Merge pull request #1295 from rnwang04/xpu_support

Enable ktransformers on Intel GPU with local chat backend

README.md

@@ -23,6 +23,8 @@ Our vision for KTransformers is to serve as a flexible platform for experimentin
 
 <h2 id="Updates">🔥 Updates</h2>
 
+ **May 14, 2025**: Support Intel Arc GPU ([Tutorial](./doc/en/xpu.md)).
+
 * **Apr 29, 2025**: Support AMX-Int8、 AMX-BF16 and Qwen3MoE ([Tutorial](./doc/en/AMX.md))
 
 https://github.com/user-attachments/assets/fafe8aec-4e22-49a8-8553-59fb5c6b00a2

csrc/ktransformers_ext/CMakeLists.txt

@@ -41,6 +41,7 @@ option(LLAMA_AVX512_FANCY_SIMD               "llama: enable AVX512-VL, AVX512-BW
 option(KTRANSFORMERS_USE_CUDA                "ktransformers: use CUDA"                          ON)
 option(KTRANSFORMERS_USE_MUSA                "ktransformers: use MUSA"                          OFF)
 option(KTRANSFORMERS_USE_ROCM                "ktransformers: use ROCM"                          OFF)
+option(KTRANSFORMERS_USE_XPU                 "ktransformers: use XPU"                           OFF)
 
 # Architecture specific
 # TODO: probably these flags need to be tweaked on some architectures
@@ -303,6 +304,8 @@ elseif (UNIX)
             message(STATUS "MUSA Toolkit found")
             add_compile_definitions(KTRANSFORMERS_USE_MUSA=1)
         endif()
+    elseif (KTRANSFORMERS_USE_XPU)
+        add_compile_definitions(KTRANSFORMERS_USE_XPU=1)
     else()
         find_package(CUDA REQUIRED)
         include_directories("${CUDA_INCLUDE_DIRS}")
@@ -361,6 +364,7 @@ elseif(UNIX)
         message(STATUS "Building for HIP")
     elseif(KTRANSFORMERS_USE_MUSA)
         target_link_libraries(${PROJECT_NAME} PRIVATE MUSA::musart)
+    elseif(KTRANSFORMERS_USE_XPU)
     else()
         target_link_libraries(${PROJECT_NAME} PRIVATE "${CUDAToolkit_LIBRARY_DIR}/libcudart.so")
     endif()

csrc/ktransformers_ext/cpu_backend/cpuinfer.h

@@ -17,6 +17,7 @@
  #include <queue>
  #include <thread>
  #include <vector>
+ #include <stdexcept>
  #ifdef KTRANSFORMERS_USE_CUDA
  #include "vendors/cuda.h"
  #elif KTRANSFORMERS_USE_MUSA
@@ -66,10 +67,14 @@
      }
  
      void submit_with_cuda_stream(intptr_t user_cuda_stream, std::pair<intptr_t, intptr_t> params) {
+        #if defined(KTRANSFORMERS_USE_CUDA) || defined(KTRANSFORMERS_USE_MUSA) || defined(KTRANSFORMERS_USE_ROCM)
          void (*func)(void*) = (void (*)(void*))params.first;
          void* args = (void*)params.second;
          *((CPUInfer**)args) = this;
          cudaLaunchHostFunc((cudaStream_t)user_cuda_stream, (cudaHostFn_t)func, args);
+        #else
+         throw std::runtime_error("submit_with_cuda_stream is not supported on this platforma");
+        #endif
      }
  
      static void sync_(void* cpu_infer_ptr) {
@@ -78,7 +83,11 @@
      }
  
      void sync_with_cuda_stream(intptr_t user_cuda_stream) {
+        #if defined(KTRANSFORMERS_USE_CUDA) || defined(KTRANSFORMERS_USE_MUSA) || defined(KTRANSFORMERS_USE_ROCM)
          cudaLaunchHostFunc((cudaStream_t)user_cuda_stream, (cudaHostFn_t)&sync_, (void*)this);
+        #else
+         throw std::runtime_error("sync_with_cuda_stream is not supported on this platforma");
+        #endif
      }
  
     public:

csrc/ktransformers_ext/ext_bindings.cpp

@@ -9,7 +9,7 @@
  **/
 // Python bindings
 #include "cpu_backend/cpuinfer.h"
-#ifndef KTRANSFORMERS_USE_ROCM
+#if !defined(KTRANSFORMERS_USE_ROCM) && !defined(KTRANSFORMERS_USE_XPU)
 #include "device_launch_parameters.h"
 #endif
 #include "llamafile/flags.h"

doc/README.md

@@ -21,7 +21,7 @@ interface, RESTful APIs compliant with OpenAI and Ollama, and even a simplified
 Our vision for KTransformers is to serve as a flexible platform for experimenting with innovative LLM inference optimizations. Please let us know if you need any other features.
 
