Add support HYGON DCU

install_dcu.sh

+#!/bin/bash
+set -e  
+
+# clear build dirs
+rm -rf build
+rm -rf *.egg-info
+rm -rf ktransformers/ktransformers_ext/build
+rm -rf ktransformers/ktransformers_ext/cuda/build
+rm -rf ktransformers/ktransformers_ext/cuda/dist
+rm -rf ktransformers/ktransformers_ext/cuda/*.egg-info
+
+echo "Installing python dependencies from requirements.txt"
+pip install -r requirements-local_chat.txt
+
+export USE_FASTPT_CUDA=True
+export CMAKE_BUILD_PARALLEL_LEVEL=32
+
+echo "Installing ktransformers"
+KTRANSFORMERS_FORCE_BUILD=TRUE pip install . --no-build-isolation
+echo "Installation completed successfully"

ktransformers/ktransformers_ext/cuda/gptq_marlin/gptq_marlin_dtypes.cuh

@@ -55,7 +55,7 @@ class ScalarType<nv_bfloat16> {
   using FragC = Vec<float, 4>;
   using FragS = Vec<nv_bfloat162, 1>;
 
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800 || KTRANSFORMERS_USE_DTK
   static __device__ float inline num2float(const nv_bfloat16 x) {
     return __bfloat162float(x);
   }

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

+- match:
+    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
+  replace:
+    class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+
+- match:
+    name: "^lm_head$"  # regular expression 
+    class: torch.nn.Linear  # only match modules matching name and class simultaneously
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+      generate_op: "KLinearTorch"
+      prefill_op: "KLinearTorch"
+
+- match:
+    name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$"  # 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: "cuda"
+      prefill_device: "cuda"
+      generate_op: "KLinearTorch"
+      prefill_op: "KLinearTorch"
+- match:
+    name: "^model\\.layers\\..*\\.mlp$"
+    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
+  replace:
+    class: ktransformers.operators.experts.KDeepseekV3MoE     # mlp module with custom forward function
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+- match:
+    class: ktransformers.models.modeling_deepseek_v3.MoEGate
+  replace:
+    class: ktransformers.operators.gate.KMoEGate
+    kwargs:
+      generate_device: "cuda:0"
+      prefill_device: "cuda:0"
+- match:
+    name: "^model\\.layers\\..*\\.mlp\\.experts$"
+  replace:
+    class: ktransformers.operators.experts.KTransformersExperts     # custom MoE Kernel with expert paralleism
+    kwargs:
+      prefill_device: "cuda"
+      prefill_op: "KExpertsTorch"
+      generate_device: "cpu"
+      generate_op: "KExpertsCPU"
+      out_device: "cuda"
+  recursive: False # don't recursively inject submodules of this module
+- match:
+    name: "^model\\.layers\\..*\\.self_attn$"
+  replace:
+    class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+      absorb_for_prefill: False # change this to True to enable long context(prefill may slower).
+- match:
+    name: "^model$"
+  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"

setup.py

@@ -328,22 +328,29 @@ class CMakeBuild(BuildExtension):
             ["cmake", "--build", ".", "--verbose", *build_args], cwd=build_temp, check=True
         )
 
+USE_FASTPT_CUDA = os.getenv('USE_FASTPT_CUDA', 'False').lower() == 'true'
 if CUDA_HOME is not None:
-    ops_module = CUDAExtension('KTransformersOps', [
+    extra_nvcc_flags = [
+        '-O3',
+        '--use_fast_math',
+        '-Xcompiler', '-fPIC',
+        '-DKTRANSFORMERS_USE_CUDA',
+    ]
+
+    if USE_FASTPT_CUDA:
+        extra_nvcc_flags.append('-DKTRANSFORMERS_USE_DTK')
+
+    ops_module = CUDAExtension(
+        'KTransformersOps', [
         'ktransformers/ktransformers_ext/cuda/custom_gguf/dequant.cu',
         'ktransformers/ktransformers_ext/cuda/binding.cpp',
         'ktransformers/ktransformers_ext/cuda/gptq_marlin/gptq_marlin.cu'
     ],
     extra_compile_args={
-            'cxx': ['-O3', '-DKTRANSFORMERS_USE_CUDA'],
-            'nvcc': [
-                '-O3',
-                '--use_fast_math',
-                '-Xcompiler', '-fPIC',
-                '-DKTRANSFORMERS_USE_CUDA',
-            ]
-        }
-    )
+        'cxx': ['-O3', '-DKTRANSFORMERS_USE_CUDA'],
+        'nvcc': extra_nvcc_flags
+    }
+  )
 elif MUSA_HOME is not None:
     SimplePorting(cuda_dir_path="ktransformers/ktransformers_ext/cuda", mapping_rule={
         # Common rules