Merge remote-tracking branch 'upstream/develop-0.2.2' into support-fp8

ktransformers/operators/linear.py

@@ -21,6 +21,7 @@ from ktransformers.ktransformers_ext.operators.custom_marlin.quantize.utils.marl
     MarlinWorkspace,
     marlin_quantize,
     GPTQ_MARLIN_MIN_THREAD_N,
+    GPTQ_MARLIN_MIN_THREAD_K,
     GPTQ_MARLIN_MAX_PARALLEL,
 )
 from ktransformers.operators.base_operator import BaseInjectedModule
@@ -65,6 +66,8 @@ class KLinearBase(ABC):
             self.in_features  = self.gguf_loader.tensor_info[key + ".weight"]["shape"][0]
             self.out_features = self.gguf_loader.tensor_info[key + ".weight"]["shape"][1]
 
+        self.loaded = False # for lm_head pre-load, TODO: use new way to do lm_head pre-load when layer wise prefill.
+
     @abstractmethod
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         pass
@@ -141,6 +144,7 @@ class KLinearTorch(KLinearBase):
         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
         if w is None: w = self.load_weight(device=device)
         # else: self.out_features = w.shape[0], self.in_features = w.shape[1]
@@ -164,6 +168,7 @@ class KLinearTorch(KLinearBase):
         self.weight = self.weight.to(device)
         if self.has_bias:
             self.bias = self.bias.to(device)
+        self.loaded = True
 
     def unload(self):
         if self.weight is not None:
@@ -251,20 +256,36 @@ class KLinearMarlin(KLinearBase):
         self.group_size = group_size
         self.act_order = act_order
         self.is_k_full = is_k_full
+        self.padding = False
+        self.orin_in_features = self.in_features
+        self.orin_out_features = self.out_features
+        if self.in_features%GPTQ_MARLIN_MIN_THREAD_K!=0 or self.out_features%GPTQ_MARLIN_MIN_THREAD_K!=0:
+            #print(f"warning!, in_features={in_features} or out_features={out_features} is undivisible by GPTQ_MARLIN_MIN_THREAD_K={GPTQ_MARLIN_MIN_THREAD_K} and GPTQ_MARLIN_MIN_THREAD_N={GPTQ_MARLIN_MIN_THREAD_N}, padding")
+            self.padding = True
+            self.in_features = (self.in_features+GPTQ_MARLIN_MIN_THREAD_K-1)//GPTQ_MARLIN_MIN_THREAD_K*GPTQ_MARLIN_MIN_THREAD_K
+            self.out_features = (self.out_features+GPTQ_MARLIN_MIN_THREAD_N-1)//GPTQ_MARLIN_MIN_THREAD_N*GPTQ_MARLIN_MIN_THREAD_N
+            #print(f"After padding: in_features={in_features}, out_features={out_features}")
+        
+        self.k = self.in_features
+        self.n = self.out_features
 
     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() != "cpu", "Marlin quantized linear only supports GPU device"
+        
+        #if self.in_features * self.out_features:
         if w is None: 
             w = self.load_weight(device=device) 
 
         if isinstance(w, nn.Parameter):
             # pad weight
-            weight = w.view(self.out_features, self.in_features).T
+            weight = w.view(self.orin_out_features, self.orin_in_features).T
             self.has_bias = False
         elif isinstance(w, tuple):
             w = list(w)
-            weight = w[0].view(self.out_features, self.in_features).T
+            weight = w[0].view(self.orin_out_features, self.orin_in_features).T
+            self.bias = w[1].view(self.orin_out_features)
             self.bias = w[1]
             self.has_bias = True
         else:
@@ -272,8 +293,14 @@ class KLinearMarlin(KLinearBase):
         weight = weight.to(device)
         if self.has_bias:
             self.bias = self.bias.to(device)
+            
+        if self.padding:
+            padded_weight = torch.zeros(self.in_features, self.out_features, device=self.device)
+            padded_weight[:self.orin_in_features, :self.orin_out_features] = weight
+            weight = padded_weight
+
         # Pack Marlin linear
-        w_ref, marlin_q_w, marlin_s, g_idx, sort_indices, _ = marlin_quantize(
+        marlin_q_w, marlin_s, g_idx, sort_indices, _ = marlin_quantize(
             weight, self.num_bits, self.group_size, self.act_order
         )
         self.workspace = MarlinWorkspace(
@@ -286,6 +313,7 @@ class KLinearMarlin(KLinearBase):
         self.sort_indices = sort_indices
         self.k = weight.shape[0]
         self.n = weight.shape[1]
+        self.loaded = True
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         # Only support input x as BF16 and FP16
@@ -293,6 +321,11 @@ class KLinearMarlin(KLinearBase):
         orig_shape = list(x.shape)
         orig_dtype = x.dtype
         x = x.reshape(-1, orig_shape[-1])
+        x = x.reshape(-1, x.shape[-1])
+        if self.padding:
+            padding_input=torch.empty(x.shape[0], self.in_features, device=x.device, dtype=x.dtype)
+            padding_input[:,:self.orin_in_features] = x
+            x = padding_input
         marlin_s = self.marlin_s.to(x.dtype)
         x = KTransformersOps.gptq_marlin_gemm(
             x,
@@ -307,6 +340,11 @@ class KLinearMarlin(KLinearBase):
             x.shape[-1],
             self.is_k_full,
         )
+        if self.padding:
+            x = x[:,:self.orin_out_features]
+            orig_shape[-1] = self.orin_out_features
+        else:
+            orig_shape[-1] = self.out_features
         if self.has_bias:
             x = x + self.bias
         orig_shape[-1] = self.n
@@ -450,23 +488,12 @@ class KTransformersLinear(BaseInjectedModule, KLinearBase):
         # build all the linear operators
         if prefill_op is not None:
             assert prefill_op in LINEAR_MAP, f"linear_type {prefill_op} not supported"
-            if prefill_op == "KLinearMarlin" and (orig_module.in_features%GPTQ_MARLIN_MIN_THREAD_N!=0 or orig_module.out_features%GPTQ_MARLIN_MIN_THREAD_N!=0):
-                print(f"This linear module's in_features or out_features is not divisible by GPTQ_MARLIN_MIN_THREAD_N({GPTQ_MARLIN_MIN_THREAD_N}), using KLinearTorch instead.")
-                print(f"module info: key:{key} orig_module:{orig_module}")
-                self.prefill_linear = KLinearTorch(key, gguf_loader, config, orig_module, prefill_device, **kwargs)
-            else:
             self.prefill_linear = LINEAR_MAP[prefill_op](key, gguf_loader, config, orig_module, prefill_device, **kwargs)
         else:
             self.prefill_linear = None
 