 <h2 id="Updates">🔥 Updates</h2>
-
+* **May 14, 2025**: Support Intel Arc GPU ([Tutorial](./en/xpu.md)).
 * **Apr 9, 2025**: Experimental support for LLaMA 4 models ([Tutorial](./en/llama4.md)).
 * **Apr 2, 2025**: Support Multi-concurrency. ([Tutorial](./en/balance-serve.md)).
 * **Mar 27, 2025**: Support Multi-concurrency.

doc/en/xpu.md

+# Intel GPU Support for KTransformers (Beta)
+
+## Introduction
+
+### Overview
+We are excited to introduce **Intel GPU support** in KTransformers (Beta release). This implementation has been tested and developed using Intel Xeon Scalable processors and Intel Arc GPU's (such as A770 and B580).
+
+## Installation Guide
+
+### 1. Install Intel GPU Driver
+Begin by installing the GPU drivers for your Intel GPU:
+- [Official GPU Installation Guide for Intel GPUs](https://dgpu-docs.intel.com/driver/overview.html)
+
+> [!Important]
+> Ensure that **Resizable BAR** is enabled in your system's BIOS before proceeding. This is essential for optimal GPU performance and to avoid potential issues such as `Bus error (core dumped)`. For detailed steps, please refer to the official guidance [here](https://www.intel.com/content/www/us/en/support/articles/000090831/graphics.html).
+
+### 2. Set Up Conda Environment
+We recommend using Miniconda3/Anaconda3 for environment management:
+
+```bash
+# Download Miniconda
+wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
+
+# Create environment
+conda create --name ktransformers python=3.11
+conda activate ktransformers
+
+# Install required libraries
+conda install -c conda-forge libstdcxx-ng
+
+# Verify GLIBCXX version (should include 3.4.32)
+strings ~/anaconda3/envs/ktransformers/lib/libstdc++.so.6 | grep GLIBCXX
+```
+
+> **Note:** Adjust the Anaconda path if your installation directory differs from `~/anaconda3`
+
+### 3. Install PyTorch and IPEX-LLM
+Install PyTorch with XPU backend support and [IPEX-LLM](https://github.com/intel/ipex-llm):
+
+```bash
+pip install --pre --upgrade ipex-llm[xpu_2.6] --extra-index-url https://download.pytorch.org/whl/xpu
+pip uninstall torch torchvision torchaudio
+pip install torch==2.7+xpu torchvision torchaudio --index-url https://download.pytorch.org/whl/test/xpu # install torch2.7
+pip install packaging ninja cpufeature numpy
+pip uninstall intel-opencl-rt dpcpp-cpp-rt
+```
+
+### 4. Build ktransformers
+
+```bash
+# Clone repository
+git clone https://github.com/kvcache-ai/ktransformers.git
+cd ktransformers
+git submodule update --init
+
+# Install dependencies
+bash install.sh
+pip uninstall triton pytorch-triton-xpu
+pip install pytorch-triton-xpu==3.3.0 --extra-index-url https://download.pytorch.org/whl/xpu # to avoid potential triton import error
+```
+
+## Running DeepSeek-R1 Models
+
+### Configuration for 16B VRAM GPUs
+Use our optimized configuration for constrained VRAM:
+
+```bash
+export SYCL_CACHE_PERSISTENT=1
+export ONEAPI_DEVICE_SELECTOR=level_zero:0
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+
+python ktransformers/local_chat.py \
+  --model_path deepseek-ai/DeepSeek-R1 \
+  --gguf_path <path_to_gguf_files> \
+  --optimize_config_path ktransformers/optimize/optimize_rules/xpu/DeepSeek-V3-Chat.yaml \
+  --cpu_infer <cpu_cores + 1> \
+  --device xpu \
+  --max_new_tokens 200
+```
+
+## Known Limitations
+- Serving function is not supported on Intel GPU platform for now
+
+## Troubleshooting
+1. Best Known Config (BKC) to obtain best performance
+
+To obtain best performance on Intel GPU platform, we recommand to lock GPU frequency and set CPU to performance mode by below settings.
+```bash
+echo "performance" | sudo tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
+echo 0 | sudo tee /sys/devices/system/cpu/cpu*/power/energy_perf_bias
+# 2400 is max frequency for Arc A770
+sudo xpu-smi config -d 0 -t 0 --frequencyrange 2400,2400
+# 2850 is max frequency for Arc B580
+# sudo xpu-smi config -d 0 -t 0 --frequencyrange 2850,2850
+```
+
+2. Runtime error like `xpu/sycl/TensorCompareKernels.cpp:163: xxx. Aborted (core dumped)`
+
+This error is mostly realted to GPU driver. If you meet such error, you could update your `intel-level-zero-gpu` to `1.3.29735.27-914~22.04` (which is a verified version by us) by below command.
+```bash
+wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | \
+sudo gpg --dearmor --output /usr/share/keyrings/intel-graphics.gpg
+echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" | \
+sudo tee /etc/apt/sources.list.d/intel-gpu-jammy.list
+sudo apt update
+# or sudo apt update --allow-insecure-repositories
+sudo apt install intel-level-zero-gpu=1.3.29735.27-914~22.04
+```
+
+3. `ImportError: cannot import name 'intel' from 'triton._C.libtriton'`
+
+Installing Triton causes pytorch-triton-xpu to stop working. You can resolve the issue with following command:
+```bash
+pip uninstall triton pytorch-triton-xpu
+# Reinstall correct version of pytorch-triton-xpu
+pip install pytorch-triton-xpu==3.3.0 --index-url  https://download.pytorch.org/whl/xpu
+```
\ No newline at end of file

ktransformers/local_chat.py

@@ -63,18 +63,23 @@ def local_chat(
     prompt_file : str | None = None,
     mode: str = "normal",
     force_think: bool = False,
-    chunk_size: int = 8192
+    chunk_size: int = 8192,
+    device: str = "cuda"
 ):
 
     torch.set_grad_enabled(False)
 
