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enable xpu on kv-cache and hqq doc (#39246)

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Signed-off-by: jiqing-feng <jiqing.feng@intel.com>
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jiqing-feng
2025-07-07 21:12:02 +08:00
committed by GitHub
parent 32db48db73
commit 14cba7ad33
2 changed files with 19 additions and 16 deletions
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28
docs/source/en/kv_cache.md
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@@ -44,7 +44,7 @@ import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16).to("cuda:0")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16, device_map="auto")
inputs = tokenizer("I like rock music because", return_tensors="pt").to(model.device)
model.generate(**inputs, do_sample=False, max_new_tokens=20, use_cache=False)
@@ -59,7 +59,7 @@ import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16).to("cuda:0")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16, device_map="auto")
inputs = tokenizer("I like rock music because", return_tensors="pt").to(model.device)
past_key_values = DynamicCache()
@@ -142,13 +142,14 @@ Enable [`QuantizedCache`] by configuring `cache_implementation="quantized"` in [
For [`HQQQuantizedCache`], we recommend setting the `axis-key` and `axis-value` parameters to `1`.
```py
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, HQQQuantizedCache, QuantizedCacheConfig
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16).to("cuda:0")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16, device_map="auto")
inputs = tokenizer("I like rock music because", return_tensors="pt").to(model.device)
out = model.generate(**inputs, do_sample=False, max_new_tokens=20, cache_implementation="quantized", cache_config={"axis-key": 1, "axis-value": 1, "backend": "hqq"})
out = model.generate(**inputs, do_sample=False, max_new_tokens=20, cache_implementation="quantized", cache_config={"backend": "HQQ"})
print(tokenizer.batch_decode(out, skip_special_tokens=True)[0])
I like rock music because it's loud and energetic. It's a great way to express myself and rel
```
@@ -159,13 +160,14 @@ I like rock music because it's loud and energetic. It's a great way to express m
For [`QuantoQuantizedCache`], we recommend setting the `axis-key` and `axis-value` parameters to `0`.
```py
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, QuantoQuantizedCache, QuantizedCacheConfig
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16).to("cuda:0")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16, device_map="auto")
inputs = tokenizer("I like rock music because", return_tensors="pt").to(model.device)
out = model.generate(**inputs, do_sample=False, max_new_tokens=20, cache_implementation="quantized", cache_config={"nbits": 4, "axis-key": 0, "axis-value": 0, "backend": "quanto"})
out = model.generate(**inputs, do_sample=False, max_new_tokens=20, cache_implementation="quantized", cache_config={"nbits": 4, "backend": "quanto"})
print(tokenizer.batch_decode(out, skip_special_tokens=True)[0])
I like rock music because it's loud and energetic. It's a great way to express myself and rel
```
@@ -207,14 +209,14 @@ import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16, device_map="auto")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16, device_map={"": 0})
inputs = tokenizer("Hello, my name is", return_tensors="pt").to(model.device)
out = model.generate(**inputs, do_sample=False, max_new_tokens=20, cache_implementation="offloaded_static")
tokenizer.batch_decode(out, skip_special_tokens=True)[0]
"Hello, my name is [Your Name], and I am a [Your Profession] with [Number of Years] of"
```
Cache offloading requires a CUDA GPU.
Cache offloading requires a CUDA GPU or Intel XPU.
### Sliding window cache
@@ -227,7 +229,7 @@ import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", torch_dtype=torch.float16).to("cuda:0")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", torch_dtype=torch.float16, device_map="auto")
inputs = tokenizer("Yesterday I was on a rock concert and.", return_tensors="pt").to(model.device)
out = model.generate(**inputs, do_sample=False, max_new_tokens=30, cache_implementation="sliding_window")
@@ -306,15 +308,15 @@ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, DynamicCache, StaticCache
model_id = "meta-llama/Llama-2-7b-chat-hf"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="cuda")
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map={"": 0})
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Init StaticCache with big enough max-length (1024 tokens for the below example)
# You can also init a DynamicCache, if that suits you better
prompt_cache = StaticCache(config=model.config, max_batch_size=1, max_cache_len=1024, device="cuda", dtype=torch.bfloat16)
prompt_cache = StaticCache(config=model.config, max_batch_size=1, max_cache_len=1024, device=model.device.type, dtype=torch.bfloat16)
INITIAL_PROMPT = "You are a helpful assistant. "
inputs_initial_prompt = tokenizer(INITIAL_PROMPT, return_tensors="pt").to("cuda")
inputs_initial_prompt = tokenizer(INITIAL_PROMPT, return_tensors="pt").to(model.device.type)
# This is the common prompt cached, we need to run forward without grad to be able to copy
with torch.no_grad():
prompt_cache = model(**inputs_initial_prompt, past_key_values = prompt_cache).past_key_values
@@ -322,7 +324,7 @@ with torch.no_grad():
prompts = ["Help me to write a blogpost about travelling.", "What is the capital of France?"]
responses = []
for prompt in prompts:
new_inputs = tokenizer(INITIAL_PROMPT + prompt, return_tensors="pt").to("cuda")
new_inputs = tokenizer(INITIAL_PROMPT + prompt, return_tensors="pt").to(model.device.type)
past_key_values = copy.deepcopy(prompt_cache)
outputs = model.generate(**new_inputs, past_key_values=past_key_values,max_new_tokens=20)
response = tokenizer.batch_decode(outputs)[0]

7
docs/source/en/quantization/hqq.md
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@@ -20,7 +20,7 @@ rendered properly in your Markdown viewer.
HQQ further supports fine-tuning with [PEFT](https://huggingface.co/docs/peft) and is fully compatible with [torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) for even faster inference and training.
Install HQQ with the following command to get the latest version and to build its corresponding CUDA kernels.
Install HQQ with the following command to get the latest version and to build its corresponding CUDA kernels if you are using a cuda device. It also support Intel XPU with pure pytorch implementation.
```bash
pip install hqq
@@ -34,13 +34,14 @@ You can choose to either replace all the linear layers in a model with the same
Quantize a model by creating a [`HqqConfig`] and specifying the `nbits` and `group_size` to replace for all the linear layers ([torch.nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html)) of the model.
``` py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, HqqConfig
quant_config = HqqConfig(nbits=8, group_size=64)
model = transformers.AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.1-8B",
torch_dtype=torch.float16,
device_map="cuda",
device_map="auto",
quantization_config=quant_config
)
```
@@ -67,7 +68,7 @@ quant_config = HqqConfig(dynamic_config={
model = transformers.AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.1-8B",
torch_dtype=torch.float16,
device_map="cuda",
device_map="auto",
quantization_config=quant_config
)
```