[Model] Deepseek GGUF support (vllm-project#13167)

docs/source/features/quantization/gguf.md

@@ -29,6 +29,13 @@ vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf --tokenizer TinyLlama/TinyLlam
 We recommend using the tokenizer from base model instead of GGUF model. Because the tokenizer conversion from GGUF is time-consuming and unstable, especially for some models with large vocab size.
 :::
 
+GGUF assumes that huggingface can convert the metadata to a config file. In case huggingface doesn't support your model you can manually create a config and pass it as hf-confing-path
+
+```console
+# If you model is not supported by huggingface you can manually provide a huggingface compatible config path
+vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf --tokenizer TinyLlama/TinyLlama-1.1B-Chat-v1.0 --hf-config-path Tinyllama/TInyLlama-1.1B-Chat-v1.0
+```
+
 You can also use the GGUF model directly through the LLM entrypoint:
 
 ```python

vllm/config.py

@@ -229,6 +229,7 @@ def __init__(
         trust_remote_code: bool,
         dtype: Union[str, torch.dtype],
         seed: int,
+        hf_config_path: Optional[str] = None,
         allowed_local_media_path: str = "",
         revision: Optional[str] = None,
         code_revision: Optional[str] = None,
@@ -259,6 +260,7 @@ def __init__(
         model_impl: Union[str, ModelImpl] = ModelImpl.AUTO,
     ) -> None:
         self.model = model
+        self.hf_config_path = hf_config_path
         self.tokenizer = tokenizer
         self.tokenizer_mode = tokenizer_mode
         self.trust_remote_code = trust_remote_code
@@ -321,8 +323,9 @@ def __init__(
         if self.enable_sleep_mode and not current_platform.is_cuda():
             raise ValueError("Sleep mode is only supported on CUDA devices.")
 
-        hf_config = get_config(self.model, trust_remote_code, revision,
-                               code_revision, config_format)
+        hf_config = get_config(self.hf_config_path or self.model,
+                               trust_remote_code, revision, code_revision,
+                               config_format)
 
         if hf_overrides_kw:
             logger.info("Overriding HF config with %s", hf_overrides_kw)
@@ -947,7 +950,7 @@ def get_multimodal_config(self) -> "MultiModalConfig":
     def try_get_generation_config(self) -> Dict[str, Any]:
         if self.generation_config is None or self.generation_config == "auto":
             config = try_get_generation_config(
-                self.model,
+                self.hf_config_path or self.model,
                 trust_remote_code=self.trust_remote_code,
                 revision=self.revision,
             )

vllm/engine/arg_utils.py

@@ -93,6 +93,7 @@ class EngineArgs:
     model: str = 'facebook/opt-125m'
     served_model_name: Optional[Union[str, List[str]]] = None
     tokenizer: Optional[str] = None
+    hf_config_path: Optional[str] = None
     task: TaskOption = "auto"
     skip_tokenizer_init: bool = False
     tokenizer_mode: str = 'auto'
@@ -262,6 +263,12 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             default=EngineArgs.tokenizer,
             help='Name or path of the huggingface tokenizer to use. '
             'If unspecified, model name or path will be used.')
+        parser.add_argument(
+            "--hf-config-path",
+            type=nullable_str,
+            default=EngineArgs.hf_config_path,
+            help='Name or path of the huggingface config to use. '
+            'If unspecified, model name or path will be used.')
         parser.add_argument(
             '--skip-tokenizer-init',
             action='store_true',
@@ -1076,6 +1083,7 @@ def create_model_config(self) -> ModelConfig:
 
         return ModelConfig(
             model=self.model,
+            hf_config_path=self.hf_config_path,
             task=self.task,
             # We know this is not None because we set it in __post_init__
             tokenizer=cast(str, self.tokenizer),

vllm/model_executor/layers/fused_moe/layer.py

@@ -5,6 +5,7 @@
 from typing import Callable, List, Optional, Tuple
 
 import torch
+from torch.nn.parameter import UninitializedParameter
 