     Config().cpu_infer = cpu_infer
+    if torch.xpu.is_available():
+        use_cuda_graph = False
 
     tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
     config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
     if mode == 'long_context':
         assert config.architectures[0] == "LlamaForCausalLM", "only LlamaForCausalLM support long_context mode"
         torch.set_default_dtype(torch.float16)
+    elif torch.xpu.is_available() and config.architectures[0] == "DeepseekV3ForCausalLM":
+        torch.set_default_dtype(torch.float16)
     else:
         torch.set_default_dtype(config.torch_dtype)
 
@@ -109,7 +114,7 @@ def local_chat(
         gguf_path = input(
             "please input the path of your gguf file(gguf file in the dir containing input gguf file must all belong to current model):"
         )
-    optimize_and_load_gguf(model, optimize_config_path, gguf_path, config)
+    optimize_and_load_gguf(model, optimize_config_path, gguf_path, config, default_device=device)
     
     try:
         model.generation_config = GenerationConfig.from_pretrained(model_path)
@@ -172,12 +177,12 @@ def local_chat(
         
         if system != "Windows" and (config.architectures[0] == "DeepseekV2ForCausalLM" or config.architectures[0] == "DeepseekV3ForCausalLM") and flashinfer_enabled and get_compute_capability() >= 8 and device_manager.gpu_vendor == GPUVendor.NVIDIA:
             generated = prefill_and_generate(
-                model, tokenizer, input_tensor.cuda(), max_new_tokens, use_cuda_graph, mode = mode, force_think = force_think, chunk_size = chunk_size,
+                model, tokenizer, input_tensor.to(device), max_new_tokens, use_cuda_graph, mode = mode, force_think = force_think, chunk_size = chunk_size,
                 use_flashinfer_mla = True, num_heads = config.num_attention_heads, head_dim_ckv = config.kv_lora_rank, head_dim_kpe = config.qk_rope_head_dim, q_head_dim = config.qk_rope_head_dim + config.qk_nope_head_dim
             )
         else:
             generated = prefill_and_generate(
-                model, tokenizer, input_tensor.cuda(), max_new_tokens, use_cuda_graph, mode = mode, force_think = force_think, chunk_size = chunk_size,
+                model, tokenizer, input_tensor.to(device), max_new_tokens, use_cuda_graph, mode = mode, force_think = force_think, chunk_size = chunk_size,
             )
 
 

ktransformers/models/custom_cache.py

@@ -293,7 +293,7 @@ class KGQACache(nn.Module):
         self.v_caches = []
         
 
-    def load(self, inference_context: sched_ext.InferenceContext): 
+    def load(self, inference_context: "sched_ext.InferenceContext"): 
         print(self.config.num_hidden_layers)
         for i in range(self.config.num_hidden_layers):
             self.k_caches.append(

ktransformers/models/modeling_deepseek.py

@@ -107,6 +107,7 @@ class DeepseekV2RMSNorm(nn.Module):
         super().__init__()
         self.weight = nn.Parameter(torch.ones(hidden_size))
         self.variance_epsilon = eps
+        self.hidden_size = hidden_size
 
     def forward(self, hidden_states):
         input_dtype = hidden_states.dtype

ktransformers/operators/attention.py

@@ -587,6 +587,100 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
                 
         return attn_output, None, past_key_value
 
+    def forward_xpu(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+        bsz, q_len, _ = hidden_states.size()
+
+        if self.q_lora_rank is None:
+            q = self.q_proj(hidden_states)
+        else:
+            q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
+        query_states = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
+
+        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
+        compressed_kv, k_pe = torch.split(
+            compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
+        )
+        k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
+        kv = (
+            self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
+            .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
+            .transpose(1, 2)
+        )
+
+        k_nope, value_states = torch.split(
+            kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1
+        )
+        kv_seq_len = value_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+
+        position_embeddings = kwargs.get("position_embeddings", None)
+        if position_embeddings is not None:
+            cos, sin = position_embeddings
+            key_states = torch.cat(
+                [k_nope, k_pe.expand([-1, self.num_heads, -1, -1])],
+                dim=-1
+            )
+            from ipex_llm.transformers.models.common import rotary_two_with_cache_inplaced
+            rotary_two_with_cache_inplaced(query_states[:, :, :, self.qk_nope_head_dim :],
+                                           key_states[:, :, :, self.qk_nope_head_dim:],
+                                           cos, sin, True)
+        else:
+            q_nope, q_pe = torch.split(
+                query_states, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
+            )
+            cos, sin = self.rotary_emb(q_pe, position_ids)
+            q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin)
+            query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
+            query_states[:, :, :, : self.qk_nope_head_dim] = q_nope
+            query_states[:, :, :, self.qk_nope_head_dim :] = q_pe
+
+            key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
+            key_states[:, :, :, : self.qk_nope_head_dim] = k_nope
+            key_states[:, :, :, self.qk_nope_head_dim :] = k_pe
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(
+                key_states.half(), value_states.half(), self.layer_idx, cache_kwargs
+            )
+
+        attn_weights = None
+        from ipex_llm.transformers.models.common import scaled_dot_product_attention
+        attn_output = scaled_dot_product_attention(
+            query_states.half(), key_states, value_states,
+            attention_mask.half(), q_len == kv_seq_len, self.softmax_scale
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim)
+        attn_output = self.o_proj(attn_output).to(hidden_states.dtype)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -598,7 +692,18 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
         cache_position: Optional[torch.LongTensor] = None,
         **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        if (os.name == 'nt'
+        if torch.xpu.is_available():
+            return self.forward_xpu(
+                hidden_states,
+                attention_mask,
+                position_ids,
+                past_key_value,
+                output_attentions,
+                use_cache,
+                cache_position,
+                **kwargs,
+            )
+        elif (os.name == 'nt'
               or get_compute_capability() < 8
               or hidden_states.device.type == 'cpu'
               or device_manager.gpu_vendor != GPUVendor.NVIDIA):

