[Bugfix] Fix LoRA extra vocab size (#15047)

Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>

benchmarks/kernels/benchmark_lora.py

@@ -17,13 +17,8 @@ from torch.utils.benchmark import Measurement as TMeasurement
 from utils import ArgPool, Bench, CudaGraphBenchParams
 from weight_shapes import WEIGHT_SHAPES
 
-from vllm.lora.ops.triton_ops.bgmv_expand import bgmv_expand
-from vllm.lora.ops.triton_ops.bgmv_expand_slice import bgmv_expand_slice
-from vllm.lora.ops.triton_ops.bgmv_shrink import bgmv_shrink
-from vllm.lora.ops.triton_ops.sgmv_expand import sgmv_expand
-from vllm.lora.ops.triton_ops.sgmv_shrink import sgmv_shrink
+from vllm.lora.ops.triton_ops import LoRAKernelMeta, lora_expand, lora_shrink
 from vllm.lora.ops.triton_ops.utils import _LORA_A_PTR_DICT, _LORA_B_PTR_DICT
-from vllm.lora.ops.triton_ops.v1 import V1KernelMeta, v1_expand, v1_shrink
 from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
@@ -167,69 +162,25 @@ class OpType(Enum):
     """
     LoRA Ops to benchmark and its properties.
     """
-    SGMV_SHRINK = auto()
-    BGMV_SHRINK = auto()
-    SGMV_EXPAND = auto()
-    BGMV_EXPAND = auto()
-    BGMV_EXPAND_SLICE = auto()
-    V1_SHRINK = auto()
-    V1_EXPAND = auto()
+    LORA_SHRINK = auto()
+    LORA_EXPAND = auto()
 
     @staticmethod
     def from_str(s: str) -> "OpType":
-        if s.lower() == 'sgmv_shrink':
-            return OpType.SGMV_SHRINK
-        if s.lower() == 'sgmv_expand':
-            return OpType.SGMV_EXPAND
-        if s.lower() == 'bgmv_shrink':
-            return OpType.BGMV_SHRINK
-        if s.lower() == 'bgmv_expand':
-            return OpType.BGMV_EXPAND
-        if s.lower() == "bgmv_expand_slice":
-            return OpType.BGMV_EXPAND_SLICE
-        if s.lower() == "v1_shrink":
-            return OpType.V1_SHRINK
-        if s.lower() == "v1_expand":
-            return OpType.V1_EXPAND
+        if s.lower() == "lora_shrink":
+            return OpType.LORA_SHRINK
+        if s.lower() == "lora_expand":
+            return OpType.LORA_EXPAND
         raise ValueError(f"Unrecognized str {s} to convert to OpType")
 
     def is_shrink_fn(self) -> bool:
-        return self in [
-            OpType.SGMV_SHRINK, OpType.BGMV_SHRINK, OpType.V1_SHRINK
-        ]
+        return self in [OpType.LORA_SHRINK]
 
     def is_expand_fn(self) -> bool:
-        return self in [
-            OpType.SGMV_EXPAND, OpType.BGMV_EXPAND, OpType.V1_EXPAND
-        ]
-
-    def is_prefill_op(self) -> bool:
-        return self in [
-            OpType.SGMV_SHRINK, OpType.SGMV_EXPAND, OpType.V1_SHRINK,
-            OpType.V1_EXPAND
-        ]
-
-    def is_decode_op(self) -> bool:
-        return self in [
-            OpType.BGMV_SHRINK, OpType.BGMV_EXPAND, OpType.BGMV_EXPAND_SLICE,
-            OpType.V1_SHRINK, OpType.V1_EXPAND
-        ]
-
-    def is_expand_slice_fn(self) -> bool:
-        return self in [OpType.BGMV_EXPAND_SLICE]
+        return self in [OpType.LORA_EXPAND]
 