         if generate_op is not None:
             assert generate_op in LINEAR_MAP, f"linear_type {generate_op} not supported"
-            if generate_op == "KLinearMarlin" and (orig_module.in_features%GPTQ_MARLIN_MIN_THREAD_N!=0 or orig_module.out_features%GPTQ_MARLIN_MIN_THREAD_N!=0):
-                print(f"This linear module's in_features or out_features is not divisible by GPTQ_MARLIN_MIN_THREAD_N({GPTQ_MARLIN_MIN_THREAD_N}), using KLinearTorch instead.")
-                print(f"module info: key:{key} orig_module:{orig_module}")
-                self.generate_op = "KLinearTorch"
-                self.generate_linear = KLinearTorch(key, gguf_loader, config, orig_module, generate_device, **kwargs)
-            else:
             self.generate_linear = LINEAR_MAP[generate_op](key, gguf_loader, config, orig_module, generate_device, **kwargs)
         else:
             self.generate_linear = None

ktransformers/optimize/optimize.py

@@ -126,6 +126,8 @@ def optimize_and_load_gguf(module: nn.Module, rule_file: str, gguf_path: str, mo
     gguf_loader=GGUFLoader(gguf_path)
     with torch.device("meta"):
         inject(module, optimize_config, model_config, gguf_loader)
+    # pre load lm_head because its big inter result
+    load_weights(module.lm_head, gguf_loader, "lm_head.")
     load_weights(module, gguf_loader)
     module.gguf_loader = gguf_loader
     del_meta(module)

ktransformers/optimize/optimize_rules/DeepSeek-V2-Chat-multi-gpu-4.yaml

@@ -219,8 +219,20 @@
     kwargs:
       generate_device: "cuda:2"
       prefill_device: "cuda:2"
+
+- match:
+    name: "^lm_head"
+    class: torch.nn.Linear
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear
+    kwargs:
+      generate_device: "cuda:3"
+      prefill_device: "cuda:3"
+      generate_op: "KLinearMarlin"
+      prefill_op: "KLinearTorch"
+      
 - match:
-    name: "(^model\\.layers\\.([5][0-9]|[4][5-9])\\.)|(^model.norm)|(^lm_head)"
+    name: "(^model\\.layers\\.([5][0-9]|[4][5-9])\\.)|(^model.norm)"
   replace:
     class: "default"
     kwargs:

ktransformers/optimize/optimize_rules/DeepSeek-V2-Chat-multi-gpu.yaml

@@ -118,7 +118,18 @@
       prefill_device: "cuda:0"
 
 - match:
-    name: "(^model\\.layers\\.([345][0-9])\\.)|(model.norm)|(lm_head)"
+    name: "^lm_head"
+    class: torch.nn.Linear
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear
+    kwargs:
+      generate_device: "cuda:1"
+      prefill_device: "cuda:1"
+      generate_op: "KLinearMarlin"
+      prefill_op: "KLinearTorch"
+
+- match:
+    name: "(^model\\.layers\\.([345][0-9])\\.)|(model.norm)"
   replace:
     class: "default"
     kwargs:

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

@@ -15,6 +15,18 @@
       prefill_device: "cuda"
       generate_op: "KLinearMarlin"
       prefill_op: "KLinearTorch"
+
+- match:
+    name: "^lm_head"
+    class: torch.nn.Linear
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+      generate_op: "KLinearMarlin"
+      prefill_op: "KLinearTorch"
+
 - match:
     name: "^model\\.layers\\..*\\.mlp$"
     class: ktransformers.models.modeling_deepseek.DeepseekV2MoE

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

@@ -118,7 +118,18 @@
       prefill_device: "cuda:0"
 