ktransformers/operators/experts.py

@@ -51,7 +51,10 @@ def generate_cuda_graphs(chunk_size: int) -> list:
 
     return deduplicate_and_sort(base_list + multiples)
 #cuda_graphs = [Config().chunk_size] 
-cuda_graphs = generate_cuda_graphs(Config().chunk_size)
+if torch.cuda.is_available():
+    cuda_graphs = generate_cuda_graphs(Config().chunk_size)
+else:
+    cuda_graphs = 1
 # class Base(BaseInjectedModule, ABC):
 class KExpertsBase(ABC):
     def __init__(self, key: str, gguf_loader: GGUFLoader, config: PretrainedConfig, orig_module: nn.Module, device: str = "cuda", **kwargs):
@@ -177,6 +180,11 @@ class KExpertsCPU(KExpertsBase):
         n_routed_experts = self.n_routed_experts
         self.cpu_infer = KExpertsCPU.CPU_INFER
         # n_routed_experts = len(self.orig_module)
+        model_dtype = torch.get_default_dtype()
+        if torch.xpu.is_available() and model_dtype == torch.float16:
+            hidden_type = 1 # fp16
+        else:
+            hidden_type = 30 # bf16
         if self.backend == "llamafile":
             moe_config = MOEConfig(
                 n_routed_experts,
@@ -192,7 +200,7 @@ class KExpertsCPU(KExpertsBase):
                 self.gate_type,
                 self.up_type,
                 self.down_type,
-                30, # TODO: get from model.dtype
+                hidden_type, # TODO: get from model.dtype
             )
             self.moe = MOE(moe_config)
         elif self.backend == "AMXBF16":
@@ -252,6 +260,10 @@ class KExpertsCPU(KExpertsBase):
                 KExpertsCPU.input_tensor_cpu = torch.zeros((cuda_graphs, self.config.hidden_size), device="cpu", pin_memory=True)
                 KExpertsCPU.expert_ids_cpu = torch.zeros((cuda_graphs, num_experts_per_tok), device="cpu", dtype=torch.long, pin_memory=True)
                 KExpertsCPU.weights_cpu = torch.zeros((cuda_graphs, num_experts_per_tok), device="cpu", dtype=torch.float32, pin_memory=True)
+                if torch.xpu.is_available():
+                    KExpertsCPU.output_cpu = torch.zeros((cuda_graphs, self.config.hidden_size), device="cpu", pin_memory=True, dtype=model_dtype)
+                    KExpertsCPU.bsz_tensor_cpu = torch.ones((1), device="cpu", dtype=torch.int32, pin_memory=True)
+                else:
                     KExpertsCPU.output_cpu = torch.zeros((cuda_graphs, self.config.hidden_size), device="cpu", pin_memory=True, dtype=torch.bfloat16)
                     KExpertsCPU.bsz_tensor_cpu = torch.zeros((1), device="cpu", dtype=torch.int32, pin_memory=True)
             
@@ -285,9 +297,9 @@ class KExpertsCPU(KExpertsBase):
     def forward(self, input_tensor, expert_ids, weights, bsz_tensor=None, cuda_graph_idx=0):
         # generate, capture and run cuda graph
         # print(expert_ids)
-        if bsz_tensor is None:
+        if bsz_tensor is None and (not torch.xpu.is_available() or input_tensor.size(0) > 1):
             bsz_tensor = torch.tensor([input_tensor.size(0)], device=input_tensor.device, dtype=torch.int32)
-        if torch.cuda.is_current_stream_capturing():
+        if torch.cuda.is_available() and torch.cuda.is_current_stream_capturing():
             if cuda_graph_idx != -1:
                 KExpertsCPU.input_tensor_cpu[cuda_graph_idx].copy_(input_tensor, non_blocking=True)
                 KExpertsCPU.expert_ids_cpu[cuda_graph_idx].copy_(expert_ids, non_blocking=True)
@@ -307,6 +319,15 @@ class KExpertsCPU(KExpertsBase):
                 self.cpu_infer.sync_with_cuda_stream(torch.cuda.current_stream().cuda_stream)
                 KExpertsCPU.output_gpu_map[self.out_device].copy_(KExpertsCPU.output_cpu, non_blocking=True)
                 return KExpertsCPU.output_gpu_map[self.out_device]
+        elif input_tensor.size(0)==1 and torch.xpu.is_available():
+            KExpertsCPU.input_tensor_cpu.copy_(input_tensor.view(-1), non_blocking=True)
+            KExpertsCPU.expert_ids_cpu.copy_(expert_ids.view(-1), non_blocking=True)
+            KExpertsCPU.weights_cpu.copy_(weights.view(-1), non_blocking=True)
+            # KExpertsCPU.bsz_tensor_cpu.copy_(bsz_tensor.view(-1), non_blocking=True)
+            self.cpu_infer.submit(self.moe.forward(expert_ids.size(0), expert_ids.size(1), KExpertsCPU.expert_ids_cpu.data_ptr(), KExpertsCPU.weights_cpu.data_ptr(), KExpertsCPU.input_tensor_cpu.data_ptr(), KExpertsCPU.output_cpu.data_ptr(), KExpertsCPU.bsz_tensor_cpu.data_ptr()))
+            self.cpu_infer.sync()
+            KExpertsCPU.output_gpu_map[self.out_device].copy_(KExpertsCPU.output_cpu, non_blocking=True)
+            return KExpertsCPU.output_gpu_map[self.out_device].view(1, -1)
         else:
             input_tensor = input_tensor.contiguous().cpu()
             expert_ids = expert_ids.contiguous().cpu()
@@ -822,7 +843,7 @@ class KDeepseekV2MoE(BaseInjectedModule, DeepseekV2MoE):
         topk_idx, topk_weight, aux_loss = self.gate(hidden_states)
         hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
         
