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FIX: Qwen1.5-GPTQ-Int4 inference error #11432

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merged 4 commits into from
Jun 26, 2024
Merged

FIX: Qwen1.5-GPTQ-Int4 inference error #11432

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9798e05
merge_qkv if quant_method is 'gptq'
liu-shaojun Jun 26, 2024
887ac64
fix python style checks
liu-shaojun Jun 26, 2024
6e6db4e
refactor
liu-shaojun Jun 26, 2024
d0ea969
update GPU example
liu-shaojun Jun 26, 2024
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4 changes: 2 additions & 2 deletions ...lm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/GPTQ/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@ conda activate llm
pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
pip install transformers==4.34.0
BUILD_CUDA_EXT=0 pip install git+https://github.com/PanQiWei/AutoGPTQ.git@1de9ab6
pip install optimum==0.14.0
pip install optimum==1.14.0
```

On Windows:
Expand All @@ -30,7 +30,7 @@ pip install --pre --upgrade ipex-llm[all]
pip install transformers==4.34.0
set BUILD_CUDA_EXT=0
pip install git+https://github.com/PanQiWei/AutoGPTQ.git@1de9ab6
pip install optimum==0.14.0
pip install optimum==1.14.0
```

### 2. Run
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7 changes: 5 additions & 2 deletions python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/GPTQ/generate.py
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Expand Up @@ -19,7 +19,7 @@
import argparse

from ipex_llm.transformers import AutoModelForCausalLM
from transformers import LlamaTokenizer, GPTQConfig
from transformers import LlamaTokenizer, AutoTokenizer

# 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
Expand Down Expand Up @@ -50,7 +50,10 @@
trust_remote_code=True,)

# Load tokenizer
tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
if "qwen" in model_path.lower():
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
else:
tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)

# Generate predicted tokens
with torch.inference_mode():
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9 changes: 8 additions & 1 deletion python/llm/src/ipex_llm/transformers/convert.py
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Expand Up @@ -734,7 +734,14 @@ def _optimize_pre(model):
# for qwen2
if model.config.model_type == "qwen2":
from ipex_llm.transformers.models.qwen2 import merge_qkv
model.apply(merge_qkv)
# Skip merge_qkv if quant_method is 'gptq'
should_apply_merge_qkv = (
not hasattr(model.config, "quantization_config") or
not hasattr(model.config.quantization_config, "quant_method") or
model.config.quantization_config.quant_method != "gptq"
)
if should_apply_merge_qkv:
model.apply(merge_qkv)
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Is gptq loaded as q4_1? If so, why can't we use merge_qkv? @qiuxin2012

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We need a new merge_qkv for gptq here. Just convert q k v to one LowBitLinear.

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@jason-dai jason-dai Jun 26, 2024 •
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We need a new merge_qkv for gptq here. Just convert q k v to one LowBitLinear.

It's different from q4_1?

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gptq is loaded as q4_1. However, gptq's weights have already been quantized. If we want to use merge_qkv, we need to dequantize from the gptq format to the normal format first, perform merge_qkv, and then quantize back into the LowBitLinear.

I will try this method.

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gptq is loaded as q4_1. However, gptq's weights have already been quantized. If we want to use merge_qkv, we need to dequantize from the gptq format to the normal format first, perform merge_qkv, and then quantize back into the LowBitLinear.

I will try this method.

Instead of dequantization, I think we can just rearrange the quantized qkv tensors into a combined one? Anyway I think we may fix it later in a separate PR if needed.

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I'll merge this PR first so users can start using it, and then I'll submit another PR to address further fixes.

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I guess we probably cannot merge qkv if quantization_config.desc_act==True.

from ipex_llm.transformers.models.qwen2 import padding_mlp
model.apply(padding_mlp)
if model.config.model_type == "qwen2_moe":
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21 changes: 15 additions & 6 deletions python/llm/src/ipex_llm/transformers/models/qwen2.py
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Expand Up @@ -405,12 +405,21 @@ def qwen2_attention_forward(
bsz, q_len, _ = hidden_states.size()
device = hidden_states.device

qkv = self.qkv_proj(hidden_states)
qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
qkv = qkv.transpose(1, 2)
query_states, key_states, value_states = qkv.split([self.num_heads,
self.num_key_value_heads,
self.num_key_value_heads], dim=1)
if hasattr(self, 'qkv_proj') and self.qkv_proj is not None:
qkv = self.qkv_proj(hidden_states)
qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
qkv = qkv.transpose(1, 2)
query_states, key_states, value_states = qkv.split([self.num_heads,
self.num_key_value_heads,
self.num_key_value_heads], dim=1)
else:
# when quant_method is 'gptq'
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
Expand Down
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