Restore CPU offloading capability

ktransformers/operators/attention.py

@@ -595,7 +595,10 @@ 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' or get_compute_capability()<8 or device_manager.gpu_vendor != GPUVendor.NVIDIA:
+        if (os.name == 'nt'
+            or get_compute_capability() < 8
+            or hidden_states.device.type == 'cpu'
+            or device_manager.gpu_vendor != GPUVendor.NVIDIA):
             return self.forward_windows(
                 hidden_states,
                 attention_mask,

ktransformers/operators/models.py

@@ -650,7 +650,10 @@ class KDeepseekV2Model(BaseInjectedModule):
         if per_layer_prefill_flag:
             causal_mask = None
         else:
-            if os.name == 'nt' or get_compute_capability()<8 or device_manager.gpu_vendor != GPUVendor.NVIDIA:
+            if (os.name == 'nt'
+                or get_compute_capability() < 8
+                or (self.transfer_map is not None and 'cpu' in self.transfer_map.values())
+                or device_manager.gpu_vendor != GPUVendor.NVIDIA):
                 # print("for Windows or GPU before ampere, use forward_windows")
                 # only use mask in forward windows or can't flash attn
                 causal_mask = self._update_causal_mask(

ktransformers/optimize/optimize_rules/DeepSeek-V2-Lite-Chat-gpu-cpu.yaml

@@ -0,0 +1,184 @@
+- match:
+    name: "^model.embed_tokens"
+  replace:
+    class: "default"
+    kwargs:
+        generate_device: "cpu"
+        prefill_device: "cpu"
+
+# === Rotary Embedding Replacement ===
+
+# GPU 0: layers 0–9
+- match:
+    name: "^model\\.layers\\.(0|[1-9])\\."
+    class: ktransformers.models.modeling_deepseek.DeepseekV2YarnRotaryEmbedding
+  replace:
+    class: ktransformers.operators.RoPE.YarnRotaryEmbedding
+    kwargs:
+      generate_device: "cuda:0"
+      prefill_device: "cuda:0"
+# CPU: layers 10-29
+- match:
+    name: "^model\\.layers\\.([12][0-9])\\."
+    class: ktransformers.models.modeling_deepseek.DeepseekV2YarnRotaryEmbedding
+  replace:
+    class: ktransformers.operators.RoPE.YarnRotaryEmbedding
+    kwargs:
+      generate_device: "cpu"
+      prefill_device: "cpu"
+
+# === Linear Layers Replacement (excluding self_attn) ===
+
+# GPU 0: layers 0–9
+- match:
+    name: "^model\\.layers\\.(0|[1-9])\\.(?!self_attn).*$"  # regular expression
+    class: torch.nn.Linear  # only match modules matching name and class simultaneously
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
+    kwargs:
+      generate_device: "cuda:0"
+      prefill_device: "cuda:0"
+      generate_op: "KLinearMarlin"
+      prefill_op: "KLinearTorch"
+# CPU: layers 10-29
+- match:
+    name: "^model\\.layers\\.([12][0-9])\\.(?!self_attn).*$"  # 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: "cpu"
+      prefill_device: "cpu"
+      generate_op: "KLinearCPUInfer"
+      prefill_op: "KLinearTorch"
+      out_device: "cpu"
+
+# === MLP (MoE) Replacement ===
+
+# GPU 0: layers 0–9
+- match:
+    name: "^model\\.layers\\.(0|[1-9])\\.mlp$"
+    class: ktransformers.models.modeling_deepseek.DeepseekV2MoE
+  replace:
+    class: ktransformers.operators.experts.KDeepseekV2MoE     # mlp module with custom forward function
+    kwargs:
+      generate_device: "cuda:0"
+      prefill_device: "cuda:0"
+# CPU: layers 10-29
+- match:
+    name: "^model\\.layers\\.([12][0-9])\\.mlp$"