-        if sequence_length == 1 and hasattr(self.experts.generate_experts, "submit_for_one_decode") and torch.cuda.is_current_stream_capturing():
+        if sequence_length == 1 and hasattr(self.experts.generate_experts, "submit_for_one_decode") and torch.cuda.is_available() and torch.cuda.is_current_stream_capturing():
             self.experts.generate_experts.submit_for_one_decode(hidden_states[0], topk_idx[0], topk_weight[0])
             if self.config.n_shared_experts is not None:
                 y_ = self.shared_experts(identity).squeeze(0)
@@ -922,7 +943,7 @@ class KDeepseekV3MoE(BaseInjectedModule, DeepseekV3MoE):
         hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
         
         # only for generate phase
-        if sequence_length == 1 and hasattr(self.experts.generate_experts, "submit_for_one_decode") and torch.cuda.is_current_stream_capturing():
+        if sequence_length == 1 and hasattr(self.experts.generate_experts, "submit_for_one_decode") and torch.cuda.is_available() and torch.cuda.is_current_stream_capturing():
             self.experts.generate_experts.submit_for_one_decode(hidden_states[0], topk_idx[0], topk_weight[0])
             if self.config.n_shared_experts is not None:
                 y_ = self.shared_experts(identity).squeeze(0)
@@ -1122,7 +1143,7 @@ class KDeepseekV3MoEV2(BaseInjectedModule, DeepseekV3MoE):
         
 
         # only for generate phase
-        if hasattr(self.experts.generate_experts, "submit_for_one_decode") and torch.cuda.is_current_stream_capturing(): # TODO: this branch cause jit bug
+        if hasattr(self.experts.generate_experts, "submit_for_one_decode") and torch.cuda.is_available() and torch.cuda.is_current_stream_capturing(): # TODO: this branch cause jit bug
             self.experts.generate_experts.submit_for_one_decode(hidden_states, topk_idx, topk_weight, bsz_tensor, cuda_graph_idx)
             if self.config.n_shared_experts is not None:
                 y_ = self.shared_experts(identity, bsz_tensor).squeeze(0)
@@ -1304,7 +1325,7 @@ class KQwen2MoeSparseMoeBlockV2(BaseInjectedModule, Qwen2MoeSparseMoeBlock):
         routing_weights = routing_weights.to(hidden_states.dtype)
 
         # only for generate phase
-        if hasattr(self.experts.generate_experts, "submit_for_one_decode") and torch.cuda.is_current_stream_capturing(): # TODO: this branch cause jit bug
+        if hasattr(self.experts.generate_experts, "submit_for_one_decode") and torch.cuda.is_available() and torch.cuda.is_current_stream_capturing(): # TODO: this branch cause jit bug
             self.experts.generate_experts.submit_for_one_decode(hidden_states, selected_experts, routing_weights, bsz_tensor, cuda_graph_idx)
             y_ = self.shared_expert(hidden_states, bsz_tensor).squeeze(0)
             y_ = F.sigmoid(self.shared_expert_gate(hidden_states)) * y_    
@@ -1417,7 +1438,7 @@ class KQwen3MoeSparseMoeBlockV2(BaseInjectedModule, Qwen3MoeSparseMoeBlock):
         routing_weights = routing_weights.to(hidden_states.dtype)
 
         # only for generate phase
-        if hasattr(self.experts.generate_experts, "submit_for_one_decode") and torch.cuda.is_current_stream_capturing(): # TODO: this branch cause jit bug
+        if hasattr(self.experts.generate_experts, "submit_for_one_decode") and torch.cuda.is_available() and torch.cuda.is_current_stream_capturing(): # TODO: this branch cause jit bug
             self.experts.generate_experts.submit_for_one_decode(hidden_states, selected_experts, routing_weights, bsz_tensor, cuda_graph_idx)
             # y_ = self.shared_expert(hidden_states, bsz_tensor).squeeze(0)
             # y_ = F.sigmoid(self.shared_expert_gate(hidden_states)) * y_    