 import vllm.envs as envs
 from vllm.distributed import (get_tensor_model_parallel_rank,
@@ -514,7 +515,12 @@ def weight_loader(self, param: torch.nn.Parameter,
         # dimension intermediate_size_per_partition is used.
         SHARD_ID_TO_SHARDED_DIM = {"w1": 0, "w2": 1, "w3": 0}
 
-        expert_data = param.data[expert_id]
+        is_gguf_weight = getattr(param, "is_gguf_weight", False)
+        is_gguf_weight_type = getattr(param, "is_gguf_weight_type", False)
+        if is_gguf_weight_type:
+            param.weight_type = loaded_weight.item()
+            param.data.copy_(loaded_weight)
+            return
 
         # is_transposed: if the dim to shard the weight
         # should be flipped. Required by GPTQ, compressed-tensors
@@ -524,6 +530,20 @@ def weight_loader(self, param: torch.nn.Parameter,
         if is_transposed:
             shard_dim = int(not shard_dim)
 
+        full_load = len(loaded_weight.shape) == 3
+        if full_load:
+            shard_dim += 1
+
+        # Materialize GGUF UninitializedParameter
+        if is_gguf_weight and isinstance(param, UninitializedParameter):
+            final_shape = list(loaded_weight.shape)
+            if shard_id in ["w1", "w3"]:
+                final_shape[1] *= 2
+            final_shape[shard_dim] = final_shape[
+                shard_dim] // get_tensor_model_parallel_world_size()
+            param.materialize(final_shape, dtype=loaded_weight.dtype)
+
+        expert_data = param.data if full_load else param.data[expert_id]
         # Case input scale: input_scale loading is only supported for fp8
         if "input_scale" in weight_name:
             # this is needed for compressed-tensors only

vllm/model_executor/layers/linear.py

@@ -235,10 +235,23 @@ def __init__(self,
     def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
         # If the weight on disk does not have a shape, give it one
         # (such scales for AutoFp8).
+        # Special case for GGUF
+
+        is_gguf_weight = getattr(param, "is_gguf_weight", False)
+        is_gguf_weight_type = getattr(param, "is_gguf_weight_type", False)
+        if is_gguf_weight_type:
+            param.weight_type = loaded_weight.item()
+
+        # Materialize GGUF UninitializedParameter
+        if is_gguf_weight and isinstance(param, UninitializedParameter):
+            param.materialize(loaded_weight.shape, dtype=loaded_weight.dtype)
+
         if len(loaded_weight.shape) == 0:
             loaded_weight = loaded_weight.reshape(1)
 
-        assert param.size() == loaded_weight.size()
+        assert param.size() == loaded_weight.size(), (
+            f"Tried to load weights of size {loaded_weight.size()}"
+            f"to a parameter of size {param.size()}")
         param.data.copy_(loaded_weight)
 
     def forward(self,

vllm/model_executor/layers/quantization/gguf.py

@@ -1,13 +1,16 @@
 # SPDX-License-Identifier: Apache-2.0
 
-from typing import Any, Dict, List, Optional
+from typing import Any, Callable, Dict, List, Optional
 
 import gguf
 import torch
 from gguf import GGMLQuantizationType as WeightType
 from torch.nn.parameter import Parameter, UninitializedParameter
 
 from vllm import _custom_ops as ops
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.fused_moe.layer import (FusedMoE,
+                                                        FusedMoEMethodBase)
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
@@ -29,7 +32,7 @@ def get_name(self) -> str:
         return "gguf"
 
     def get_supported_act_dtypes(self) -> List[torch.dtype]:
-        return [torch.half, torch.bfloat16]
+        return [torch.half]
 
     @classmethod
     def get_min_capability(cls) -> int:
@@ -49,6 +52,8 @@ def get_quant_method(self, layer: torch.nn.Module,
             return GGUFLinearMethod(self)
         elif isinstance(layer, VocabParallelEmbedding):
             return GGUFEmbeddingMethod(self)
+        elif isinstance(layer, FusedMoE):
+            return GGUFMoEMethod(self)
         return None
 
 
@@ -184,6 +189,124 @@ def apply(self,
         return out
 
 
+class GGUFMoEMethod(FusedMoEMethodBase):
+    """MoE method for GGUF.
+
+    Args:
+        quant_config: The GGUF quantization config.
+    """
+
+    def __init__(self, quant_config: GGUFConfig):
+        self.quant_config = quant_config
+
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size_per_partition: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+
+        tensor_shape = (num_experts, 2 * intermediate_size_per_partition,
+                        hidden_size)
+        #gate up proj
+        w13_qweight = GGUFUninitializedParameter(requires_grad=False)
+        set_weight_attrs(
+            w13_qweight, {
+                "input_dim": 1,
+                "output_dim": 0,
+                "tensor_shape": tensor_shape,
+                "is_gguf_weight": True,
+                "data_container": [],
+            })
+        set_weight_attrs(w13_qweight, extra_weight_attrs)
+        layer.register_parameter("w13_qweight", w13_qweight)
+
+        w13_qweight_type = Parameter(torch.empty(1, dtype=torch.uint8),
+                                     requires_grad=False)
+        set_weight_attrs(w13_qweight_type, {
+            "is_gguf_weight_type": True,
+            "weight_type": 0,
+            "ignore_warning": True
+        })
+        set_weight_attrs(w13_qweight_type, extra_weight_attrs)
+        layer.register_parameter("w13_qweight_type", w13_qweight_type)
+
+        tensor_shape = (num_experts, intermediate_size_per_partition,
+                        hidden_size)
+        #gate down proj
+        w2_qweight = GGUFUninitializedParameter(requires_grad=False)
+        set_weight_attrs(
+            w2_qweight, {
+                "input_dim": 1,
+                "output_dim": 0,
+                "tensor_shape": tensor_shape,
+                "is_gguf_weight": True,
+                "data_container": [],
+            })
+        set_weight_attrs(w2_qweight, extra_weight_attrs)
+        layer.register_parameter("w2_qweight", w2_qweight)
+
+        w2_qweight_type = Parameter(torch.empty(1, dtype=torch.uint8),
+                                    requires_grad=False)
+        set_weight_attrs(w2_qweight_type, {
+            "is_gguf_weight_type": True,
+            "weight_type": 0,
+            "ignore_warning": True
+        })
+
+        set_weight_attrs(w2_qweight_type, extra_weight_attrs)
+        layer.register_parameter("w2_qweight_type", w2_qweight_type)
+        self.act = SiluAndMul()
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool,
+        use_grouped_topk: bool = False,
+        topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
+        global_num_experts: int = -1,
+        expert_map: Optional[torch.Tensor] = None,
+        custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        e_score_correction_bias: Optional[torch.Tensor] = None,
+        activation: str = "silu",
+    ):
+        assert activation == "silu", "Only SiLU activation is supported."
