Merge tag 'v0.8.3' into v0.8.3-dev

vllm/model_executor/layers/quantization/gptq_marlin.py

@@ -592,9 +592,14 @@ class GPTQMarlinMoEMethod(FusedMoEMethodBase):
         custom_routing_function: Optional[Callable] = None,
         scoring_func: str = "softmax",
         e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
         activation: str = "silu",
     ) -> torch.Tensor:
         assert activation == "silu", "Only SiLU activation is supported."
+        if apply_router_weight_on_input is not None:
+            raise NotImplementedError(
+                "Apply router weight on input is not supported for"
+                "fused Marlin MoE method.")
 
         # The input must currently be float16
         orig_dtype = x.dtype

vllm/model_executor/layers/quantization/kernels/scaled_mm/__init__.py

@@ -3,6 +3,8 @@
 import os
 from typing import Dict, List, Optional, Type
 
+from vllm.model_executor.layers.quantization.kernels.scaled_mm.aiter import (
+    AiterScaledMMLinearKernel)
 from vllm.model_executor.layers.quantization.kernels.scaled_mm.cutlass import (
     CutlassScaledMMLinearKernel)
 from vllm.model_executor.layers.quantization.kernels.scaled_mm.ScaledMMLinearKernel import (  # noqa: E501
@@ -17,7 +19,7 @@ from vllm.platforms import PlatformEnum, current_platform
 _POSSIBLE_KERNELS: Dict[PlatformEnum, List[Type[ScaledMMLinearKernel]]] = {
     PlatformEnum.CPU: [CutlassScaledMMLinearKernel],
     PlatformEnum.CUDA: [CutlassScaledMMLinearKernel],
-    PlatformEnum.ROCM: [TritonScaledMMLinearKernel],
+    PlatformEnum.ROCM: [AiterScaledMMLinearKernel, TritonScaledMMLinearKernel],
     PlatformEnum.TPU: [XLAScaledMMLinearKernel],
 }
 

vllm/model_executor/layers/quantization/kernels/scaled_mm/aiter.py

+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Optional, Tuple
+
+import torch
+
+import vllm.envs as envs
+from vllm import _custom_ops as ops
+from vllm.platforms import current_platform
+
+from .cutlass import CutlassScaledMMLinearKernel
+from .ScaledMMLinearKernel import ScaledMMLinearLayerConfig
+
+
+class AiterScaledMMLinearKernel(CutlassScaledMMLinearKernel):
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 90
+
+    @classmethod
+    def can_implement(
+            cls, c: ScaledMMLinearLayerConfig) -> Tuple[bool, Optional[str]]:
+        if not current_platform.is_rocm():
+            return (
+                False,
+                "AiterScaledMMLinearKernel requires `aiter` which is not " +
+                "currently supported on non-ROCm platform.")
+
+        try:
+            import aiter  # noqa: F401 # deliberately attempt to import aiter
+        except Exception:
+            return (
+                False,
+                "AiterScaledMMLinearKernel requires `aiter` which is not " +
+                "installed on ROCm.")
+        # Check if rocm_aiter_gemm_w8a8_scaled_mm is enabled
+        if not (
+            envs.VLLM_ROCM_USE_AITER_LINEAR \
+            and envs.VLLM_ROCM_USE_AITER
+        ):
+            return (False, "AiterScaledMMLinearKernel is disabled. " +
+                    "Enable by setting `VLLM_ROCM_USE_AITER=1` " +
+                    "and `VLLM_ROCM_USE_AITER_LINEAR=1`. " +
+                    "`VLLM_ROCM_USE_AITER_LINEAR` default is True.")
+
+        if not c.input_symmetric:
+            return (False,
+                    "AiterScaledMMLinearKernel only supports symmetric " +
+                    "quantization.")
+        return True, None
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        super().process_weights_after_loading(layer)
+
+    def apply_weights(self,
+                      layer: torch.nn.Module,
+                      x: torch.Tensor,
+                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        """
+        `AiterScaledMMLinearKernel` implements a fused version of
+            `output = torch.mm((scale_a * a), (scale_b * b)).to(out_dtype)`
+        where scale_a * a and scale_b * b are implemented using numpy-style
+        broadcasting.
+        Currently only support per-tensor-per-tensor GEMM
+        and per-token-per-channel GEMM through AITER
+        w8a8 scaled gemm. `AiterScaledMMLinearKernel` also does not support
+        ATIER block scaled GEMM and mix-precision GEMM.
+        """
+        w_q, w_s, i_s, i_zp, azp_adj = self._get_weight_params(layer)
+
+        # ops.scaled_int8_quant supports both dynamic and static quant:
+        # * dynamic, i_s is None and x_s computed from x.
+        # * static, i_s is scalar and x_s is i_s.
+        symmetric = azp_adj is None
+        assert symmetric, ("AiterScaledMMLinearKernel only supports"
+                           " symmetric quantization.")
+        x_q, x_s, x_zp = ops.scaled_int8_quant(x,
+                                               i_s,
+                                               i_zp,
+                                               symmetric=symmetric)
+
+        assert x_zp is None, ("AiterScaledMMLinearKernel only supports"
+                              " symmetric quantization.")
+        out_dtype = x.dtype
+
+        assert (w_q.shape[0] % 16 == 0 and w_q.shape[1] % 16 == 0)
+        assert (out_dtype is torch.bfloat16 or out_dtype is torch.float16)
+        assert bias is None or bias.shape[0] == w_q.shape[
+            1] and bias.dtype == out_dtype
+
+        m = x_q.shape[0]  # a
+        n = w_q.shape[1]  # b
+
+        per_tensor_scale_a = (x_s.numel() == 1)
+        per_tensor_scale_b = (w_s.numel() == 1)
+        per_token_scale_a = (x_s.numel() == m)
+        per_channel_scale_b = (w_s.numel() == n)
+
+        # @TODO:
+        # Maybe broadcast the per-tensor-scale into per-channel-scale
+        # if one of the scale is a per-channel-scale.
+        # For now, it only supports:
+        # - per-tensor-per-tensor a8w8 scaled GEMM, and
+        # - per-token-per-channel a8w8 scaled GEMM
+        assert ((per_tensor_scale_a and per_tensor_scale_b)
+                or (per_token_scale_a and per_channel_scale_b)), (
+                    "Currently only support per-tensor-per-tensor GEMM " +
+                    " and per-token-per-channel GEMM through AITER"
+                    " w8a8 scaled gemm. `AiterScaledMMLinearKernel` " +
+                    "does not support AITER block scaled GEMM.")
+
+        from aiter import gemm_a8w8_CK
+
+        # gemm_a8w8_CK(a, b, scale_a, scale_b, bias) expects
+        # a to be [M, K]
+        # b to be [N, K]
+        # CutlassScaledMMLinearKernel prepare weight `w_q` in [K, N] format
+        return gemm_a8w8_CK(x_q, w_q.t(), x_s, w_s, bias).to(out_dtype)