ktransformers/operators/gate.py

@@ -183,3 +183,33 @@ class KMoEGateQwen2Moe(BaseInjectedModule, KMoEGateBase):
             self.weight = None
         if self.e_score_correction_bias is not None:
             self.e_score_correction_bias = None
+
+
+class KMoEGateIPEXLLM(KMoEGate):
+    def __init__(
+        self,
+        key: str,
+        gguf_loader: GGUFLoader,
+        config: PretrainedConfig,
+        orig_module: nn.Module = None,
+        generate_device: str = "xpu",
+        prefill_device: str = "xpu",
+        **kwargs,
+    ):
+        BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, generate_device, **kwargs)
+        KMoEGate.__init__(self, key, gguf_loader, config, orig_module, generate_device, **kwargs)
+        self.generate_device = generate_device
+        self.prefill_device = prefill_device
+
+    def forward(self, hidden_states) -> torch.Tensor:
+        x = hidden_states.view(-1, hidden_states.size(-1))
+        logits = torch.nn.functional.linear(
+            x.type(torch.float32), self.orig_module.weight.type(torch.float32), None
+        )
+        scores = logits.sigmoid()
+
+        from ipex_llm.transformers.models.common import moe_group_topk
+        topk_idx, topk_weight = moe_group_topk(scores, self.orig_module.e_score_correction_bias,
+                                               self.n_group, self.topk_group, self.top_k,
+                                               self.norm_topk_prob, self.routed_scaling_factor)
+        return topk_idx, topk_weight.to(x.dtype)
\ No newline at end of file

ktransformers/operators/layernorm.py

@@ -30,10 +30,11 @@ from ktransformers.models.modeling_qwen2_moe import Qwen2MoeRMSNorm
 from ktransformers.models.modeling_qwen3_moe import Qwen3MoeRMSNorm
 from ktransformers.operators.base_operator import BaseInjectedModule
 from ktransformers.util.custom_loader import GGUFLoader
-from flashinfer.norm import (
+if not torch.xpu.is_available():
+    from flashinfer.norm import (
         fused_add_rmsnorm,
         rmsnorm,
-)
+    )
 
 
 logger = logging.getLogger(__name__)
@@ -194,3 +195,28 @@ class DeepseekV3RMSNormTorch(DeepseekV3RMSNorm, BaseInjectedModule):
         if residual is not None:
             return self.weight * x.to(input_dtype), residual
         return self.weight * x.to(input_dtype)
+
+
+class KDeepseekRMSNormIPEXLLM(DeepseekV3RMSNorm, BaseInjectedModule):
+    def __init__(self,
+                 key: str,
+                 gguf_loader : GGUFLoader,
+                 config: PretrainedConfig,
+                 orig_module: nn.Module,
+                 prefill_device: str = "xpu",
+                 generate_device: str = "xpu",
+                 **kwargs):
+        BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
+        self.orig_module.__init__(orig_module.hidden_size,
+            orig_module.variance_epsilon)
+        self.eps = orig_module.variance_epsilon
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        from ipex_llm.transformers.models.common import rms_norm_forward
+        output = rms_norm_forward(self, x.float())
+        return output.to(x.dtype)
+
+    def load(self):
+        BaseInjectedModule.load(self)
+        if self.weight.dtype != torch.float32:
+            self.weight = self.weight.float()
\ No newline at end of file

ktransformers/operators/linear.py

@@ -14,18 +14,20 @@ Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
 import ctypes
 import torch
 from torch import Tensor, nn
-import KTransformersOps 
-import vLLMMarlin
+if not torch.xpu.is_available():
+    import KTransformersOps
+    import vLLMMarlin
 from ktransformers.util.custom_loader import GGUFLoader, SafeTensorLoader
 from ktransformers.util.utils import InferenceState
-from ktransformers.ktransformers_ext.operators.custom_marlin.quantize.utils.marlin_utils import (
+if not torch.xpu.is_available():
+    from ktransformers.ktransformers_ext.operators.custom_marlin.quantize.utils.marlin_utils import (
         MarlinWorkspace,
         marlin_quantize,
         GPTQ_MARLIN_MIN_THREAD_N,
         GPTQ_MARLIN_MIN_THREAD_K,
         GPTQ_MARLIN_MAX_PARALLEL,
         vllm_marlin_quantize
-)
+    )
 from ktransformers.operators.base_operator import BaseInjectedModule
 from transformers.configuration_utils import PretrainedConfig
 from ktransformers.ktransformers_ext.triton.fp8gemm import fp8_gemm, act_quant, weight_dequant
@@ -778,6 +780,75 @@ class KLinearCPUInfer(KLinearBase):
         if self.has_bias:
             self.bias = None       
 