     def num_slices(self) -> list[int]:
-        if self in [
-                OpType.SGMV_EXPAND, OpType.SGMV_SHRINK, OpType.V1_SHRINK,
-                OpType.V1_EXPAND
-        ]:
-            # SGMV kernels and v1 kernels supports slices
         return [1, 2, 3]
-        if self in [OpType.BGMV_SHRINK, OpType.BGMV_EXPAND]:
-            return [1]
-        if self in [OpType.BGMV_EXPAND_SLICE]:
-            return [2, 3]
-        raise ValueError(f"Unrecognized OpType {self}")
 
     def mkn(self, batch_size: int, seq_length: int, hidden_size: int,
             lora_rank: int) -> tuple[int, int, int]:
@@ -239,7 +190,7 @@ class OpType(Enum):
             k = hidden_size
             n = lora_rank
         else:
-            assert self.is_expand_fn() or self.is_expand_slice_fn()
+            assert self.is_expand_fn()
             m = num_tokens
             k = lora_rank
             n = hidden_size
@@ -254,7 +205,7 @@ class OpType(Enum):
         if self.is_shrink_fn():
             return op_dtype, op_dtype, torch.float32
         else:
-            assert self.is_expand_fn() or self.is_expand_slice_fn()
+            assert self.is_expand_fn()
             return torch.float32, op_dtype, op_dtype
 
     def matmul_shapes(
@@ -268,43 +219,19 @@ class OpType(Enum):
         m, k, n = self.mkn(batch_size, seq_length, hidden_size, lora_rank)
 
         b_shape = (num_loras, n, k)  # col-major
-        if self in [OpType.SGMV_SHRINK, OpType.V1_SHRINK]:
-            # SGMV shrink and V1 shrink kernels support num_slices inherently
-            # in the kernel.
+        if self in [OpType.LORA_SHRINK]:
+            # LoRA shrink kernels support num_slices inherently in the kernel.
             return ((m, k), b_shape, (num_slices, m, n))
-        if self in [OpType.SGMV_EXPAND, OpType.V1_EXPAND]:
-            # SGMV expand and V1 expand kernels support num_slices inherently
-            # in the kernel
+        if self in [OpType.LORA_EXPAND]:
+            # LoRA expand kernels support num_slices inherently in the kernel
             return ((num_slices, m, k), b_shape, (m, n * num_slices))
-        if self == OpType.BGMV_SHRINK:
-            return ((m, k), b_shape, (m, n))
-        if self == OpType.BGMV_EXPAND:
-            return ((m, k), b_shape, (m, n))
-        if self == OpType.BGMV_EXPAND_SLICE:
-            return ((num_slices, m, k), b_shape, (m, n * num_slices))
-
         raise ValueError(f"Unrecognized op_type {self}")
 
     def bench_fn(self) -> Callable:
-
-        def emulate_bgmv_expand_slice(kwargs_list: list[dict[str, Any]]):
-            for x in kwargs_list:
-                bgmv_expand_slice(**x)
-
-        if self == OpType.SGMV_SHRINK:
-            return sgmv_shrink
-        if self == OpType.SGMV_EXPAND:
-            return sgmv_expand
-        if self == OpType.BGMV_SHRINK:
-            return bgmv_shrink
-        if self == OpType.BGMV_EXPAND:
-            return bgmv_expand
-        if self == OpType.BGMV_EXPAND_SLICE:
-            return emulate_bgmv_expand_slice
-        if self == OpType.V1_SHRINK:
-            return v1_shrink
-        if self == OpType.V1_EXPAND:
-            return v1_expand
+        if self == OpType.LORA_SHRINK:
+            return lora_shrink
+        if self == OpType.LORA_EXPAND:
+            return lora_expand
 
         raise ValueError(f"Unrecognized optype {self}")
 
