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IPEX support FP8 kvcache

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Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
This commit is contained in:
Wang, Yi A 2025-03-29 02:31:38 -07:00
parent 0142550096
commit 065f87a337
3 changed files with 27 additions and 7 deletions
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4
Dockerfile_intel
View File

@ -119,7 +119,9 @@ ENV CCL_TOPO_FABRIC_VERTEX_CONNECTION_CHECK=0
ENV TORCH_DEVICE_BACKEND_AUTOLOAD=0
RUN pip install https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/oneccl_bind_pt-2.6.0%2Bxpu-cp311-cp311-linux_x86_64.whl
RUN pip install https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/intel_extension_for_pytorch-2.6.10%2Bxpu-cp311-cp311-linux_x86_64.whl
RUN git clone https://github.com/intel/intel-extension-for-pytorch && cd intel-extension-for-pytorch && git checkout d5a7036316a01ea8220eb4da78a2207c423a1166
RUN sed -i 's/VERSION_MINOR 7/VERSION_MINOR 6/' intel-extension-for-pytorch/version.txt
RUN cd intel-extension-for-pytorch && git submodule update --init --recursive && USE_AOT_DEVLIST='pvc,ats-m150' BUILD_SEPARATE_OPS=OFF BUILD_WITH_CPU=OFF USE_XETLA=ON python setup.py install && rm -rf /usr/src/intel-extension-for-pytorch
# Install benchmarker
COPY --from=builder /usr/src/target/release-opt/text-generation-benchmark /usr/local/bin/text-generation-benchmark
# Install router

6
server/text_generation_server/layers/attention/ipex.py
View File

@ -45,6 +45,8 @@ def attention(
causal,
block_tables,
None,
k_scale=kv_scales.key_scale_cpu,
v_scale=kv_scales.value_scale_cpu,
)
else:
ipex.llm.functional.varlen_attention(
@ -99,6 +101,8 @@ def paged_attention(
True,
block_tables,
None,
k_scale=kv_scales.key_scale_cpu,
v_scale=kv_scales.value_scale_cpu,
)
else:
input_lengths = seqlen.input_lengths + seqlen.cache_lengths
@ -114,6 +118,8 @@ def paged_attention(
BLOCK_SIZE,
max_s,
None,
k_scale=kv_scales.key_scale_cpu,
v_scale=kv_scales.value_scale_cpu,
)
return out

24
server/text_generation_server/layers/attention/kv_cache.py
View File

@ -68,15 +68,20 @@ class KVCache:
if dtype in {torch.float8_e5m2, torch.float8_e4m3fn}:
if not (
(ATTENTION == "flashinfer" and SYSTEM == "cuda")
or (ATTENTION == "paged" and SYSTEM in ("cuda", "rocm"))
or (ATTENTION == "paged" and SYSTEM in ("cuda", "rocm", "ipex"))
or (ATTENTION == "flashdecoding-ipex")
):
raise ValueError(
"FP8 KV cache is currently only supported for flashinfer on CUDA and paged attention on CUDA and ROCm. "
"FP8 KV cache is currently only supported for flashinfer on CUDA and paged attention on CUDA, ROCm and INTEL IPEX and flashdecoding in Intel IPEX "
)
if SYSTEM == "rocm" and dtype == torch.float8_e5m2:
raise ValueError(
"float8_e5m2 FP8 KV cache is not supported on AMD ROCm"
)
if device.type == "cpu" and dtype == torch.float8_e4m3fn:
raise ValueError(
"float8_e4m3fn FP8 KV cache is not supported on Intel IPEX CPU"
)
element_size = torch.tensor([], dtype=dtype).element_size()
if SYSTEM == "ipex" and device.type == "xpu":
@ -133,7 +138,8 @@ class KVCache:
return False
elif self.dtype == torch.float8_e4m3fn and (
(ATTENTION in ("paged", "flashinfer") and SYSTEM == "cuda")
or (ATTENTION == "paged" and SYSTEM == "rocm")
or (ATTENTION == "paged" and SYSTEM in ["rocm", "ipex"])
or (ATTENTION == "flashdecoding-ipex")
):
log_once(logger.info, "Using FP8 KV cache scales")
return True
@ -141,7 +147,7 @@ class KVCache:
# We have scales, but not the correct FP8 cache type, so warn once.
log_once(
logger.info,
"Ignoring FP8 KV cache scales, supported only for float8_e4m3fn KV cache with flashinfer on CUDA and paged attention on ROCm",
"Ignoring FP8 KV cache scales, supported only for float8_e4m3fn KV cache with flashinfer on CUDA and paged attention on ROCm/IPEX and flashdecoding on IPEX",
)
return False
@ -208,7 +214,13 @@ class KVCache:
import intel_extension_for_pytorch as ipex
ipex.llm.modules.PagedAttention.reshape_and_cache_flash(
key, value, key_cache, value_cache, slots
key,
value,
key_cache,
value_cache,
slots,
k_scale=kv_scales.key_scale_cpu,
v_scale=kv_scales.value_scale_cpu,
)
else:
paged_reshape_and_cache(
@ -268,7 +280,7 @@ def paged_reshape_and_cache(
import intel_extension_for_pytorch as ipex
ipex.llm.modules.PagedAttention.reshape_and_cache(
key, value, key_cache, value_cache, slots
key, value, key_cache, value_cache, slots, k_scale=k_scale, v_scale=v_scale
)
else:
raise NotImplementedError(