vllm/model_executor/layers/quantization/kernels/scaled_mm/xla.py

@@ -97,7 +97,8 @@ class XLAScaledMMLinearKernel(ScaledMMLinearKernel):
                                              block_size=-1,
                                              int4_weight=False,
                                              quantize_activation=True)
-
+        # `quantized_matmul` output is fp32, cast it down to bf16 for perf
+        out = out.to(x.dtype)
         # Explicitly capture control flow to make dynamo happy.
         # https://pytorch.org/docs/main/generated/exportdb/index.html#cond-branch-class-method # noqa: E501
         return cond(bias is None, self.no_add_bias, self.add_bias, [out, bias])

vllm/model_executor/layers/quantization/moe_wna16.py

@@ -293,6 +293,7 @@ class MoeWNA16Method(FusedMoEMethodBase):
         custom_routing_function: Optional[Callable] = None,
         scoring_func: str = "softmax",
         e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
         activation: str = "silu",
         use_nn_moe: Optional[bool] = False,
         moe_ep_size: Optional[int] = None,
@@ -316,23 +317,24 @@ class MoeWNA16Method(FusedMoEMethodBase):
         weight_bits = self.quant_config.weight_bits
         has_zp = self.quant_config.has_zp
 
-        return fused_experts(x,
-                             layer.w13_qweight,
-                             layer.w2_qweight,
-                             topk_weights=topk_weights,
-                             topk_ids=topk_ids,
-                             inplace=True,
-                             use_int4_w4a16=weight_bits == 4,
-                             use_int8_w8a16=weight_bits == 8,
-                             global_num_experts=global_num_experts,
-                             expert_map=expert_map,
-                             w1_scale=layer.w13_scales,
-                             w2_scale=layer.w2_scales,
-                             w1_zp=layer.w13_qzeros if has_zp else None,
-                             w2_zp=layer.w2_qzeros if has_zp else None,
-                             block_shape=[0, layer.group_size],
-                             use_nn_moe=False,
-                             )
+        return fused_experts(
+            x,
+            layer.w13_qweight,
+            layer.w2_qweight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            inplace=True,
+            use_int4_w4a16=weight_bits == 4,
+            use_int8_w8a16=weight_bits == 8,
+            global_num_experts=global_num_experts,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+            expert_map=expert_map,
+            w1_scale=layer.w13_scales,
+            w2_scale=layer.w2_scales,
+            w1_zp=layer.w13_qzeros if has_zp else None,
+            w2_zp=layer.w2_qzeros if has_zp else None,
+            block_shape=[0, layer.group_size],
+            use_nn_moe=False)
 
     @staticmethod
     def get_weight_loader(layer, weight_loader):

vllm/model_executor/layers/quantization/quark/quark_moe.py

@@ -202,6 +202,8 @@ class QuarkW8A8Fp8MoEMethod(QuarkMoEMethod):
         custom_routing_function: Optional[Callable] = None,
         scoring_func: str = "softmax",
         e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
     ) -> torch.Tensor:
         from vllm.model_executor.layers.fused_moe import fused_experts
 
@@ -217,16 +219,18 @@ class QuarkW8A8Fp8MoEMethod(QuarkMoEMethod):
             scoring_func=scoring_func,
             e_score_correction_bias=e_score_correction_bias)
 
-        return fused_experts(x,
-                             layer.w13_weight,
-                             layer.w2_weight,
-                             topk_weights=topk_weights,
-                             topk_ids=topk_ids,
-                             inplace=True,
-                             use_fp8_w8a8=True,
-                             global_num_experts=global_num_experts,
-                             expert_map=expert_map,
-                             w1_scale=layer.w13_weight_scale,
-                             w2_scale=layer.w2_weight_scale,
-                             a1_scale=layer.w13_input_scale,
-                             a2_scale=layer.w2_input_scale)
+        return fused_experts(
+            x,
+            layer.w13_weight,
+            layer.w2_weight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            inplace=True,
+            use_fp8_w8a8=True,
+            global_num_experts=global_num_experts,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+            expert_map=expert_map,
+            w1_scale=layer.w13_weight_scale,
+            w2_scale=layer.w2_weight_scale,
+            a1_scale=layer.w13_input_scale,
+            a2_scale=layer.w2_input_scale)

vllm/model_executor/layers/quantization/quark/schemes/quark_w8a8_fp8.py

@@ -22,6 +22,7 @@ class QuarkW8A8Fp8(QuarkScheme):
         self.qscheme = qscheme
         self.is_static_input_scheme = is_static_input_scheme
         self.fp8_linear = Fp8LinearOp(use_per_token_if_dynamic=True)
+        self.out_dtype = torch.get_default_dtype()
 
     @classmethod
     def get_min_capability(cls) -> int:
@@ -134,5 +135,6 @@ class QuarkW8A8Fp8(QuarkScheme):
         return self.fp8_linear.apply(input=x,
                                      weight=layer.weight,
                                      weight_scale=layer.weight_scale,
+                                     out_dtype=self.out_dtype,
                                      input_scale=layer.input_scale,
                                      bias=bias)

vllm/model_executor/layers/quantization/utils/w8a8_utils.py

@@ -51,6 +51,16 @@ def cutlass_block_fp8_supported() -> bool:
     return ops.cutlass_scaled_mm_supports_block_fp8(capability)
 
 
+def cutlass_group_gemm_supported() -> bool:
+    if not current_platform.is_cuda():
+        return False
+
+    capability_tuple = current_platform.get_device_capability()
+    capability = -1 if capability_tuple is None else capability_tuple.to_int()
+
+    return ops.cutlass_group_gemm_supported(capability)
+
+
 CUTLASS_FP8_SUPPORTED = cutlass_fp8_supported()
 CUTLASS_BLOCK_FP8_SUPPORTED = cutlass_block_fp8_supported()
 
@@ -154,6 +164,7 @@ class Fp8LinearOp:
         input: torch.Tensor,
         weight: torch.Tensor,
         weight_scale: torch.Tensor,
+        out_dtype: Optional[torch.dtype] = None,
         input_scale: Optional[torch.Tensor] = None,
         input_scale_ub: Optional[torch.Tensor] = None,
         bias: Optional[torch.Tensor] = None,
@@ -173,8 +184,13 @@ class Fp8LinearOp:
         if use_per_token_if_dynamic is None:
             use_per_token_if_dynamic = self.use_per_token_if_dynamic
 
