diff --git a/Dockerfile_intel b/Dockerfile_intel index b2a905ec..3bc04332 100644 --- a/Dockerfile_intel +++ b/Dockerfile_intel @@ -98,7 +98,8 @@ ENV HF_HOME=/data \ WORKDIR /usr/src -RUN pip install torch==2.6.0 torchvision==0.21.0 --index-url https://download.pytorch.org/whl/xpu + +RUN pip install torch==2.7.0 torchvision==0.22.0 --index-url https://download.pytorch.org/whl/xpu # Install server COPY proto proto @@ -116,8 +117,8 @@ ENV TORCH_LLM_ALLREDUCE=1 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 pip install https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/xpu/oneccl_bind_pt-2.7.0%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-2.7.10%2Bxpu-cp311-cp311-linux_x86_64.whl # Install benchmarker COPY --from=builder /usr/src/target/release-opt/text-generation-benchmark /usr/local/bin/text-generation-benchmark # Install router @@ -180,13 +181,13 @@ RUN case ${TARGETPLATFORM} in \ RUN conda install -c conda-forge gperftools mkl -RUN pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cpu -RUN pip install triton==3.1.0 py-libnuma +RUN pip install torch==2.7.0 torchvision==0.22.0 torchaudio==2.7.0 --index-url https://download.pytorch.org/whl/cpu +RUN pip install triton==3.2.0 py-libnuma WORKDIR /usr/src -RUN pip install https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/cpu/intel_extension_for_pytorch-2.6.0%2Bcpu-cp311-cp311-linux_x86_64.whl -RUN pip install https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/cpu/oneccl_bind_pt-2.6.0%2Bcpu-cp311-cp311-linux_x86_64.whl +RUN pip install https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/cpu/intel_extension_for_pytorch-2.7.0%2Bcpu-cp311-cp311-linux_x86_64.whl +RUN pip install https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_stable/cpu/oneccl_bind_pt-2.7.0%2Bcpu-cp311-cp311-linux_x86_64.whl ENV LD_PRELOAD=/opt/conda/lib/libtcmalloc.so diff --git a/server/text_generation_server/layers/attention/ipex.py b/server/text_generation_server/layers/attention/ipex.py index 2b89060e..36ef2efc 100644 --- a/server/text_generation_server/layers/attention/ipex.py +++ b/server/text_generation_server/layers/attention/ipex.py @@ -8,7 +8,10 @@ from text_generation_server.models.globals import ( BLOCK_SIZE, ) -SUPPORTS_WINDOWING = False +if ATTENTION == "flashdecoding-ipex": + SUPPORTS_WINDOWING = True +else: + SUPPORTS_WINDOWING = False def attention( @@ -25,13 +28,19 @@ def attention( causal: bool = True, softcap: Optional[float] = None, ): - if softcap is not None: - raise NotImplementedError("softcap is not available in IPEX") out = torch.empty_like(query) + kv_cache_dtype = "auto" + if kv_cache.key.dtype == torch.float8_e5m2: + kv_cache_dtype = "fp8_e5m2" + if kv_cache.key.dtype == torch.float8_e4m3fn: + kv_cache_dtype = "fp8_e4m3" # We do not need to check window_size_left (not supported) here, so it is already checked ahead of time at model load. if ATTENTION == "flashdecoding-ipex": + window_size_right = -1 if window_size_left == -1 else 0 + if softcap is None: + softcap = -1.0 ipex.llm.modules.PagedAttention.flash_attn_varlen_func( out, query.contiguous() if query.device.type == "xpu" else query, @@ -45,8 +54,18 @@ def attention( causal, block_tables, None, + window_size_left=window_size_left, + window_size_right=window_size_right, + kv_cache_dtype=kv_cache_dtype, + k_scale=kv_scales.key_scale_cpu, + v_scale=kv_scales.value_scale_cpu, + softcap=softcap, ) else: + if softcap is not None: + raise NotImplementedError( + "softcap is not available in IPEX paged attention" + ) ipex.llm.functional.varlen_attention( query.contiguous() if query.device.type == "xpu" else query, key.contiguous() if key.device.type == "xpu" else key, @@ -80,12 +99,16 @@ def paged_attention( softcap: Optional[float] = None, window_size_left: Optional[int] = -1, ): - if softcap is not None: - raise NotImplementedError("softcap is not available in IPEX") - out = torch.empty_like(query) - + kv_cache_dtype = "auto" + if kv_cache.key.dtype == torch.float8_e5m2: + kv_cache_dtype = "fp8_e5m2" + if kv_cache.key.dtype == torch.float8_e4m3fn: + kv_cache_dtype = "fp8_e4m3" if ATTENTION == "flashdecoding-ipex": + window_size_right = -1 if window_size_left == -1 else 0 + if softcap is None: + softcap = -1.0 ipex.llm.modules.PagedAttention.flash_attn_varlen_func( out, query.contiguous() if query.device.type == "xpu" else query, @@ -99,9 +122,19 @@ def paged_attention( True, block_tables, None, + window_size_left=window_size_left, + window_size_right=window_size_right, + kv_cache_dtype=kv_cache_dtype, + k_scale=kv_scales.key_scale_cpu, + v_scale=kv_scales.value_scale_cpu, + softcap=softcap, ) else: input_lengths = seqlen.input_lengths + seqlen.cache_lengths + if softcap is not None: + raise NotImplementedError( + "softcap is not available in IPEX paged attention" + ) ipex.llm.modules.PagedAttention.single_query_cached_kv_attention( out, query, @@ -114,6 +147,8 @@ def paged_attention( BLOCK_SIZE, max_s, None, + k_scale=kv_scales.key_scale_cpu, + v_scale=kv_scales.value_scale_cpu, ) return out diff --git a/server/text_generation_server/layers/attention/kv_cache.py b/server/text_generation_server/layers/attention/kv_cache.py index aaf4d2b2..a37ecd4c 100644 --- a/server/text_generation_server/layers/attention/kv_cache.py +++ b/server/text_generation_server/layers/attention/kv_cache.py @@ -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 @@ -207,8 +213,20 @@ class KVCache: elif ATTENTION == "flashdecoding-ipex" and key.device.type == "xpu": import intel_extension_for_pytorch as ipex + kv_cache_dtype = "auto" + if key_cache.dtype == torch.float8_e5m2: + kv_cache_dtype = "fp8_e5m2" + if key_cache.dtype == torch.float8_e4m3fn: + kv_cache_dtype = "fp8_e4m3" ipex.llm.modules.PagedAttention.reshape_and_cache_flash( - key, value, key_cache, value_cache, slots + key, + value, + key_cache, + value_cache, + slots, + kv_cache_dtype=kv_cache_dtype, + k_scale=kv_scales.key_scale_cpu, + v_scale=kv_scales.value_scale_cpu, ) else: paged_reshape_and_cache( @@ -267,8 +285,21 @@ def paged_reshape_and_cache( elif SYSTEM == "ipex": import intel_extension_for_pytorch as ipex + kv_cache_dtype = "auto" + if key_cache.dtype == torch.float8_e5m2: + kv_cache_dtype = "fp8_e5m2" + if key_cache.dtype == torch.float8_e4m3fn: + kv_cache_dtype = "fp8_e4m3" + ipex.llm.modules.PagedAttention.reshape_and_cache( - key, value, key_cache, value_cache, slots + key, + value, + key_cache, + value_cache, + slots, + kv_cache_dtype=kv_cache_dtype, + k_scale=k_scale, + v_scale=v_scale, ) else: raise NotImplementedError(