@@ -318,34 +245,13 @@ class OpType(Enum):
         """
         w_dtype = lora_weights[0].dtype
         num_slices = len(lora_weights)
-        if self in [OpType.SGMV_SHRINK, OpType.V1_SHRINK]:
+        if self in [OpType.LORA_SHRINK]:
             for slice_idx in range(num_slices):
                 ref_group_gemm(ref_out=output[slice_idx, :],
                                input=input,
                                lora_weights=lora_weights[slice_idx],
                                **kwargs)
-        elif self in [OpType.SGMV_EXPAND, OpType.V1_EXPAND]:
-            hidden_size = lora_weights[0].shape[1]
-            for slice_idx in range(num_slices):
-                slice_offset = slice_idx * hidden_size
-                ref_group_gemm(
-                    ref_out=output[:, slice_offset:slice_offset + hidden_size],
-                    input=input[slice_idx].clone().to(dtype=w_dtype),
-                    lora_weights=lora_weights[slice_idx],
-                    **kwargs)
-        elif self == OpType.BGMV_SHRINK:
-            assert num_slices == 1
-            ref_group_gemm(ref_out=output,
-                           input=input,
-                           lora_weights=lora_weights[0],
-                           **kwargs)
-        elif self == OpType.BGMV_EXPAND:
-            assert num_slices == 1
-            ref_group_gemm(ref_out=output,
-                           input=input.clone().to(dtype=w_dtype),
-                           lora_weights=lora_weights[0],
-                           **kwargs)
-        elif self == OpType.BGMV_EXPAND_SLICE:
+        elif self in [OpType.LORA_EXPAND]:
             hidden_size = lora_weights[0].shape[1]
             for slice_idx in range(num_slices):
                 slice_offset = slice_idx * hidden_size
@@ -411,13 +317,11 @@ class BenchmarkTensors:
     input: torch.Tensor
     lora_weights_lst: list[torch.Tensor]
     output: torch.Tensor
-    # metadata tensors
+    # LoRA kernel metadata
+    lora_kernel_meta: LoRAKernelMeta
+    # Metadata tensors used in testing correctness
     seq_lens: torch.Tensor
-    seq_start_loc: torch.Tensor
     prompt_lora_mapping: torch.Tensor
-    token_lora_mapping: torch.Tensor
-    # v1 kernel metadata
-    v1_kernel_meta: Optional[V1KernelMeta] = None
 
     def io_types(self) -> str:
         return (f"{dtype_to_str(self.input.dtype)}x"
@@ -444,35 +348,29 @@ class BenchmarkTensors:
         assert ctx.num_active_loras <= ctx.num_loras
         total_tokens = ctx.batch_size * ctx.seq_length
 
+        # Make metadata tensors involved in correctness testing.
         # Prepare seq lens tensor
         seq_len_tensor = torch.randint(ctx.seq_length, ctx.seq_length + 1,
                                        (ctx.batch_size, ))
-        # Prepare seq_start_loc tensor
-        seq_start_loc_tensor = torch.cumsum(torch.tensor(
-            [0] + seq_len_tensor[:-1].tolist(), dtype=torch.long),
-                                            dim=0)
         assert total_tokens == seq_len_tensor.sum()
         # Prepare prompt lora indices tensor
         prompt_lora_indices_tensor = make_prompt_lora_mapping(
             ctx.batch_size, ctx.num_active_loras, ctx.sort_by_lora_id, "cpu")
-        # Prepare token lora indices tensor
+
+        # Make LoRAKernelMeta
         token_lora_indices_tensor = make_token_lora_mapping(
             total_tokens, ctx.batch_size, prompt_lora_indices_tensor,
             seq_len_tensor, "cpu")
-
-        v1_kernel_meta = None
-        if op_type in [OpType.V1_SHRINK, OpType.V1_EXPAND]:
-            v1_kernel_meta = V1KernelMeta.make(
+        lora_kernel_meta = LoRAKernelMeta.make(
             max_loras=ctx.num_loras,
             max_num_tokens=token_lora_indices_tensor.size(0),
             device="cpu")
-            v1_kernel_meta.prepare_tensors(
+        lora_kernel_meta.prepare_tensors(
             token_lora_mapping=token_lora_indices_tensor)
 