 - match:
-    name: "(^model\\.layers\\.([12][0-9])\\.)|(model.norm)|(lm_head)"
+    name: "^lm_head"
+    class: torch.nn.Linear
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear
+    kwargs:
+      generate_device: "cuda:1"
+      prefill_device: "cuda:1"
+      generate_op: "KLinearMarlin"
+      prefill_op: "KLinearTorch"
+
+- match:
+    name: "(^model\\.layers\\.([12][0-9])\\.)|(model.norm)"
   replace:
     class: "default"
     kwargs:

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

@@ -15,6 +15,18 @@
       prefill_device: "cuda"
       generate_op: "KLinearMarlin"
       prefill_op: "KLinearTorch"
+
+- match:
+    name: "^lm_head"
+    class: torch.nn.Linear
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+      generate_op: "KLinearMarlin"
+      prefill_op: "KLinearTorch"
+
 - match:
     name: "^model\\.layers\\..*\\.mlp$"
     class: ktransformers.models.modeling_deepseek.DeepseekV2MoE

ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu-4.yaml

@@ -188,7 +188,7 @@
 # !!!Do remember 'close' cuda graph if you are using marlin expert.!!!
 # !!!KExpertsTorch is untested, we don't have enough VRAM.!!!
 
-# # GPU 0: layers 3–4
+# GPU 0: layers 3–4
 # - match:
 #     name: "^model\\.layers\\.([3-4])\\.mlp\\.experts$"
 #   replace:
@@ -363,11 +363,20 @@
       generate_device: "cuda:2"
       prefill_device: "cuda:2"
 
-# don't inject lm_head if already inject marlin experts
+- match:
+    name: "^lm_head"
+    class: torch.nn.Linear
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear
+    kwargs:
+      generate_device: "cuda:3"
+      prefill_device: "cuda:3"
+      generate_op: "KLinearMarlin"
+      prefill_op: "KLinearTorch"
 
-# For final modules (model.norm and lm_head), ensure they are on GPU 3 (as in your original config)
+# For final modules (model.norm), ensure they are on GPU 3 (as in your original config)
 - match:
-    name: "(^model\\.layers\\.(4[5-9]|5[0-9]|60)\\.)|(^model\\.norm)|(^lm_head)"
+    name: "(^model\\.layers\\.(4[5-9]|5[0-9]|60)\\.)|(^model\\.norm)"
   replace:
     class: "default"
     kwargs:

ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu-8.yaml

@@ -713,11 +713,20 @@
       generate_device: "cuda:7"
       prefill_device: "cuda:7"
 
-# don't inject lm_head if already inject marlin experts
+- match:
+    name: "^lm_head"
+    class: torch.nn.Linear
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear
+    kwargs:
+      generate_device: "cuda:7"
+      prefill_device: "cuda:7"
+      generate_op: "KLinearMarlin"
+      prefill_op: "KLinearTorch"
 
-# For final modules (model.norm and lm_head), ensure they are on GPU 7 (as in your original config)
+# For final modules (model.norm), ensure they are on GPU 7 (as in your original config)
 - match:
-    name: "(^model\\.layers\\.(4[5-9]|5[0-9]|60)\\.)|(^model\\.norm)|(^lm_head)"
+    name: "(^model\\.layers\\.(4[5-9]|5[0-9]|60)\\.)|(^model\\.norm)"
   replace:
     class: "default"
     kwargs:

ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu-marlin.yaml

@@ -153,7 +153,18 @@
       prefill_device: "cuda:0"
 
 - match:
-    name: "(^model\\.layers\\.([3456][0-9])\\.)|(model.norm)|(lm_head)"
+    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"
+
+- match:
+    name: "(^model\\.layers\\.([3456][0-9])\\.)|(model.norm)"
   replace:
     class: "default"
     kwargs:

ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu.yaml

@@ -135,7 +135,18 @@
       prefill_device: "cuda:0"
 
 - match:
-    name: "(^model\\.layers\\.([3456][0-9])\\.)|(model.norm)|(lm_head)"
+    name: "^lm_head"
+    class: torch.nn.Linear
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear
+    kwargs:
+      generate_device: "cuda:1"
+      prefill_device: "cuda:1"
+      generate_op: "KLinearMarlin"
+      prefill_op: "KLinearTorch"
+
+- match:
+    name: "(^model\\.layers\\.([3456][0-9])\\.)|(model.norm)"
   replace:
     class: "default"
     kwargs:

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

@@ -5,6 +5,18 @@
     kwargs:
       generate_device: "cuda"
       prefill_device: "cuda"
+
+- match:
+    name: "^lm_head$"  # regular expression 
+    class: torch.nn.Linear  # only match modules matching name and class simultaneously
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+      generate_op: "KLinearMarlin"
+      prefill_op: "KLinearTorch"
+
 - match:
     name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression 
     class: torch.nn.Linear  # only match modules matching name and class simultaneously

ktransformers/optimize/optimize_rules/Mixtral.yaml

@@ -15,6 +15,16 @@
       prefill_device: "cuda"
       generate_op: "KLinearMarlin"
       prefill_op: "KLinearTorch"
+- match:
+    name: "^lm_head"
+    class: torch.nn.Linear
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+      generate_op: "KLinearMarlin"
+      prefill_op: "KLinearTorch"
 - match:
     name: "^model\\.layers\\..*\\.block_sparse_moe$"
     class: ktransformers.models.modeling_mixtral.MixtralSparseMoeBlock

ktransformers/optimize/optimize_rules/Qwen2-57B-A14B-Instruct-multi-gpu.yaml

@@ -77,9 +77,19 @@
     kwargs:
         generate_device: "cpu"
         prefill_device: "cpu"
+- match:
+    name: "^lm_head"
+    class: torch.nn.Linear
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear
+    kwargs:
+      generate_device: "cuda:1"
+      prefill_device: "cuda:1"
+      generate_op: "KLinearMarlin"
+      prefill_op: "KLinearTorch"
 