+        if out_dtype is None:
+            out_dtype = input.dtype
+
         # cutlass_scaled_mm supports per tensor/channel W and per tensor/token A
         if self.cutlass_fp8_supported:
+            assert input.dtype != current_platform.fp8_dtype(
+            ), "FP8 input to cutlass is not currently implemented"
             qinput, x_scale = ops.scaled_fp8_quant(
                 input_2d,
                 input_scale,
@@ -184,7 +200,7 @@ class Fp8LinearOp:
             # Fused GEMM_DQ
             output = ops.cutlass_scaled_mm(qinput,
                                            weight,
-                                           out_dtype=input.dtype,
+                                           out_dtype=out_dtype,
                                            scale_a=x_scale,
                                            scale_b=weight_scale,
                                            bias=bias)
@@ -193,12 +209,15 @@ class Fp8LinearOp:
         # torch.scaled_mm supports per tensor weights + activations only
         # so fallback to naive if per channel or per token
         else:
-            # Maybe apply padding to output, see comment in __init__
-            qinput, x_scale = ops.scaled_fp8_quant(
-                input_2d,
-                input_scale,
-                num_token_padding=self.output_padding,
-                use_per_token_if_dynamic=use_per_token_if_dynamic)
+            if input.dtype != current_platform.fp8_dtype():
+                # Maybe apply padding to output, see comment in __init__
+                qinput, x_scale = ops.scaled_fp8_quant(
+                    input_2d,
+                    input_scale,
+                    num_token_padding=self.output_padding,
+                    use_per_token_if_dynamic=use_per_token_if_dynamic)
+            else:
+                qinput, x_scale = input_2d, input_scale
 
             per_tensor_weights = (weight_scale.numel() == 1)
             per_tensor_activations = (x_scale.numel() == 1)
@@ -207,7 +226,7 @@ class Fp8LinearOp:
                 # Fused GEMM_DQ
                 output = torch._scaled_mm(qinput,
                                           weight,
-                                          out_dtype=input.dtype,
+                                          out_dtype=out_dtype,
                                           scale_a=x_scale,
                                           scale_b=weight_scale,
                                           bias=bias)
@@ -231,7 +250,7 @@ class Fp8LinearOp:
                 # Fused GEMM_DQ Rowwise GEMM
                 output = torch._scaled_mm(qinput,
                                           weight,
-                                          out_dtype=input.dtype,
+                                          out_dtype=out_dtype,
                                           scale_a=x_scale,
                                           scale_b=weight_scale.t(),
                                           bias=bias)

vllm/model_executor/layers/rotary_embedding.py

@@ -851,6 +851,70 @@ class Llama3RotaryEmbedding(RotaryEmbedding):
         return new_freqs
 
 
+class Llama4VisionRotaryEmbedding(RotaryEmbedding):
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position_embeddings: int,
+        base: int,
+        is_neox_style: bool,
+        dtype: torch.dtype,
+    ):
+        super().__init__(head_size, rotary_dim, max_position_embeddings, base,
+                         is_neox_style, dtype)
+
+    def _compute_inv_freq(self, base: Union[int, float]) -> torch.Tensor:
+        inv_freqs = super()._compute_inv_freq(base)
+        inv_freqs = inv_freqs[:(self.rotary_dim // 2)]
+        return inv_freqs
+
+    def _compute_cos_sin_cache(self) -> torch.Tensor:
+        inv_freq = self._compute_inv_freq(self.base)
+
+        # self.max_position_embeddings here is number of image patches
+        # i.e. (image_size // patch_size) ** 2
+        num_patches = self.max_position_embeddings
+        img_idx = torch.arange(num_patches,
+                    dtype=torch.int32) \
+                    .reshape(num_patches, 1)
+        img_idx = torch.cat([img_idx, img_idx[:1]], dim=0)
+        img_idx[-1, -1] = -2  # set to ID_CLS_TOKEN
+        num_patches_single_dim = int(math.sqrt(num_patches))
+        frequencies_x = img_idx % num_patches_single_dim
+        frequencies_y = img_idx // num_patches_single_dim
+        freqs_x = ((frequencies_x + 1)[..., None] *
+                   inv_freq[None, None, :]).repeat_interleave(2, dim=-1)
+        freqs_y = ((frequencies_y + 1)[..., None] *
+                   inv_freq[None, None, :]).repeat_interleave(2, dim=-1)
+        freqs = torch.cat([freqs_x, freqs_y],
+                          dim=-1).float().contiguous()[..., ::2]
+        freqs = freqs.masked_fill(img_idx.reshape(-1, 1, 1) < 0, 0)
+        cache = torch.view_as_complex(
+            torch.stack([torch.cos(freqs), torch.sin(freqs)], dim=-1))
+        return cache
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        self.cos_sin_cache: torch.Tensor = self.cos_sin_cache.to(query.device)
+        query_ = torch.view_as_complex(query.float().reshape(
+            *query.shape[:-1], -1, 2))
+        key_ = torch.view_as_complex(key.float().reshape(
+            *key.shape[:-1], -1, 2))
+        broadcast_shape = [
+            d if i == 1 or i == (query_.ndim - 1) else 1
+            for i, d in enumerate(query_.shape)
+        ]
+        freqs_ci = self.cos_sin_cache.view(*broadcast_shape)
+        query_out = torch.view_as_real(query_ * freqs_ci).flatten(3)
+        key_out = torch.view_as_real(key_ * freqs_ci).flatten(3)
+        return query_out.type_as(query), key_out.type_as(key)
+
+
 class MRotaryEmbedding(RotaryEmbedding):
     """Rotary Embedding with Multimodal Sections."""
 