         return BenchmarkTensors(input_tensor, lora_weights, output_tensor,
-                                seq_len_tensor, seq_start_loc_tensor,
-                                prompt_lora_indices_tensor,
-                                token_lora_indices_tensor, v1_kernel_meta)
+                                lora_kernel_meta, seq_len_tensor,
+                                prompt_lora_indices_tensor)
 
     def sanity_check(self) -> None:
         """
@@ -482,9 +380,9 @@ class BenchmarkTensors:
         # check metadata tensors
         assert torch.sum(self.seq_lens) == num_tokens
         num_seqs = self.seq_lens.shape[0]
-        assert self.seq_start_loc.shape[0] == num_seqs
+        #assert self.seq_start_loc.shape[0] == num_seqs
         assert self.prompt_lora_mapping.shape[0] == num_seqs
-        assert self.token_lora_mapping.shape[0] == num_tokens
+        assert self.lora_kernel_meta.token_lora_mapping.shape[0] == num_tokens
 
     def to_device(self, device: str):
         """
@@ -499,220 +397,27 @@ class BenchmarkTensors:
         self.input = to_device(self.input)
         self.output = to_device(self.output)
         self.seq_lens = to_device(self.seq_lens)
-        self.seq_start_loc = to_device(self.seq_start_loc)
         self.prompt_lora_mapping = to_device(self.prompt_lora_mapping)
-        self.token_lora_mapping = to_device(self.token_lora_mapping)
         for i in range(len(self.lora_weights_lst)):
             self.lora_weights_lst[i] = to_device(self.lora_weights_lst[i])
 
-        # v1 meta
-        if self.v1_kernel_meta:
-            for field_name in V1KernelMeta.__dataclass_fields__:
-                field = getattr(self.v1_kernel_meta, field_name)
+        # LoRA meta
+        for field_name in LoRAKernelMeta.__dataclass_fields__:
+            field = getattr(self.lora_kernel_meta, field_name)
             assert isinstance(field, torch.Tensor)
-                setattr(self.v1_kernel_meta, field_name, to_device(field))
+            setattr(self.lora_kernel_meta, field_name, to_device(field))
 
     def metadata(self) -> tuple[int, int, int]:
         """
         Return num_seqs, num_tokens and max_seq_len
         """
         num_seqs = self.seq_lens.shape[0]
-        num_tokens = self.token_lora_mapping.shape[0]
+        num_tokens = self.lora_kernel_meta.token_lora_mapping.shape[0]
         max_seq_len = torch.max(self.seq_lens).item()
         num_slices = len(self.lora_weights_lst)
         return num_seqs, num_tokens, max_seq_len, num_slices
 