 - match:
-    name: "(^model.norm)|(^lm_head)"
+    name: "(^model.norm)"
   replace:
     class: "default"
     kwargs:

ktransformers/optimize/optimize_rules/Qwen2-57B-A14B-Instruct.yaml

@@ -15,6 +15,16 @@
       prefill_device: "cuda"
       generate_op: "KLinearMarlin"
       prefill_op: "KLinearTorch"
+- match:
+    name: "^lm_head"
+    class: torch.nn.Linear
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+      generate_op: "KLinearMarlin"
+      prefill_op: "KLinearTorch"
 - match:
     name: "^model\\.layers\\..*\\.mlp$"
     class: ktransformers.models.modeling_qwen2_moe.Qwen2MoeSparseMoeBlock

ktransformers/server/backend/interfaces/ktransformers.py

@@ -25,10 +25,10 @@ class KTransformersThreadContext(TransformersThreadContext):
 class KTransformersInterface(TransformersInterface):
     def __init__(self, args: ConfigArgs = default_args):
         self.args = args
-        torch.set_default_dtype(torch.bfloat16)
         torch.set_grad_enabled(False)
         self.tokenizer = AutoTokenizer.from_pretrained(args.model_dir, device=args.device, trust_remote_code=args.trust_remote_code)
         config = AutoConfig.from_pretrained(args.model_dir, trust_remote_code=args.trust_remote_code)
+        torch.set_default_dtype(config.torch_dtype)
         if config.architectures[0] == "Qwen2MoeForCausalLM":
             config._attn_implementation = "flash_attention_2"
 

ktransformers/tests/mmlu_pro_test.py

@@ -176,7 +176,7 @@ if __name__ == "__main__":
     parser.add_argument("--result", type=str, default="./mmlu_pro.json", help="Path to save the result JSON file")
     parser.add_argument("--log", type=str, default="./mmlu_pro.log", help="Path to save the log file")
     parser.add_argument("--model", type=str, default="Pro/deepseek-ai/DeepSeek-V3", help="Model name or path")
-    parser.add_argument("--api_url", type=str, default="http://localhost:10002/v1/chat/completions", help="API URL")
+    parser.add_argument("--api_url", type=str, default="http://localhost:15488/v1/chat/completions", help="API URL")
     # parser.add_argument("--api_url", type=str, default="https://api.siliconflow.cn/v1/chat/completions", help="API URL")
 
     args = parser.parse_args()

ktransformers/util/custom_gguf.py

@@ -26,6 +26,7 @@ from enum import IntEnum
 import torch
 import KTransformersOps
 from .custom_loader import SafeTensorLoader
+import ctypes
 
 class GGMLQuantizationType(IntEnum):
     F32     = 0
@@ -305,7 +306,7 @@ class GGUFLoader:
         data = torch.from_numpy(data)
         return data, ggml_type
 
-    def load_expert_tensor(self, name, data, expert_id, elements_per_expert, device = "gpu")->torch.Tensor:
+    def load_expert_tensor(self, name, data, expert_id, elements_per_expert, device = "cuda", target_dtype = torch.get_default_dtype())->torch.Tensor:
         t = self.tensor_info[name]
         if device.lower() == "cpu":
             print(f"loading expert {expert_id} of {name} with CPU")
@@ -324,19 +325,21 @@ class GGUFLoader:
         data = data[offset: offset + block_size * blocks_per_experts]
         
         if "cuda" in device.lower():
-            values = GGML_DEQUANTIZE_GPU[ggml_name](data, device)
+            values = GGML_DEQUANTIZE_GPU[ggml_name](data, device, target_dtype)
         else:
             values = GGML_DEQUANTIZE[ggml_name](data)
-            values = torch.from_numpy(values)
+            values = torch.from_numpy(values.copy())
 
         values = values.view(shape[-2::-1])
 
         return values
 
-    def load_gguf_tensor(self, name: str, device:str = "cpu")->torch.Tensor:
+    def load_gguf_tensor(self, name: str, device:str = "cpu", target_dtype = None)->torch.Tensor:
         t = self.tensor_info[name]
         if device.lower() == "cpu":
             print(f"loading {name} with CPU")
+        if target_dtype == None:
+            target_dtype = torch.get_default_dtype()
         
         shape = t["shape"]
         ggml_type = t["ggml_type"]
@@ -348,16 +351,38 @@ class GGUFLoader:
 
         data = self.get_mmap_tensor(name)
 