+class KLinearIPEXLLM(KLinearBase):
+    def __init__(
+        self,
+        key: str,
+        gguf_loader: GGUFLoader,
+        config: PretrainedConfig,
+        orig_module: nn.Module = None,
+        device: str = "xpu",
+        precision: str = "sym_int4",
+        **kwargs,
+    ):
+        super().__init__(key, gguf_loader, config, orig_module, device, **kwargs)
+        self.has_bias = False
+        self.dtype = torch.get_default_dtype()
+        self.weight = None
+        self.has_bias = False
+        self.precision = precision
+        self.qtype = None
+
+    def forward(self, x: torch.Tensor, bsz_tensor: torch.Tensor = None) -> torch.Tensor:
+        dtype = x.dtype
+        out_device = x.device
+        from ipex_llm.transformers.models.common import linear_forward
+        x = linear_forward(x.half(), self.weight, self.qtype, self.out_features)
+
+        if self.has_bias:
+            x = x + self.bias
+        x = x.to(dtype=dtype, device=out_device)
+        return x
+
+    def load(self, w: dict | nn.Parameter | tuple | None = None, device: str|None = None):
+        if self.loaded: return
+        if device is None: device = self.device
+        assert device.lower()[:3] == "xpu", "IPEX-LLM quantized linear only supports XPU device"
+        if w is None: w = self.load_weight(device=device)
+
+        if isinstance(w, nn.Parameter):
+            try:
+                weight = w.to(dtype=self.dtype).view(self.out_features, self.in_features).T
+            except:
+                weight = w.to(dtype=self.dtype).T
+            self.has_bias = False
+        elif isinstance(w, tuple):
+            try:
+                weight = w[0].to(dtype=self.dtype).view(self.out_features, self.in_features).T
+            except:
+                weight = w[0].to(dtype=self.dtype).T
+            self.bias = w[1].to(dtype=self.dtype)
+            self.has_bias = True
+        else:
+            raise ValueError("Invalid weight type")
+        weight = weight.to("cpu").float().transpose(0, 1).contiguous()
+
+        if self.has_bias:
+            self.bias = self.bias.to(device)
+
+        # quantize linear weight
+        from ipex_llm.transformers.models.common import quantize_linear
+        paramsLowBit, qtype = quantize_linear(weight, self.in_features, self.precision)
+        self.weight = paramsLowBit.to(device)
+        self.qtype = qtype
+        self.loaded = True
+
+    def unload(self):
+        if self.weight is not None:
+            self.weight = None
+        if self.has_bias:
+            self.bias = None
+
 LINEAR_MAP = {
     "KLinearMarlin": KLinearMarlin,
     "KLinearTorch": KLinearTorch,
@@ -785,6 +856,7 @@ LINEAR_MAP = {
     "VLinearMarlin": VLinearMarlin,
     "KLinearFP8": KLinearFP8,
     "KLinearQ8": KLinearQ8,
+    "KLinearIPEXLLM": KLinearIPEXLLM,
 }
 
 class KTransformersLinear(BaseInjectedModule, KLinearBase):

ktransformers/operators/models.py

@@ -647,6 +647,13 @@ class KDeepseekV2Model(BaseInjectedModule):
         if position_ids is None:
             position_ids = cache_position.unsqueeze(0)
 
+        if inputs_embeds.device.type == "xpu" and position_ids is not None:
+            cos, sin = self.layers[0].self_attn.rotary_emb(inputs_embeds,
+                                                           position_ids)
+            position_embeddings = (cos, sin)
+        else:
+            position_embeddings = None
+
         if per_layer_prefill_flag:
             causal_mask = None
         else:
@@ -737,6 +744,7 @@ class KDeepseekV2Model(BaseInjectedModule):
                     output_attentions=output_attentions,
                     use_cache=use_cache,
                     cache_position=cache_position,
+                    position_embeddings=position_embeddings,
                 )
                 t5 = time.time()
                 if per_layer_prefill_flag:

ktransformers/optimize/optimize.py

@@ -103,7 +103,7 @@ def gen_optimize_config(module: nn.Module, out_data: Mapping, rule_list: List, p
         for name, child in module._modules.items():
             if child is not None:
                 child_prefix = prefix + name + "."
-                gen_optimize_config(child, out_data, rule_list, child_prefix)
+                gen_optimize_config(child, out_data, rule_list, child_prefix, default_device = default_device)
     
 
 def translate_model_config(model_config: PretrainedConfig):
@@ -127,8 +127,11 @@ def optimize_and_load_gguf(module: nn.Module, rule_file: str, gguf_path: str, mo
     with torch.device("meta"):
         inject(module, optimize_config, model_config, weights_loader)
     # pre load lm_head because its big inter result
-    load_weights(module.lm_head, weights_loader, "lm_head.")
-    load_weights(module, weights_loader)
+    load_weights(module.lm_head, weights_loader, "lm_head.", device=default_device)
+    load_weights(module, weights_loader, device=default_device)
     module.gguf_loader = weights_loader
     del_meta(module)
+    if torch.cuda.is_available():
         torch.cuda.empty_cache()
+    elif torch.xpu.is_available():
+        torch.xpu.empty_cache()