@@ -1130,6 +1194,10 @@ def get_rope(
                                                scaling_factor, low_freq_factor,
                                                high_freq_factor,
                                                original_max_position)
+        elif scaling_type == "mllama4":
+            rotary_emb = Llama4VisionRotaryEmbedding(head_size, rotary_dim,
+                                                     max_position, base,
+                                                     is_neox_style, dtype)
         elif scaling_type == "default":
             if "mrope_section" in rope_scaling:
                 rotary_emb = MRotaryEmbedding(

vllm/model_executor/layers/vocab_parallel_embedding.py

@@ -250,7 +250,7 @@ class VocabParallelEmbedding(torch.nn.Module):
         # If we are making an embedding layer, then our quantization linear
         # method must implement the embedding operation. If we are another
         # layer type like ParallelLMHead, this is not important.
-        is_embedding_layer = type(self.__class__) is VocabParallelEmbedding
+        is_embedding_layer = type(self) is VocabParallelEmbedding
         quant_method_implements_embedding = method_has_implemented_embedding(
             type(quant_method))
         if is_embedding_layer and not quant_method_implements_embedding:

vllm/model_executor/model_loader/loader.py

@@ -1261,6 +1261,8 @@ class BitsAndBytesModelLoader(BaseModelLoader):
                                          pack_ratio)
 
                 offsets = np.concatenate(([0], np.cumsum(num_elements)))
+                # Make torch infer_schema happy
+                offsets = torch.tensor(offsets).cpu()
                 set_weight_attrs(param, {"bnb_shard_offsets": offsets})
 
                 if load_8bit:

vllm/model_executor/model_loader/utils.py

@@ -37,16 +37,13 @@ def is_transformers_impl_compatible(
     mod = module or getattr(transformers, arch, None)
     if mod is None:
         return False
-    if hasattr(mod, "supports_backend"):
-        return mod.is_backend_compatible()
-    else:
-        return mod._supports_flex_attn
+    return mod.is_backend_compatible()
 
 
-def resolve_transformers_fallback(model_config: ModelConfig,
-                                  architectures: list[str]):
+def resolve_transformers_arch(model_config: ModelConfig,
+                              architectures: list[str]):
     for i, arch in enumerate(architectures):
-        if arch == "TransformersModel":
+        if arch == "TransformersForCausalLM":
             continue
         auto_map: dict[str, str] = getattr(model_config.hf_config, "auto_map",
                                            None) or dict()
@@ -70,7 +67,7 @@ def resolve_transformers_fallback(model_config: ModelConfig,
                 raise ValueError(
                     f"The Transformers implementation of {arch} is not "
                     "compatible with vLLM.")
-            architectures[i] = "TransformersModel"
+            architectures[i] = "TransformersForCausalLM"
         if model_config.model_impl == ModelImpl.AUTO:
             if not is_transformers_impl_compatible(arch, custom_model_module):
                 raise ValueError(
@@ -81,7 +78,7 @@ def resolve_transformers_fallback(model_config: ModelConfig,
                 "%s has no vLLM implementation, falling back to Transformers "
                 "implementation. Some features may not be supported and "
                 "performance may not be optimal.", arch)
-            architectures[i] = "TransformersModel"
+            architectures[i] = "TransformersForCausalLM"
     return architectures
 
 
@@ -140,8 +137,7 @@ def get_model_architecture(
                             for arch in architectures)
     if (not is_vllm_supported
             or model_config.model_impl == ModelImpl.TRANSFORMERS):
-        architectures = resolve_transformers_fallback(model_config,
-                                                      architectures)
+        architectures = resolve_transformers_arch(model_config, architectures)
 
     model_cls, arch = ModelRegistry.resolve_model_cls(architectures)
     if model_config.task == "embed":

vllm/model_executor/models/adapters.py

@@ -99,16 +99,17 @@ def _create_pooling_model_cls(
                     mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
                     weights = mapper.apply(weights)
 
-                    self.model.load_weights(weights)
-                    return
+                    loaded_params = self.model.load_weights(weights)
+                    loaded_params = {f"model.{name}" for name in loaded_params}
+                    return loaded_params
 
             # For most other models
             if hasattr(orig_cls, "load_weights"):
-                orig_cls.load_weights(self, weights)  # type: ignore
+                return orig_cls.load_weights(self, weights)  # type: ignore
             # Fallback
             else:
                 loader = AutoWeightsLoader(self)
-                loader.load_weights(weights)
+                return loader.load_weights(weights)
 