+        block_size = GGML_BLOCK_SIZES[ggml_name]
+        elements_per_block = GGML_ELEMENTS_PER_BLOCK[ggml_name]
+        num_elements = int(np.prod(shape))
+        num_blocks = num_elements // elements_per_block
+        
+        blocks_per_iter = 16384
+        if num_blocks > blocks_per_iter: # dequant large tensor
+            values = torch.empty((num_blocks, elements_per_block), dtype=target_dtype, device=device)
+            for i in range( (num_blocks + blocks_per_iter - 1) // blocks_per_iter):
+                blocks_begin = i * blocks_per_iter
+                blocks_end = min(blocks_begin + blocks_per_iter, num_blocks)
+                if "cuda" in device.lower():
+                    cur_values = GGML_DEQUANTIZE_GPU[ggml_name](data[blocks_begin*block_size : blocks_end*block_size], device, target_dtype)
+                else:
+                    cur_values = GGML_DEQUANTIZE[ggml_name](data[blocks_begin*block_size : blocks_end*block_size])
+                    cur_values = torch.from_numpy(cur_values.copy())
+                
+                cur_values = cur_values.view(-1, elements_per_block)
+                if ggml_name == "BF16":
+                    cur_values = cur_values.view(torch.bfloat16)
+                values[blocks_begin : blocks_end] = cur_values
+        else:
             if "cuda" in device.lower():
                 values = GGML_DEQUANTIZE_GPU[ggml_name](data, device)
-            #values = GGML_DEQUANTIZE[ggml_name](data)
-            #print("load_gguf_tensor")
-            #values = torch.from_numpy(values).to(device = device)
             else:
                 values = GGML_DEQUANTIZE[ggml_name](data)
                 values = torch.from_numpy(values)
+                
         if ggml_name == "BF16":
             values = values.view(torch.bfloat16)
+            
+
         values = values.view(shape[::-1])
         if "attn_q" in name and self.gguf_file_meta['general.architecture'] in ["llama"]:
             n_head = self.gguf_file_meta['llama.attention.head_count']
@@ -456,14 +481,15 @@ def dequantize_q2_k(data):
 
     return d * (scales & 15) * (tmp & 3) - dmin * (scales >> 4)
 
-def dequantize_q2_k_gpu(data, device:str ="cuda"):
+def dequantize_q2_k_gpu(data, device:str ="cuda", target_dtype = torch.get_default_dtype()):
     block_size = GGML_BLOCK_SIZES["Q2_K"]
+    ele_per_blk = GGML_ELEMENTS_PER_BLOCK["Q2_K"]
     data = np.frombuffer(data, dtype=data.dtype)
     device = torch.device(device)
     # TODO: this and from_numpy in other functions will cause a warning saying that numpy is not writable, 
     # the best way to fix this is transfer ptr to KTransformersOps instead of Tensor.
-    data = torch.from_numpy(data)
-    return KTransformersOps.dequantize_q2_k(data, block_size, device)
+    c_pointer = ctypes.addressof(ctypes.cast(data.ctypes.data, ctypes.POINTER(ctypes.c_int8)).contents)
+    return KTransformersOps.dequantize_q2_k(c_pointer, data.size, block_size, ele_per_blk, device, target_dtype)
 
 def dequantize_q3_k(data):
     # C implementation
@@ -507,14 +533,15 @@ def dequantize_q3_k(data):
         (((qs[:, 48:64] >> 6) & 3) - bits[:, 16:, 7])
     ], axis=1)
 
-def dequantize_q3_k_gpu(data, device:str ="cuda"):
+def dequantize_q3_k_gpu(data, device:str ="cuda", target_dtype = torch.get_default_dtype()):
     block_size = GGML_BLOCK_SIZES["Q3_K"]
+    ele_per_blk = GGML_ELEMENTS_PER_BLOCK["Q3_K"]
     data = np.frombuffer(data, dtype=data.dtype)
     device = torch.device(device)
     # TODO: this and from_numpy in other functions will cause a warning saying that numpy is not writable, 
     # the best way to fix this is transfer ptr to KTransformersOps instead of Tensor.
-    data = torch.from_numpy(data)
-    return KTransformersOps.dequantize_q3_k(data, block_size, device)
+    c_pointer = ctypes.addressof(ctypes.cast(data.ctypes.data, ctypes.POINTER(ctypes.c_int8)).contents)
+    return KTransformersOps.dequantize_q3_k(c_pointer, data.size, block_size, ele_per_blk, device, target_dtype)
 
 def dequantize_q4_k(data):
     # C implementation
@@ -538,13 +565,15 @@ def dequantize_q4_k(data):
     # Dequantize final weights using scales and offsets
     return factors * qs2 - offsets
 
-def dequantize_q4_k_gpu(data, device:str ="cuda"):
+def dequantize_q4_k_gpu(data, device:str ="cuda", target_dtype = torch.get_default_dtype()):
+    block_size = GGML_BLOCK_SIZES["Q4_K"]
+    ele_per_blk = GGML_ELEMENTS_PER_BLOCK["Q4_K"]
     data = np.frombuffer(data, dtype=data.dtype)
     device = torch.device(device)
     # TODO: this and from_numpy in other functions will cause a warning saying that numpy is not writable, 
     # the best way to fix this is transfer ptr to KTransformersOps instead of Tensor.
-    data = torch.from_numpy(data)
-    return KTransformersOps.dequantize_q4_k(data, 144, device)
+    c_pointer = ctypes.addressof(ctypes.cast(data.ctypes.data, ctypes.POINTER(ctypes.c_int8)).contents)
+    return KTransformersOps.dequantize_q4_k(c_pointer, data.size, block_size, ele_per_blk, device, target_dtype)
 
 def dequantize_q5_k(data):
     # C implementation
@@ -602,14 +631,15 @@ def dequantize_q5_k(data):
         d8 * (qs_hi_4[:, 3] + (bits[:, :, 7] << 4)) - m8,
     ], axis=1)
 