+    class: ktransformers.models.modeling_deepseek.DeepseekV2MoE
+  replace:
+    class: ktransformers.operators.experts.KDeepseekV2MoE     # mlp module with custom forward function
+    kwargs:
+      generate_device: "cpu"
+      prefill_device: "cpu"
+
+# === MLP Gate Replacement ===
+
+# GPU 0: layers 0–9
+- match:
+    name: "^model\\.layers\\.(0|[1-9])\\.mlp\\.gate$"
+    class: ktransformers.models.modeling_deepseek_v3.MoEGate
+  replace:
+    class: ktransformers.operators.gate.KMoEGate
+    kwargs:
+      generate_device: "cuda:0"
+      prefill_device: "cuda:0"
+# CPU: layers 10-29
+- match:
+    name: "^model\\.layers\\.([12][0-9])\\.mlp\\.gate$"
+    class: ktransformers.models.modeling_deepseek_v3.MoEGate
+  replace:
+    class: ktransformers.operators.gate.KMoEGate
+    kwargs:
+      generate_device: "cpu"
+      prefill_device: "cpu"
+
+# === MLP Experts Replacement ===
+
+# GPU 0: layers 0–9
+- match:
+    name: "^model\\.layers\\.(0|[1-9])\\.mlp\\.experts$"
+  replace:
+    class: ktransformers.operators.experts.KTransformersExperts     # custom MoE Kernel with expert paralleism
+    kwargs:
+      prefill_device: "cuda:0"
+      prefill_op: "KExpertsTorch"
+      generate_device: "cpu"
+      generate_op:  "KExpertsCPU"
+      out_device: "cuda:0"
+  recursive: False # don't recursively inject submodules of this module
+# CPU: layers 10-29
+- match:
+    name: "^model\\.layers\\.([12][0-9])\\.mlp\\.experts$"
+  replace:
+    class: ktransformers.operators.experts.KTransformersExperts     # custom MoE Kernel with expert paralleism
+    kwargs:
+      prefill_device: "cpu"
+      prefill_op: "KExpertsTorch"
+      generate_device: "cpu"
+      generate_op:  "KExpertsCPU"
+      out_device: "cpu"
+  recursive: False # don't recursively inject submodules of this module
+
+# === Self-Attention Replacement ===
+
+# GPU 0: layers 0–9
+- match:
+    name: "^model\\.layers\\.(0|[1-9])\\.self_attn$"
+  replace:
+    class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
+    kwargs:
+      generate_device: "cuda:0"
+      prefill_device: "cuda:0"
+# CPU: layers 10-29
+- match:
+    name: "^model\\.layers\\.([12][0-9])\\.self_attn$"
+  replace:
+    class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
+    kwargs:
+      generate_device: "cpu"
+      prefill_device: "cpu"
+
+# === Overall Model Replacement with Transfer Map ===
+
+- match:
+    name: "^model$"
+  replace:
+    class: "ktransformers.operators.models.KDeepseekV2Model"
+    kwargs:
+      per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill
+      transfer_map:
+        10: "cpu"
+
+# === Default Catch-All for Other Modules ===#
+# GPU 0: layers 0–9
+- match:
+    name: "^model\\.layers\\.(0|[1-9])\\."
+  replace:
+    class: "default"
+    kwargs:
+      generate_device: "cuda:0"
+      prefill_device: "cuda:0"
+
+#lmm_head on GPU 0
+- match:
+    name: "^lm_head"
+    class: torch.nn.Linear
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear
+    kwargs:
+      generate_device: "cuda:0"
+      prefill_device: "cuda:0"
+      generate_op: "KLinearMarlin"
+      prefill_op: "KLinearTorch"
+
+# CPU: layers 10-29
+- match:
+    name: "(^model\\.layers\\.([12][0-9])\\.)|(model.norm)"
+  replace:
+    class: "default"
+    kwargs:
+      generate_device: "cpu"
+      prefill_device: "cpu"