ktransformers/optimize/optimize_rules/xpu/DeepSeek-V2-Chat.yaml

+- match:
+    class: ktransformers.models.modeling_deepseek.DeepseekV2YarnRotaryEmbedding
+  replace:
+    class: ktransformers.operators.RoPE.YarnRotaryEmbedding
+    kwargs:
+      generate_device: "xpu"
+      prefill_device: "xpu"
+- match:
+    name: "^model\\.layers\\..*"  # regular expression
+    class: torch.nn.Linear  # only match modules matching name and class simultaneously
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
+    kwargs:
+      generate_device: "xpu"
+      prefill_device: "xpu"
+      generate_op: "KLinearIPEXLLM"
+      prefill_op: "KLinearIPEXLLM"
+- match:
+    name: "^model\\.layers\\..*\\.mlp$"
+    class: ktransformers.models.modeling_deepseek.DeepseekV2MoE
+  replace:
+    class: ktransformers.operators.experts.KDeepseekV2MoE     # mlp module with custom forward function
+    kwargs:
+      generate_device: "xpu"
+      prefill_device: "xpu"
+- match:
+    name: "^model\\.layers\\..*\\.mlp\\.experts$"
+  replace:
+    class: ktransformers.operators.experts.KTransformersExperts     # custom MoE Kernel with expert paralleism
+    kwargs:
+      prefill_device: "xpu"
+      prefill_op: "KExpertsTorch"
+      generate_device: "cpu"
+      generate_op: "KExpertsCPU"
+      out_device: "xpu"
+  recursive: False # don't recursively inject submodules of this module
+- match:
+    class: ktransformers.models.modeling_deepseek.DeepseekV2RMSNorm
+  replace:
+    class: ktransformers.operators.layernorm.KDeepseekRMSNormIPEXLLM
+    kwargs:
+      generate_device: "xpu"
+      prefill_device: "xpu"
+- match:
+    name: "^model\\.layers\\..*\\.self_attn$"
+  replace:
+    class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
+    kwargs:
+      generate_device: "xpu"
+      prefill_device: "xpu"
+- match:
+    name: "^model$"
+  replace:
+    class: "ktransformers.operators.models.KDeepseekV2Model"
+    kwargs:
+      per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill
+      device: "xpu"
+- match:
+    name: "^model.embed_tokens"
+  replace:
+    class: "default"
+    kwargs:
+      generate_device: "cpu"
+      prefill_device: "cpu"
\ No newline at end of file

ktransformers/optimize/optimize_rules/xpu/DeepSeek-V3-Chat.yaml

+- match:
+    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
+  replace:
+    class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
+    kwargs:
+      generate_device: "xpu"
+      prefill_device: "xpu"
+- match:
+    name: "^lm_head$"  # regular expression
+    class: torch.nn.Linear  # only match modules matching name and class simultaneously
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
+    kwargs:
+      generate_device: "xpu"
+      prefill_device: "xpu"
+      generate_op: "KLinearIPEXLLM"
+      prefill_op: "KLinearIPEXLLM"
+- match:
+    name: "^model\\.layers\\..*"  # regular expression
+    class: torch.nn.Linear  # only match modules matching name and class simultaneously
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
+    kwargs:
+      generate_device: "xpu"
+      prefill_device: "xpu"
+      generate_op: "KLinearIPEXLLM"
+      prefill_op: "KLinearIPEXLLM"
+- match:
+    name: "^model\\.layers\\..*\\.mlp$"
+    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
+  replace:
+    class: ktransformers.operators.experts.KDeepseekV3MoE     # mlp module with custom forward function
+    kwargs:
+      generate_device: "xpu"
+      prefill_device: "xpu"
+- match:
+    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RMSNorm
+  replace:
+    class: ktransformers.operators.layernorm.KDeepseekRMSNormIPEXLLM
+    kwargs:
+      generate_device: "xpu"
+      prefill_device: "xpu"
+- match:
+    class: ktransformers.models.modeling_deepseek_v3.MoEGate
+  replace:
+    class: ktransformers.operators.gate.KMoEGateIPEXLLM
+    kwargs:
+      generate_device: "xpu:0"
+      prefill_device: "xpu:0"
+- match:
+    name: "^model\\.layers\\..*\\.mlp\\.experts$"
+  replace:
+    class: ktransformers.operators.experts.KTransformersExperts     # custom MoE Kernel with expert paralleism
+    kwargs:
+      prefill_device: "xpu"
+      prefill_op: "KExpertsTorch"
+      generate_device: "cpu"
+      generate_op: "KExpertsCPU"
+      out_device: "xpu"
+  recursive: False # don't recursively inject submodules of this module
+- match:
+    name: "^model\\.layers\\..*\\.self_attn$"
+  replace:
+    class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
+    kwargs:
+      generate_device: "xpu"
+      prefill_device: "xpu"
+      absorb_for_prefill: False # change this to True to enable long context(prefill may slower).
+- match:
+    name: "^model$"
+  replace:
+    class: "ktransformers.operators.models.KDeepseekV2Model"
+    kwargs:
+      per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill
+- match:
+    name: "^model.embed_tokens"
+  replace:
+    class: "default"
+    kwargs:
+      generate_device: "cpu"
+      prefill_device: "cpu"
\ No newline at end of file

ktransformers/util/custom_gguf.py

@@ -24,7 +24,8 @@ from typing import Sequence
 import os
 from enum import IntEnum
 import torch
-import KTransformersOps
+if not torch.xpu.is_available():
+    import KTransformersOps
 import ctypes
 import math
 

ktransformers/util/custom_loader.py

@@ -7,7 +7,8 @@ from typing import Sequence
 import os
 from enum import IntEnum
 import torch
-import KTransformersOps
+if not torch.xpu.is_available():
+    import KTransformersOps
 from safetensors import safe_open
 from ktransformers.ktransformers_ext.triton.fp8gemm import fp8_gemm, act_quant, weight_dequant
 from ktransformers.util.custom_gguf import *
@@ -459,7 +460,7 @@ class GGUFLoader(ModelLoader):
                 values = GGML_DEQUANTIZE_GPU[ggml_name](data, device)
             else:
                 values = GGML_DEQUANTIZE[ggml_name](data)
-                values = torch.from_numpy(values)
+                values = torch.from_numpy(values).to(device)
                 
         if ggml_name == "BF16":
             values = values.view(torch.bfloat16)