-def dequantize_q5_k_gpu(data, device:str ="cuda"):
+def dequantize_q5_k_gpu(data, device:str ="cuda", target_dtype = torch.get_default_dtype()):
     block_size = GGML_BLOCK_SIZES["Q5_K"]
+    ele_per_blk = GGML_ELEMENTS_PER_BLOCK["Q5_K"]
     data = np.frombuffer(data, dtype=data.dtype)
     device = torch.device(device)
     # TODO: this and from_numpy in other functions will cause a warning saying that numpy is not writable, 
     # the best way to fix this is transfer ptr to KTransformersOps instead of Tensor.
-    data = torch.from_numpy(data)
-    return KTransformersOps.dequantize_q5_k(data, block_size, device)
+    c_pointer = ctypes.addressof(ctypes.cast(data.ctypes.data, ctypes.POINTER(ctypes.c_int8)).contents)
+    return KTransformersOps.dequantize_q5_k(c_pointer, data.size, block_size, ele_per_blk, device, target_dtype)
 
 def dequantize_q6_k(data):
     # C implementation
@@ -660,13 +690,14 @@ def dequantize_q6_k(data):
     ], axis=1) 
 
 # @torch.jit.script
-def dequantize_q6_k_gpu(data: np.ndarray, device:str = "cuda"):
+def dequantize_q6_k_gpu(data: np.ndarray, device:str = "cuda", target_dtype = torch.get_default_dtype()):
     block_size = GGML_BLOCK_SIZES["Q6_K"]
+    ele_per_blk = GGML_ELEMENTS_PER_BLOCK["Q6_K"]
     device = torch.device(device)
     num_blocks = len(data) // block_size
     data = np.frombuffer(data, dtype=data.dtype)
-    data = torch.from_numpy(data)
-    return KTransformersOps.dequantize_q6_k(data, block_size, device)
+    c_pointer = ctypes.addressof(ctypes.cast(data.ctypes.data, ctypes.POINTER(ctypes.c_int8)).contents)
+    return KTransformersOps.dequantize_q6_k(c_pointer, data.size, block_size, ele_per_blk, device, target_dtype)
 
 kvalues_iq4nl = np.array([-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113], dtype=np.int8)
 
@@ -700,13 +731,14 @@ def dequantize_iq4_xs(data):
 
     return y.flatten()
 
-def dequantize_iq4_xs_gpu(data: np.ndarray, device:str = "cuda"):
+def dequantize_iq4_xs_gpu(data: np.ndarray, device:str = "cuda", target_dtype = torch.get_default_dtype()):
     block_size = GGML_BLOCK_SIZES["IQ4_XS"]
+    ele_per_blk = GGML_ELEMENTS_PER_BLOCK["IQ4_XS"]
     device = torch.device(device)
     num_blocks = len(data) // block_size
     data = np.frombuffer(data, dtype=data.dtype)
-    data = torch.from_numpy(data)
-    return KTransformersOps.dequantize_iq4_xs(data, block_size, device)
+    c_pointer = ctypes.addressof(ctypes.cast(data.ctypes.data, ctypes.POINTER(ctypes.c_int8)).contents)
+    return KTransformersOps.dequantize_iq4_xs(c_pointer, data.size, block_size, ele_per_blk, device, target_dtype)
 
 def dequantize_q4_0(data):
     # C implementation
@@ -723,7 +755,7 @@ def dequantize_q4_0(data):
         scales * ((qs >> 4).astype(np.int8) - 8),
     ], axis=1)
 
-def dequantize_q4_0_gpu(data):
+def dequantize_q4_0_gpu(data, device:str = "cuda", target_dtype = torch.get_default_dtype()):
     raise NotImplementedError()
 
 def dequantize_q5_0(data):
@@ -747,7 +779,7 @@ def dequantize_q5_0(data):
         scales * x1,
     ], axis=1)
 
-def dequantize_q5_0_gpu(data):
+def dequantize_q5_0_gpu(data, device:str = "cuda", target_dtype = torch.get_default_dtype()):
     raise NotImplementedError()
 
 def dequantize_q8_0(data):
@@ -759,32 +791,41 @@ def dequantize_q8_0(data):
     qs = np.frombuffer(data, dtype=np.int8).reshape(num_blocks, 2 + 32)[:, 2:]
     return scales * qs
 