     return ModelForPooling  # type: ignore
 

vllm/model_executor/models/aya_vision.py

+# SPDX-License-Identifier: Apache-2.0 Adapted from
+# https://github.com/huggingface/transformers/tree/main/src/transformers/models/aya_vision
+from functools import cached_property
+from typing import (Iterable, Literal, Mapping, Optional, Sequence, Set, Tuple,
+                    TypedDict, Union, cast)
+
+import torch
+from torch import nn
+from transformers import BatchFeature, GotOcr2ImageProcessor
+from transformers.activations import ACT2FN
+from transformers.image_processing_utils import get_size_dict
+from transformers.models.aya_vision import AyaVisionConfig
+from transformers.models.aya_vision.processing_aya_vision import (
+    AyaVisionProcessor)
+from transformers.models.got_ocr2.image_processing_got_ocr2 import (
+    get_optimal_tiled_canvas)
+
+from vllm.config import VllmConfig
+from vllm.jsontree import json_map_leaves
+from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import MultiModalKwargs
+from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
+                                   MultiModalDataItems)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo,
+                                        MultiModalFieldConfig,
+                                        PromptReplacement, PromptUpdate,
+                                        encode_tokens)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .siglip import SiglipVisionModel
+from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
+                    maybe_prefix, merge_multimodal_embeddings)
+from .vision import scatter_patch_features, select_patch_features
+
+
+class AyaVisionImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    pixel_values: torch.Tensor
+    """
+    Shape: `(num_patches_total, num_channels, height, width)`
+
+    `num_patches_total` is the total number of patches over each image over each
+    prompt in the batch.
+    """
+
+    num_patches: torch.Tensor
+    """Shape: `(batch_size * num_images)`"""
+
+    embed_is_patch: Union[torch.Tensor, list[torch.Tensor]]
+    """
+    A boolean mask indicating which image embeddings correspond to patch tokens.
+
+    Shape: `(batch_size * num_images, num_embeds)`
+    """
+
+
+class AyaVisionMultiModalProjector(nn.Module):
+
+    def __init__(self, config: AyaVisionConfig):
+        super().__init__()
+        self.config = config
+        self.downsample_factor = config.downsample_factor
+        self.alignment_intermediate_size = getattr(
+            config, "alignment_intermediate_size",
+            config.text_config.hidden_size)
+        self.layernorm = nn.LayerNorm(config.vision_config.hidden_size *
+                                      (config.downsample_factor**2),
+                                      eps=config.adapter_layer_norm_eps)
+
+        self.linear_1 = nn.Linear(
+            config.vision_config.hidden_size * (config.downsample_factor**2),
+            self.alignment_intermediate_size,
+            bias=True,
+        )
+
+        self.act = ACT2FN["silu"]  # SwiGLU uses SiLU activation
+        # For SwiGLU, project down to half size since we split intermediate dim
+        self.linear_2 = nn.Linear(self.alignment_intermediate_size // 2,
+                                  config.text_config.hidden_size,
+                                  bias=True)
+
+    def forward(self, image_features: torch.Tensor) -> torch.Tensor:
+        image_features = self.pixel_shuffle(image_features)
+        image_features = self.layernorm(image_features)
+        hidden_states = self.linear_1(image_features)
+
+        # Split along last dimension and apply SwiGLU
+        x, gate = hidden_states.chunk(2, dim=-1)
+        hidden_states = self.act(gate) * x
+
+        hidden_states = self.linear_2(hidden_states)
+        return hidden_states
+
+    def pixel_shuffle(self,
+                      image_features: torch.Tensor) -> torch.Tensor:  # B, S, D
+        batch_size, seq_length, _ = image_features.shape
+        height = width = int(seq_length**0.5)
+        image_features = image_features.reshape(image_features.shape[0], width,
+                                                height, -1)
+        channels = image_features.shape[-1]
+        image_features = image_features.reshape(
+            batch_size, width, int(height / self.downsample_factor),
+            int(channels * self.downsample_factor))
+        image_features = image_features.permute(0, 2, 1, 3)
+        image_features = image_features.reshape(
+            batch_size, int(height / self.downsample_factor),
+            int(width / self.downsample_factor), -1)
+        image_features = image_features.permute(0, 2, 1, 3)
+        return image_features
+
+
+class AyaVisionProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self) -> AyaVisionConfig:
+        return self.ctx.get_hf_config(AyaVisionConfig)
+
+    def get_hf_processor(self, **kwargs: object) -> AyaVisionProcessor:
+        return self.ctx.get_hf_processor(AyaVisionProcessor, **kwargs)
+
+    def get_image_processor(self) -> GotOcr2ImageProcessor:
+        return self.get_hf_processor().image_processor
+
+    def get_mm_max_tokens_per_item(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> Mapping[str, int]:
+        return {"image": self.get_max_image_tokens()}
+
+    def get_max_image_tokens(self) -> int:
+        hf_processor = self.get_hf_processor()
+        image_processor = hf_processor.image_processor
+        image_size = self.get_image_size_with_most_features()
+        tokenizer = hf_processor.tokenizer
+        num_patches = self.get_num_patches(
+            image_width=image_size.width,
+            image_height=image_size.height,
+            size=image_processor.size,
+            min_patches=image_processor.min_patches,
+            max_patches=image_processor.max_patches)
+        image_string = hf_processor._prompt_split_image(num_patches)
+        x = encode_tokens(
+            tokenizer,
+            image_string,
+            add_special_tokens=False,
+        )
+        return len(x)
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        image_processor = self.get_image_processor()
+        height = image_processor.size['height']
+        width = image_processor.size['width']
+        max_patches = image_processor.max_patches
+        return ImageSize(height=height * max_patches,
+                         width=width * max_patches)
+
+    def get_num_patches(self, *, image_width: int, image_height: int,
+                        size: dict, min_patches: int, max_patches: int) -> int:
+        """
+        Calculate the number of patches needed for a given image based on size
+        constraints.  This method replicates and adjusts the logic from:
+        transformers/models/got_ocr2/image_processing_got_ocr2
+        """
+        size = get_size_dict(size, default_to_square=False)
+        num_columns, num_rows = get_optimal_tiled_canvas(
+            (image_height, image_width), (size["height"], size["width"]),
+            min_patches, max_patches)
+        num_blocks = num_columns * num_rows
+        return num_blocks if num_blocks == 1 else num_blocks + 1
+
+
+class AyaVisionDummyInputsBuilder(
+        BaseDummyInputsBuilder[AyaVisionProcessingInfo]):
+
+    def get_dummy_processor_inputs(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> ProcessorInputs:
+        processor = self.info.get_hf_processor()
+        image_token = processor.image_token
+
+        num_images = mm_counts.get("image", 0)
+        image_size = \
+            self.info.get_image_size_with_most_features()
+
+        mm_data = {
+            "image":
+            self._get_dummy_images(width=image_size.width,
+                                   height=image_size.height,
+                                   num_images=num_images)
+        }
+        return ProcessorInputs(
+            prompt_text=image_token * num_images,
+            mm_data=mm_data,
+        )
+
+
+class AyaVisionMultiModalProcessor(
+        BaseMultiModalProcessor[AyaVisionProcessingInfo]):
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        processed_outputs = super()._call_hf_processor(
+            prompt,
+            mm_data,
+            mm_kwargs,
+        )
+        hf_processor = self.info.get_hf_processor(**mm_kwargs)
+        image_processor = hf_processor.image_processor
+
+        hf_config = self.info.get_hf_config()
+        # HF processor pops the `num_patches` kwarg, which is needed by vLLM
+        if (images :=
+                mm_data.get("images")) is not None and '<image>' in prompt:
+            assert isinstance(images, list)
+            parsed_images = (self._get_data_parser().parse_mm_data({
+                "image":
+                images
+            }).get_items("image", ImageProcessorItems))
+            image_sizes = [
+                parsed_images.get_image_size(i)
+                for i in range(len(parsed_images))
+            ]
+            num_patches = [
+                self.info.get_num_patches(
+                    image_width=image_size.width,
+                    image_height=image_size.height,
+                    size=image_processor.size,