+        topk_weights, topk_ids = FusedMoE.select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function,
+            scoring_func=scoring_func,
+            e_score_correction_bias=e_score_correction_bias)
+        final_hidden_states = torch.empty_like(x)
+        for tok, (w, idx) in enumerate(zip(topk_weights, topk_ids)):
+            inp = x[tok].reshape((1, ) + x.shape[1:])
+            current_hidden_state = None
+            for ww, ii in zip(w, idx):
+                expert_up = layer.w13_qweight[ii]
+
+                out = _fuse_mul_mat(inp, expert_up,
+                                    layer.w13_qweight_type.weight_type)
+                out = self.act(out)
+
+                expert_down = layer.w2_qweight[ii]
+                current_state = _fuse_mul_mat(
+                    out, expert_down,
+                    layer.w2_qweight_type.weight_type).mul_(ww)
+                if current_hidden_state is None:
+                    current_hidden_state = current_state
+                else:
+                    current_hidden_state.add_(current_state)
+            final_hidden_states[tok] = current_hidden_state
+        return final_hidden_states
+
+
 class GGUFEmbeddingMethod(GGUFLinearMethod):
     """Embedding method for GGUF.
 

vllm/model_executor/model_loader/loader.py

@@ -1245,9 +1245,24 @@ def _get_gguf_weights_map(self, model_config: ModelConfig):
         """
         config = model_config.hf_config
         model_type = config.model_type
+        gguf_to_hf_name_map = {}
         # hack: ggufs have a different name than transformers
         if model_type == "cohere":
             model_type = "command-r"
+        if model_type in ("deepseek_v3", "deepseek_v2"):
+            model_type = "deepseek2"
+            # GGUF layer map assumes that we will have a merged expert weights
+            # so we need to map them manually
+            for idx in range(config.num_hidden_layers):
+                gguf_to_hf_name_map[f"blk.{idx}.exp_probs_b.bias"] = \
+                        f"model.layers.{idx}.mlp.gate.e_score_correction_bias"
+                gguf_to_hf_name_map[f"blk.{idx}.ffn_down_exps.weight"] = \
+                        f"model.layers.{idx}.mlp.experts.0.down_proj.weight"
+                gguf_to_hf_name_map[f"blk.{idx}.ffn_gate_exps.weight"] = \
+                        f"model.layers.{idx}.mlp.experts.0.gate_proj.weight"
+                gguf_to_hf_name_map[f"blk.{idx}.ffn_up_exps.weight"] = \
+                        f"model.layers.{idx}.mlp.experts.0.up_proj.weight"
+
         arch = None
         for key, value in gguf.MODEL_ARCH_NAMES.items():
             if value == model_type:
@@ -1258,10 +1273,10 @@ def _get_gguf_weights_map(self, model_config: ModelConfig):
         num_layers = config.num_hidden_layers
         name_map = gguf.get_tensor_name_map(arch, num_layers)
         with torch.device("meta"):
-            dummy_model = AutoModelForCausalLM.from_config(config)
+            dummy_model = AutoModelForCausalLM.from_config(
+                config, trust_remote_code=model_config.trust_remote_code)
         state_dict = dummy_model.state_dict()
 
-        gguf_to_hf_name_map = {}
         for hf_name in state_dict:
             name, suffix = hf_name.rsplit(".", 1)
             gguf_name = name_map.get_name(name)

vllm/model_executor/model_loader/weight_utils.py

@@ -496,7 +496,6 @@ def gguf_quant_weights_iterator(
             weight = tensor.data
             weight_type = tensor.tensor_type
             name = gguf_to_hf_name_map[tensor.name]
-
             if weight_type.name != "F32":
                 name = name.replace("weight", "qweight")
             param = torch.tensor(weight)