-def dequantize_q8_0_gpu(data, device:str = "cuda"):
+def dequantize_q8_0_gpu(data, device:str = "cuda", target_dtype = torch.get_default_dtype()):
     # C struct definition
     # https://github.com/ggerganov/ggml/blob/fca1caafea7de9fbd7efc733b9818f9cf2da3050/src/ggml-quants.h#L43
-    num_blocks = len(data) // GGML_BLOCK_SIZES["Q8_0"]
+    
+    block_size = GGML_BLOCK_SIZES["Q8_0"]
+    ele_per_blk = GGML_ELEMENTS_PER_BLOCK["Q8_0"]
     device = torch.device(device)
     data = np.frombuffer(data, dtype=data.dtype)
-    data = torch.from_numpy(data)
-    return KTransformersOps.dequantize_q8_0(data, 34, device)
+    c_pointer = ctypes.addressof(ctypes.cast(data.ctypes.data, ctypes.POINTER(ctypes.c_int8)).contents)
+    return KTransformersOps.dequantize_q8_0(c_pointer, data.size, block_size, ele_per_blk, device, target_dtype)
 
 
 def dequantize_f32(data):
     return np.frombuffer(data, dtype=np.float32)
 
-def dequantize_f32_gpu(data, device):
+def dequantize_f32_gpu(data, device, target_dtype = torch.get_default_dtype()):
     data = np.frombuffer(data, dtype=np.float32)
-    res = torch.from_numpy(data)
-    res_gpu = torch.empty_like(res, device=device)
+    res = torch.from_numpy(data.copy())
+    res_gpu = torch.empty_like(res, device=device, dtype=target_dtype)
     res_gpu.copy_(res)
     return res_gpu
 
 def dequantize_f16(data):
     return np.frombuffer(data, dtype=np.float16)
 
-def dequantize_f16_gpu(data, device):
+def dequantize_f16_gpu(data, device, target_dtype = torch.get_default_dtype()):
+    data = np.frombuffer(data, dtype=np.float16)
+    res = torch.from_numpy(data.copy())
+    res_gpu = torch.empty_like(res, device=device, dtype=target_dtype)
+    res_gpu.copy_(res)
+    return res_gpu
+
+def dequantize_bf16_gpu(data, device, target_dtype = torch.get_default_dtype()):
     data = np.frombuffer(data, dtype=np.float16)
-    res = torch.from_numpy(data)
+    res = torch.from_numpy(data.copy())
     res_gpu = torch.empty_like(res, device=device)
     res_gpu.copy_(res)
     return res_gpu
@@ -807,7 +848,7 @@ GGML_DEQUANTIZE = {
 GGML_DEQUANTIZE_GPU = {
     "F32": dequantize_f32_gpu,
     "F16": dequantize_f16_gpu,
-    "BF16": dequantize_f16_gpu,
+    "BF16": dequantize_bf16_gpu,
     "Q4_0": dequantize_q4_0_gpu,
     "Q5_0": dequantize_q5_0_gpu,
     "Q8_0": dequantize_q8_0_gpu,

ktransformers/util/utils.py

@@ -90,7 +90,7 @@ def load_cur_state_dict(module: nn.Module, gguf_loader: GGUFLoader, prefix: str
             raise Exception(f"can't find {translated_key} in GGUF file!")
         
 def load_weights(module:nn.Module, gguf_loader:GGUFLoader, prefix=''):
-    # print(f"recursively loading weights {prefix},{return_when_injected=}, {only_load_injected=}")
+    #print(f"recursively loading weights {prefix}")
     if not isinstance(module, base_operator.BaseInjectedModule):
         load_cur_state_dict(module, gguf_loader, prefix)
         for name, child in module._modules.items():

setup.py

@@ -30,6 +30,11 @@ from wheel.bdist_wheel import bdist_wheel as _bdist_wheel
 from setuptools import setup, Extension
 from cpufeature.extension import CPUFeature
 from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CUDA_HOME
+try:
+    from torch_musa.utils.simple_porting import SimplePorting
+    from torch_musa.utils.musa_extension import BuildExtension, MUSAExtension, MUSA_HOME
+except ImportError:
+    MUSA_HOME=None
 
 class CpuInstructInfo:
     CPU_INSTRUCT = os.getenv("CPU_INSTRUCT", "NATIVE")
@@ -49,6 +54,16 @@ class VersionInfo:
     )
     FORCE_BUILD = os.getenv("KTRANSFORMERS_FORCE_BUILD", "FALSE") == "TRUE"
 
+    def get_musa_bare_metal_version(self, musa_dir):
+        raw_output = subprocess.run(
+            [musa_dir + "/bin/mcc", "-v"], check=True,
+            stdout=subprocess.PIPE, stderr=subprocess.STDOUT).stdout.decode("utf-8")
+        output = raw_output.split()
+        release_idx = output.index("version") + 1
+        bare_metal_version = parse(output[release_idx].split(",")[0])
+        musa_version = f"{bare_metal_version.major}{bare_metal_version.minor}"
+        return musa_version
+
     def get_cuda_bare_metal_version(self, cuda_dir):
         raw_output = subprocess.check_output(
             [cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True)
@@ -58,7 +73,7 @@ class VersionInfo:
         cuda_version = f"{bare_metal_version.major}{bare_metal_version.minor}"
         return cuda_version
 