+                    min_patches=image_processor.min_patches,
+                    max_patches=image_processor.max_patches)
+                for image_size in image_sizes
+            ]
+            image_tokens_list = [
+                hf_processor._prompt_split_image(num_patch)
+                for num_patch in num_patches
+            ]
+            tokenizer = self.info.get_tokenizer()
+            image_token_ids = [
+                tokenizer.encode(image_tokens, add_special_tokens=False)
+                for image_tokens in image_tokens_list
+            ]
+            embed_is_patch = [
+                torch.tensor(image_repl_tokens) == hf_config.image_token_index
+                for image_repl_tokens in image_token_ids
+            ]
+            processed_outputs["embed_is_patch"] = embed_is_patch
+            processed_outputs["num_patches"] = torch.tensor(num_patches)
+
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        num_patches = hf_inputs.get("num_patches", torch.empty(0))
+        return dict(
+            pixel_values=MultiModalFieldConfig.flat_from_sizes(
+                "image", num_patches),
+            num_patches=MultiModalFieldConfig.batched("image"),
+            embed_is_patch=MultiModalFieldConfig.batched("image"),
+            image_embeds=MultiModalFieldConfig.batched("image"),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        image_token = hf_processor.image_token
+        image_processor = hf_processor.image_processor
+
+        def get_replacement(item_idx: int):
+            images: ImageProcessorItems = mm_items.get("image",
+                                                       ImageProcessorItems)
+            image_size: ImageSize = images.get_image_size(item_idx)
+            num_patches = self.info.get_num_patches(
+                image_width=image_size.width,
+                image_height=image_size.height,
+                size=image_processor.size,
+                min_patches=image_processor.min_patches,
+                max_patches=image_processor.max_patches)
+            return hf_processor._prompt_split_image(num_patches=num_patches)
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=image_token,
+                replacement=get_replacement,
+            )
+        ]
+
+
+def _get_num_hidden_layers(hf_config: AyaVisionConfig) -> int:
+    feature_layers = hf_config.vision_feature_layer
+    num_hidden_layers = hf_config.vision_config.num_hidden_layers
+    # If we have one feature layer, initialize up to that layer
+    if isinstance(feature_layers, int):
+        return _get_layer_index(feature_layers, num_hidden_layers)
+    # If we have multiple feature layers, initialize up to the deepest m
+    elif isinstance(feature_layers, (list, tuple)):
+        return max(
+            _get_layer_index(idx, num_hidden_layers) for idx in feature_layers)
+    raise TypeError(f"vision_layer_feature type: {type(feature_layers)}"
+                    " is not supported")
+
+
+def _get_layer_index(feature_layer_index: int, num_hidden_layers: int) -> int:
+    if feature_layer_index < 0:
+        return num_hidden_layers + feature_layer_index + 1
+    return feature_layer_index
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    AyaVisionMultiModalProcessor,
+    info=AyaVisionProcessingInfo,
+    dummy_inputs=AyaVisionDummyInputsBuilder)
+class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal,
+                                        SupportsPP):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config: AyaVisionConfig = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+        num_hidden_layers = _get_num_hidden_layers(config)
+        self.config = config
+        self.quant_config = quant_config
+        self.multimodal_config = multimodal_config
+
+        self.vision_tower = SiglipVisionModel(
+            config.vision_config,
+            quant_config,
+            num_hidden_layers_override=num_hidden_layers,
+            prefix=maybe_prefix(prefix, "vision_model"))
+        self.vocab_size = config.text_config.vocab_size
+        self.multi_modal_projector = AyaVisionMultiModalProjector(config)
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "model"),
+            # Cohere2ForCausalLM and CohereForCausalLM are the same on vllm
+            architectures=["Cohere2ForCausalLM"])
+
+    @property
+    def dtype(self):
+        return next(self.parameters()).dtype
+
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
+
+    def _image_pixels_to_features(self, vision_tower: SiglipVisionModel,
+                                  pixel_values: torch.Tensor,
+                                  **kwargs) -> torch.Tensor:
+        target_dtype = vision_tower.get_input_embeddings().weight.dtype
+        image_features = vision_tower(pixel_values.to(dtype=target_dtype),
+                                      **kwargs)
+
+        def select_features(leaf: torch.Tensor):
+            return self._select_image_features(
+                leaf,
+                strategy=self.config.vision_feature_select_strategy,
+            )
+
+        return cast(
+            Union[torch.Tensor, tuple[torch.Tensor, ...]],
+            json_map_leaves(select_features, image_features),
+        )
+
+    def _select_image_features(self, image_features: torch.Tensor, *,
+                               strategy: str) -> torch.Tensor:
+        if strategy == "default":
+            return image_features[:, 1:]
+        elif strategy == "full":
+            return image_features
+
+        raise ValueError(f"Unexpected select feature strategy: {strategy}")
+
+    def _process_image_input(self, image_input: AyaVisionImagePixelInputs,
+                             **kwargs) -> list[torch.Tensor]:
+        assert self.vision_tower is not None
+        pixel_values = image_input["pixel_values"]
+        num_patches = image_input["num_patches"]
+        image_features = self._image_pixels_to_features(
+            self.vision_tower, pixel_values=pixel_values)
+        image_embeds = self.multi_modal_projector(image_features)
+        return [
+            e.flatten(0, 2) for e in image_embeds.split(num_patches.tolist())
+        ]
+
+    def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
+        h = w = self.config.vision_config.image_size
+        expected_dims = (3, h, w)
+
+        def _validate_shape(d: torch.Tensor):
+            if d.shape != expected_dims:
+                raise ValueError(
+                    "The expected shape of pixel values per image per batch "
+                    f"is {expected_dims}. You supplied {tuple(d.shape)}.")
+
+        for d in data:
+            _validate_shape(d)
+
+        return data
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[AyaVisionImagePixelInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        num_patches = kwargs.pop("num_patches", None)
+        embed_is_patch = kwargs.pop("embed_is_patch", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+        assert image_embeds is None, "Aya Vision does not support image_embeds."
+
+        if not isinstance(pixel_values, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of pixel values. "
+                             f"Got type: {type(pixel_values)}")
+        if num_patches is not None and not isinstance(num_patches,
+                                                      (torch.Tensor, list)):
+            raise ValueError("Incorrect type of num_patches. "
+                             f"Got type: {type(num_patches)}")
+
+        if not isinstance(embed_is_patch, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of embed_is_patch. "
+                             f"Got type: {type(embed_is_patch)}")
+
+        pixel_values = flatten_bn(pixel_values, concat=True)
+        num_patches = flatten_bn(num_patches, concat=True)
+        embed_is_patch = flatten_bn(embed_is_patch)
+        return AyaVisionImagePixelInputs(
+            type="pixel_values",
+            pixel_values=self._validate_pixel_values(pixel_values),
+            num_patches=num_patches,
+            embed_is_patch=embed_is_patch,
+        )
+
+    def get_multimodal_embeddings(
+            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return None
+        image_features = self._process_image_input(image_input, **kwargs)
+        return scatter_patch_features(
+            image_features,
+            image_input["embed_is_patch"],
+        )
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids=input_ids,
+                inputs_embeds=inputs_embeds,
+                multimodal_embeddings=select_patch_features(
+                    multimodal_embeddings),
+                placeholder_token_id=self.config.image_token_index)
+
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
+
+        hidden_states = self.language_model.model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    @cached_property
+    def sampler(self):
+        if hasattr(self.language_model, "sampler"):
+            return self.language_model.sampler
+
+        return get_sampler()
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        return self.language_model.sample(logits, sampling_metadata)

