device)
# Ensure layer_past is on same device as hidden_states (might not be correct)
if layer_past is not None:
layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past)
# Ensure that attention_mask is always on the same device as hidden_states
if attention_mask is not None:
attention_mask = attention_mask.to(hidden_states.device)
if isinstance(head_mask, torch.Tensor):
head_mask = head_mask.to(hidden_states.device)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
outputs = self._gradient_checkpointing_func(
block.__call__,
hidden_states,
None,
attention_mask,
position_ids,
head_mask[i],
use_cache,
output_attentions,
)
else:
outputs = block(
hidden_states=hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask[i],
use_cache=use_cache,
rotary_emb=(sin, cos),
output_attentions=output_attentions,
)
hidden_states = outputs[0]
if use_cache is True:
presents = presents + (outputs[1],)
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
# Model Parallel: If it's the last layer for that device, put things on the next device
if self.model_parallel:
for k, v in self.device_map.items():
if i == v[-1] and "cuda:" + str(k) != self.last_device:
hidden_states = hidden_states.to("cuda:" + str(k + 1))
hidden_states = self.ln_f(hidden_states)
hidden_states = hidden_states.view(output_shape)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions]
if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)

17
python/llm/src/ipex_llm/transformers/models/utils.py
View file

@ -168,7 +168,7 @@ def should_use_fuse_rope(hidden_states, position_ids, training):
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, model_family): def apply_rotary_pos_emb(q, k, cos, sin, position_ids, model_family):
if model_family in ["llama", "baichuan", "internlm", "aquila", "gpt_neox", "mistral", if model_family in ["llama", "baichuan", "internlm", "aquila", "gpt_neox", "mistral",
"mixtral", "qwen2", "yuan", "stablelm", "qwen2_moe"]: "qwen2", "yuan", "stablelm", "qwen2_moe"]:
# The first two dimensions of cos and sin are always 1, so we can `squeeze` them. # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
cos = cos.squeeze(1).squeeze(0) # [seq_len, dim] cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
sin = sin.squeeze(1).squeeze(0) # [seq_len, dim] sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
@ -183,7 +183,7 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, model_family):
q_embed = (q * cos) + (rotate_half(q) * sin) q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin) k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed return q_embed, k_embed
elif model_family in ["gptj", "chatglm"]: elif model_family in ["chatglm"]:
q_embed = (q * cos) + (rotate_every_two(q) * sin) q_embed = (q * cos) + (rotate_every_two(q) * sin)
k_embed = (k * cos) + (rotate_every_two(k) * sin) k_embed = (k * cos) + (rotate_every_two(k) * sin)
return q_embed, k_embed return q_embed, k_embed
@ -192,19 +192,6 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, model_family):
f"{model_family} is not supported.") f"{model_family} is not supported.")
def apply_ipex_rotate_every_two(q, k, cos, sin):
# ipex's apply_rotary_embedding_two_qk can change the origin storage,
# so q/k will get the result directly.
from ipex_llm.transformers.utils import get_ipex_version
if get_ipex_version() >= "2.1.10+xpu":
torch.ops.torch_ipex.apply_rotary_embedding_two_qk(
q, k, sin, cos, q, k
)
else:
torch.ops.torch_ipex.apply_rotary_embedding(q, sin, cos, q)
torch.ops.torch_ipex.apply_rotary_embedding(k, sin, cos, k)
def is_enough_kv_cache_room_4_36(past_key_value, idx, seq_len=1): def is_enough_kv_cache_room_4_36(past_key_value, idx, seq_len=1):
# to determinate if is enough kv cache room in transformers==4.36 # to determinate if is enough kv cache room in transformers==4.36
# seq_len for current seq len # seq_len for current seq len

16
python/llm/src/ipex_llm/transformers/speculative.py
View file

@ -432,8 +432,7 @@ def _check_and_extend_kv_cache(past_key_values, max_step_draft, kv_alloc_block_l
from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_31, \ from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_31, \
extend_kv_cache extend_kv_cache
enough_kv_room = True enough_kv_room = True
if model_type not in ["chatglm", "qwen", "baichuan", "llama", "mistral", if model_type not in ["chatglm", "qwen", "baichuan", "llama", "mistral", "opt"]:
"gptj", "opt"]:
return past_key_values, False return past_key_values, False
cache_k = past_key_values[0][0] cache_k = past_key_values[0][0]
if model_type == "chatglm": if model_type == "chatglm":
@ -527,7 +526,7 @@ def _crop_past_key_values(self, past_key_values, new_cache_size, _enable_ipex=Fa
v[:-(new_cache_size), :, :, :]) v[:-(new_cache_size), :, :, :])
for k, v in past_key_values for k, v in past_key_values
] ]
elif self.config.model_type in ["baichuan", "gptj"]: elif self.config.model_type in ["baichuan"]:
past_key_values = [ past_key_values = [
(k[:, :, :-(new_cache_size), :], (k[:, :, :-(new_cache_size), :],
v[:, :, :-(new_cache_size), :]) v[:, :, :-(new_cache_size), :])
@ -796,13 +795,6 @@ def _non_cpu_ipex_verify(self, verify_input_ids, past_key_values, cur_attention_
device=verify_input_ids.device) device=verify_input_ids.device)
position_ids = position_ids.unsqueeze(0).repeat(1, 1) + past_key_value_len position_ids = position_ids.unsqueeze(0).repeat(1, 1) + past_key_value_len
forward_args["position_ids"] = position_ids forward_args["position_ids"] = position_ids
elif self.config.model_type == "gptj":
past_length = past_key_values[0][0].size(2)
input_len = verify_input_ids.shape[1]
position_ids = torch.arange(past_length, input_len + past_length,
dtype=torch.long, device=verify_input_ids.device)
position_ids = position_ids.unsqueeze(0).view(-1, input_len)
forward_args["position_ids"] = position_ids
return self(**forward_args) return self(**forward_args)
@ -971,10 +963,6 @@ def speculative_generate(self,
past_key_value_len = past_key_values[0][0].shape[0] past_key_value_len = past_key_values[0][0].shape[0]
position_ids = torch.Tensor([[past_key_value_len + step_draft]]).long() position_ids = torch.Tensor([[past_key_value_len + step_draft]]).long()
forward_args["position_ids"] = position_ids forward_args["position_ids"] = position_ids
elif self.config.model_type == "gptj":
past_length = draft_past_key_values[0][0].size(2)
position_ids = torch.Tensor([[past_length]]).long().to(self.device)
forward_args["position_ids"] = position_ids
if _enable_ipex: if _enable_ipex:
if any(keyword in self.config.model_type if any(keyword in self.config.model_type