-    def get_cuda_version_of_torch(self,):
+    def get_cuda_version_of_torch(self):
         torch_cuda_version = parse(torch.version.cuda)
         cuda_version = f"{torch_cuda_version.major}{torch_cuda_version.minor}"
         return cuda_version
@@ -128,12 +143,21 @@ class VersionInfo:
         return flash_version
 
     def get_package_version(self, full_version=False):
-        flash_version = self.get_flash_version()
-        package_version = f"{str(flash_version)}+cu{self.get_cuda_bare_metal_version(CUDA_HOME)}torch{self.get_torch_version()}{self.get_cpu_instruct()}"
+        flash_version = str(self.get_flash_version())
+        torch_version = self.get_torch_version()
+        cpu_instruct = self.get_cpu_instruct()
+        backend_version = ""
+        if CUDA_HOME is not None:
+            backend_version = f"cu{self.get_cuda_bare_metal_version(CUDA_HOME)}"
+        elif MUSA_HOME is not None:
+            backend_version = f"mu{self.get_musa_bare_metal_version(MUSA_HOME)}"
+        else:
+            raise ValueError("Unsupported backend: CUDA_HOME and MUSA_HOME are not set.")
+        package_version = f"{flash_version}+{backend_version}torch{torch_version}{cpu_instruct}"
         if full_version:
             return package_version
         if not VersionInfo.FORCE_BUILD:
-            return str(flash_version)
+            return flash_version
         return package_version
 
 
@@ -218,6 +242,14 @@ class CMakeBuild(BuildExtension):
             f"-DPYTHON_EXECUTABLE={sys.executable}",
             f"-DCMAKE_BUILD_TYPE={cfg}",  # not used on MSVC, but no harm
         ]
+
+        if CUDA_HOME is not None:
+            cmake_args += ["-DKTRANSFORMERS_USE_CUDA=ON"]
+        elif MUSA_HOME is not None:
+            cmake_args += ["-DKTRANSFORMERS_USE_MUSA=ON"]
+        else:
+            raise ValueError("Unsupported backend: CUDA_HOME and MUSA_HOME are not set.")
+
         build_args = []
         if "CMAKE_ARGS" in os.environ:
             cmake_args += [
@@ -276,8 +308,13 @@ class CMakeBuild(BuildExtension):
                     "-DCMAKE_OSX_ARCHITECTURES={}".format(";".join(archs))]
 
         if "CMAKE_BUILD_PARALLEL_LEVEL" not in os.environ:
+            cpu_count = os.cpu_count()
+            if cpu_count is None:
+                cpu_count = 1
             if hasattr(self, "parallel") and self.parallel:
-                build_args += [f"-j{self.parallel}"]
+                build_args += [f"--parallel={self.parallel}"]
+            else:
+                build_args += [f"--parallel={cpu_count}"]
         print("CMake args:", cmake_args)
         build_temp = Path(ext.sourcedir) / "build"
         if not build_temp.exists():
@@ -288,28 +325,55 @@ class CMakeBuild(BuildExtension):
         print("Standard output:", result.stdout)
         print("Standard error:", result.stderr)
         subprocess.run(
-            ["cmake", "--build", ".", *build_args], cwd=build_temp, check=True
+            ["cmake", "--build", ".", "--verbose", *build_args], cwd=build_temp, check=True
         )
 
-
-setup(
-    version=VersionInfo().get_package_version(),
-    cmdclass={"bdist_wheel":BuildWheelsCommand ,"build_ext": CMakeBuild},
-    ext_modules=[
-        CMakeExtension("cpuinfer_ext"),
-        CUDAExtension('KTransformersOps', [
+if CUDA_HOME is not None:
+    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'],
+            'cxx': ['-O3', '-DKTRANSFORMERS_USE_CUDA'],
             'nvcc': [
                 '-O3',
                 '--use_fast_math',
                 '-Xcompiler', '-fPIC',
+                '-DKTRANSFORMERS_USE_CUDA',
+            ]
+        }
+    )
+elif MUSA_HOME is not None:
+    SimplePorting(cuda_dir_path="ktransformers/ktransformers_ext/cuda", mapping_rule={
+        # Common rules
+        "at::cuda": "at::musa",
+        "#include <ATen/cuda/CUDAContext.h>": "#include \"torch_musa/csrc/aten/musa/MUSAContext.h\"",
+        "#include <c10/cuda/CUDAGuard.h>": "#include \"torch_musa/csrc/core/MUSAGuard.h\"",
+        }).run()
+    ops_module = MUSAExtension('KTransformersOps', [
+        'ktransformers/ktransformers_ext/cuda_musa/custom_gguf/dequant.mu',
+        'ktransformers/ktransformers_ext/cuda_musa/binding.cpp',
+        # TODO: Add Marlin support for MUSA.
+        # 'ktransformers/ktransformers_ext/cuda_musa/gptq_marlin/gptq_marlin.mu'
+    ],
+    extra_compile_args={
+            'cxx': ['force_mcc'],
+            'mcc': [
+                '-O3',
+                '-DKTRANSFORMERS_USE_MUSA',
+                '-DTHRUST_IGNORE_CUB_VERSION_CHECK',
             ]
         }
     )
+else:
+    raise ValueError("Unsupported backend: CUDA_HOME and MUSA_HOME are not set.")
+
+setup(
+    version=VersionInfo().get_package_version(),
+    cmdclass={"bdist_wheel":BuildWheelsCommand ,"build_ext": CMakeBuild},
+    ext_modules=[
+        CMakeExtension("cpuinfer_ext"),
+        ops_module,
     ]
 )