vllm/model_executor/models/baichuan.py

@@ -51,7 +51,7 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsLoRA, SupportsPP, SupportsQuant
-from .utils import (is_pp_missing_parameter,
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers)
 
 from vllm import _custom_ops as ops
@@ -301,6 +301,16 @@ class BaiChuanModel(nn.Module):
         self.make_empty_intermediate_tensors = (
             make_empty_intermediate_tensors_factory(
                 ["hidden_states", "residual"], config.hidden_size))
+        
+        self.quant_method = None
+        if quant_config is not None:
+            self.quant_method=quant_config.get_name()
+            self.quant_config=quant_config
+
+        self.use_llama_nn = os.environ.get('LLAMA_NN') == '1'
+        self.use_gemm_pad = os.environ.get('GEMM_PAD') == '1'
+        self.use_fa_pad = os.environ.get('FA_PAD') == '1'
+        self.use_awq_pad = os.environ.get('AWQ_PAD') == '1'
 
     def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)
@@ -336,86 +346,6 @@ class BaiChuanModel(nn.Module):
         hidden_states, _ = self.norm(hidden_states, residual)
         return hidden_states
 
-
-class BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA, SupportsPP,
-                              SupportsQuant):
-    packed_modules_mapping = {
-        "W_pack": ["W_pack"],
-        "gate_up_proj": [
-            "gate_proj",
-            "up_proj",
-        ],
-    }
-
-    def __init__(
-        self,
-        *,
-        vllm_config: VllmConfig,
-        prefix: str = "",
-        position_embedding: str = "ROPE",
-    ):
-        super().__init__()
-        config = vllm_config.model_config.hf_config
-        quant_config = vllm_config.quant_config
-        lora_config = vllm_config.lora_config
-        self.config = config
-        self.lora_config = lora_config
-
-        self.quant_config = quant_config
-        self.model = BaiChuanModel(vllm_config=vllm_config,
-                                   prefix=prefix,
-                                   position_embedding=position_embedding)
-        self.lm_head = ParallelLMHead(config.vocab_size,
-                                      config.hidden_size,
-                                      quant_config=quant_config)
-        if self.config.tie_word_embeddings:
-            self.lm_head.weight = self.model.embed_tokens.weight
-        self.logits_processor = LogitsProcessor(config.vocab_size)
-        self.sampler = get_sampler()
-        self.make_empty_intermediate_tensors = (
-            self.model.make_empty_intermediate_tensors)
-        
-        self.quant_method = None
-        if quant_config is not None:
-            self.quant_method=quant_config.get_name()
-            self.quant_config=quant_config
-
-        self.use_llama_nn = os.environ.get('LLAMA_NN') == '1'
-        self.use_gemm_pad = os.environ.get('GEMM_PAD') == '1'
-        self.use_fa_pad = os.environ.get('FA_PAD') == '1'
-        self.use_awq_pad = os.environ.get('AWQ_PAD') == '1'
-
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.get_input_embeddings(input_ids)
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        intermediate_tensors: Optional[IntermediateTensors] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-    ) -> Union[torch.Tensor, IntermediateTensors]:
-        hidden_states = self.model(input_ids, positions, intermediate_tensors,
-                                   inputs_embeds)
-        return hidden_states
-
-    def compute_logits(
-        self,
-        hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
-    ) -> Optional[torch.Tensor]:
-        logits = self.logits_processor(self.lm_head, hidden_states,
-                                       sampling_metadata)
-        return logits
-
-    def sample(
-        self,
-        logits: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
-    ) -> Optional[SamplerOutput]:
-        next_tokens = self.sampler(logits, sampling_metadata)
-        return next_tokens
-
     def load_weights(self, weights: Iterable[Tuple[str,
                                                    torch.Tensor]]) -> Set[str]:
         stacked_params_mapping = [
@@ -428,17 +358,6 @@ class BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA, SupportsPP,
         for name, loaded_weight in weights:
             if "rotary_emb.inv_freq" in name:
                 continue
-            if name == "lm_head.weight":
-                # Unlike Baichuan, Baichuan2 normalizes the head weights.
-                # Refer to:
-                # https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat/blob/84603cde5ebffb6084e476cfaeceaf0b8b91fe54/modeling_baichuan.py#L508
-                # Distinguish between Baichuan and Baichuan2 by checking the
-                # vocab size. This is suggested by
-                # https://github.com/vllm-project/vllm/pull/1022#discussion_r1325652704
-                is_baichuan2 = self.config.vocab_size == 125696
-                if is_baichuan2:
-                    loaded_weight = torch.nn.functional.normalize(
-                        loaded_weight)
 
             for (param_name, weight_name, shard_id) in stacked_params_mapping:
                 if weight_name not in name:
@@ -464,7 +383,7 @@ class BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA, SupportsPP,
                                         default_weight_loader)
                 weight_loader(param, loaded_weight)
             loaded_params.add(name)
-        
+            
         if self.use_llama_nn and self.quant_method is None :
             lay_key_words = [
                 "self_attn.W_pack.weight",
@@ -540,11 +459,101 @@ class BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA, SupportsPP,
                         zeros_and_scalse_pad= torch.zeros(dim_n,pad_group,dtype=torch.int32).cuda()
                         zeros_and_scalse.data=torch.cat((zeros_and_scalse.data,zeros_and_scalse_pad),dim=1).contiguous()
                         qweight_pad= torch.zeros(dim_n,int(group_size//4),dtype=torch.int32).cuda()
-                        qweight.data=torch.cat((qweight.data,qweight_pad),dim=1).contiguous()         
-        
+                        qweight.data=torch.cat((qweight.data,qweight_pad),dim=1).contiguous()  
         return loaded_params
 
 
+class BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA, SupportsPP,
+                              SupportsQuant):
+    packed_modules_mapping = {
+        "W_pack": ["W_pack"],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        position_embedding: str = "ROPE",
+    ):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+        self.config = config
+        self.lora_config = lora_config
+
+        self.quant_config = quant_config
+        self.model = BaiChuanModel(vllm_config=vllm_config,
+                                   prefix=prefix,
+                                   position_embedding=position_embedding)
+        self.lm_head = ParallelLMHead(config.vocab_size,
+                                      config.hidden_size,
+                                      quant_config=quant_config)
+        self.lm_head.weight.weight_loader = self.lm_head_weight_loader
+        if self.config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.sampler = get_sampler()
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+        
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(logits, sampling_metadata)
+        return next_tokens
+
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
+
+    def lm_head_weight_loader(self, param: nn.Parameter,
+                              loaded_weight: torch.Tensor):
+        # Unlike Baichuan, Baichuan2 normalizes the head weights.
+        # Refer to:
+        # https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat/blob/84603cde5ebffb6084e476cfaeceaf0b8b91fe54/modeling_baichuan.py#L508
+        # Distinguish between Baichuan and Baichuan2 by checking the
+        # vocab size. This is suggested by
+        # https://github.com/vllm-project/vllm/pull/1022#discussion_r1325652704
+        is_baichuan2 = self.config.vocab_size == 125696
+        if is_baichuan2:
+            loaded_weight = torch.nn.functional.normalize(loaded_weight)
+
+        default_weight_loader(param, loaded_weight)
+
+
 class BaichuanForCausalLM(BaiChuanBaseForCausalLM):
     """Baichuan 13B and Baichuan2 7B/13B.
     NOTE: the class name has a lower case 'c'.

vllm/model_executor/models/bamba.py

@@ -34,7 +34,7 @@ from vllm.utils import LayerBlockType
 
 from .interfaces import (HasInnerState, IsHybrid, SupportsLoRA, SupportsPP,
                          SupportsQuant, SupportsV0Only)
-from .utils import (is_pp_missing_parameter,
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
 
@@ -363,6 +363,58 @@ class BambaModel(nn.Module):
         hidden_states, _ = self.final_layernorm(hidden_states, residual)
         return hidden_states
 
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: Set[str] = set()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            if "A_log" in name:
+                name = name.replace("A_log", "A")
+
+            if ".self_attn." in name:
+                name = name.replace(".self_attn", "")
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Skip layers on other devices.
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
 
 class BambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
                        IsHybrid, SupportsV0Only, SupportsQuant):
@@ -502,52 +554,5 @@ class BambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
 
     def load_weights(self, weights: Iterable[Tuple[str,
                                                    torch.Tensor]]) -> Set[str]:
-        stacked_params_mapping = [
-            # (param_name, shard_name, shard_id)
-            ("qkv_proj", "q_proj", "q"),
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
\ No newline at end of file
-            ("qkv_proj", "k_proj", "k"),
-            ("qkv_proj", "v_proj", "v"),
-            ("gate_up_proj", "gate_proj", 0),
-            ("gate_up_proj", "up_proj", 1),
-        ]
-
-        params_dict = dict(self.named_parameters())
-        loaded_params: Set[str] = set()
-        for name, loaded_weight in weights:
-            if "rotary_emb.inv_freq" in name:
-                continue
-
-            if "A_log" in name:
-                name = name.replace("A_log", "A")
-
-            if ".self_attn." in name:
-                name = name.replace(".self_attn", "")
-
-            for param_name, weight_name, shard_id in stacked_params_mapping:
-                if weight_name not in name:
-                    continue
-
-                name = name.replace(weight_name, param_name)
-                # Skip loading extra bias for GPTQ models.
-                if name.endswith(".bias") and name not in params_dict:
-                    continue
-                # Skip layers on other devices.
-                if is_pp_missing_parameter(name, self):
-                    continue
-                param = params_dict[name]
-                weight_loader = param.weight_loader
-                weight_loader(param, loaded_weight, shard_id)
-                break
-            else:
-                # Skip loading extra bias for GPTQ models.
-                if name.endswith(".bias") and name not in params_dict:
-                    continue
-                if is_pp_missing_parameter(name, self):
-                    continue
-
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader",
-                                        default_weight_loader)
-                weight_loader(param, loaded_weight)
-            loaded_params.add(name)
-        return loaded_params

vllm/model_executor/models/bert.py

@@ -26,7 +26,7 @@ from vllm.sequence import IntermediateTensors, PoolerOutput
 from vllm.transformers_utils.config import (
     get_cross_encoder_activation_function)
 
-from .interfaces import SupportsCrossEncoding, SupportsV0Only
+from .interfaces import SupportsCrossEncoding, SupportsQuant, SupportsV0Only
 from .utils import WeightsMapper, maybe_prefix
 
 
@@ -313,7 +313,8 @@ class BertOutput(nn.Module):
         return hidden_states
 
 
-class BertModel(nn.Module):
+class BertModel(nn.Module, SupportsQuant):
+    packed_modules_mapping = {"qkv_proj": ["query", "key", "value"]}
 
     def __init__(self,
                  *,
@@ -385,7 +386,7 @@ class BertModel(nn.Module):
         return loaded_params
 
 
-class BertEmbeddingModel(nn.Module, SupportsV0Only):
+class BertEmbeddingModel(nn.Module, SupportsV0Only, SupportsQuant):
     """A model that uses Bert to provide embedding functionalities.
 
    This class encapsulates the BertModel and provides an interface for
@@ -443,7 +444,8 @@ class BertEmbeddingModel(nn.Module, SupportsV0Only):
                                                 softmax=False)
 
 
-class BertForSequenceClassification(nn.Module, SupportsCrossEncoding):
+class BertForSequenceClassification(nn.Module, SupportsCrossEncoding,
+                                    SupportsQuant):
     """A model that uses Bert to provide embedding functionalities.
 
    This class encapsulates the BertModel and provides an interface for

vllm/model_executor/models/blip.py

@@ -16,6 +16,8 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
+from .interfaces import SupportsQuant
+
 
 def get_blip_patch_grid_length(*, image_size: int, patch_size: int) -> int:
     assert image_size % patch_size == 0
@@ -243,9 +245,10 @@ class BlipEncoder(nn.Module):
         return hidden_states
 
 
-class BlipVisionModel(nn.Module):
+class BlipVisionModel(nn.Module, SupportsQuant):
     config_class = BlipVisionConfig
     main_input_name = "pixel_values"
+    packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
 
     def __init__(
         self,

vllm/model_executor/models/blip2.py

@@ -24,7 +24,8 @@ from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
 from vllm.sequence import IntermediateTensors
 
 from .blip import BlipVisionModel
-from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .interfaces import (MultiModalEmbeddings, SupportsMultiModal, SupportsPP,
+                         SupportsQuant)
 from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
                     maybe_prefix, merge_multimodal_embeddings)
 
@@ -498,7 +499,8 @@ class Blip2MultiModalProcessor(BaseMultiModalProcessor[Blip2ProcessingInfo]):
 @MULTIMODAL_REGISTRY.register_processor(Blip2MultiModalProcessor,
                                         info=Blip2ProcessingInfo,
                                         dummy_inputs=Blip2DummyInputsBuilder)
-class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
+class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
+                                    SupportsQuant):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
 

vllm/model_executor/models/bloom.py

@@ -46,7 +46,7 @@ from vllm.sequence import IntermediateTensors
 from vllm import _custom_ops as ops
 from vllm.model_executor.utils import pad_weight, gemm_bank_conf
 
-from .interfaces import SupportsPP, SupportsV0Only
+from .interfaces import SupportsPP, SupportsQuant, SupportsV0Only
 from .utils import (is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
@@ -290,7 +290,7 @@ class BloomModel(nn.Module):
         return hidden_states
 
 
-class BloomForCausalLM(nn.Module, SupportsPP, SupportsV0Only):
+class BloomForCausalLM(nn.Module, SupportsPP, SupportsV0Only, SupportsQuant):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()