-    def convert_to_sgmv_benchmark_tensors(self):
-        """
-        For sgmv punica kernels, when consecutive sequences have the
-        same LoRA ID, we just merge them together.
-        This happens in punica.py::compute_metadata
-        """
-
-        # Collapse seq_lens and seq_start_loc
-        _, seq_lens = torch.unique_consecutive(self.token_lora_mapping,
-                                               return_counts=True)
-        cum_result = torch.cumsum(seq_lens, dim=0)
-        seq_start_loc = torch.zeros_like(seq_lens)
-        seq_start_loc[1:].copy_(cum_result[:-1])
-
-        # Collapse prompt mapping
-        prompt_lora_mapping = torch.unique_consecutive(
-            self.prompt_lora_mapping)
-
-        assert torch.sum(seq_lens) == torch.sum(self.seq_lens), \
-         f"dont match - new {torch.sum(seq_lens)} vs {torch.sum(self.seq_lens)}"
-
-        self.prompt_lora_mapping = prompt_lora_mapping.to(
-            dtype=self.prompt_lora_mapping.dtype)
-        self.seq_lens = seq_lens.to(dtype=self.seq_lens.dtype)
-        self.seq_start_loc = seq_start_loc.to(dtype=self.seq_start_loc.dtype)
-
-    def as_sgmv_shrink_kwargs(self) -> dict[str, Any]:
-        self.convert_to_sgmv_benchmark_tensors()
-        self.sanity_check()
-        self.to_device(self.input.device)
-
-        num_seqs, num_tokens, max_seq_len, num_slices = self.metadata()
-
-        # Sanity check matrix shapes.
-        i_shape, lw_shape, o_shape = self.input.shape, self.lora_weights_lst[
-            0].shape, self.output.shape
-        # Expected input shape [num_tokens, hidden_size]
-        assert len(i_shape) == 2
-        assert i_shape[0] == num_tokens
-        hidden_size = i_shape[1]
-        # Expected lora weight shape [num_loras, lora_rank, hidden_size]
-        assert len(lw_shape) == 3
-        assert lw_shape[2] == hidden_size
-        lora_rank = lw_shape[1]
-        # Expected output shape [num_slices, num_tokens, lora_rank]
-        assert len(o_shape) == 3
-        assert o_shape == (num_slices, num_tokens, lora_rank)
-
-        return {
-            'inputs': self.input,
-            'lora_a_weights': self.lora_weights_lst,
-            'output_tensor': self.output,
-            'b_seq_start_loc': self.seq_start_loc,
-            'seq_len_tensor': self.seq_lens,
-            'lora_indices_tensor': self.prompt_lora_mapping,
-            'batches': num_seqs,
-            'max_seq_length': max_seq_len,
-            'token_nums': num_tokens,
-            'scaling': 1.0,
-        }
-
-    def as_sgmv_expand_kwargs(self, add_inputs: bool) -> dict[str, Any]:
-
-        self.convert_to_sgmv_benchmark_tensors()
-        self.sanity_check()
-        self.to_device(self.input.device)
-
-        num_seqs, num_tokens, max_seq_len, num_slices = self.metadata()
-
-        # Sanity check matrix shapes.
-        i_shape, lw_shape, o_shape = self.input.shape, self.lora_weights_lst[
-            0].shape, self.output.shape
-        # Expected input shape : [num_slices, num_tokens, lora_rank]
-        assert len(i_shape) == 3
-        assert i_shape[0] == num_slices
-        assert i_shape[1] == num_tokens
-        lora_rank = i_shape[2]
-        # Expected lora weight shape : [num_lora, hidden_size, lora_rank]
-        assert len(lw_shape) == 3
-        assert lw_shape[2] == lora_rank
-        hidden_size = lw_shape[1]
-        # Expected output shape : [num_tokens, hidden_size * num_slices]
-        assert len(o_shape) == 2
-        assert o_shape == (num_tokens, hidden_size * num_slices)
-
-        return {
-            'inputs': self.input,
-            'lora_b_weights': self.lora_weights_lst,
-            'output_tensor': self.output,
-            'b_seq_start_loc': self.seq_start_loc,
-            'seq_len_tensor': self.seq_lens,
-            'lora_indices_tensor': self.prompt_lora_mapping,
-            'batches': num_seqs,
-            'max_seq_length': max_seq_len,
-            'token_nums': num_tokens,
-            'offset_start': 0,
-            'add_inputs': add_inputs,
-        }
-
-    def as_bgmv_shrink_kwargs(self) -> dict[str, Any]:
-        assert len(self.lora_weights_lst) == 1
-        self.to_device(self.input.device)
-
-        _, num_tokens, _, _ = self.metadata()
-        # Sanity check shapes
-        i_shape, lw_shape, o_shape = self.input.shape, self.lora_weights_lst[
-            0].shape, self.output.shape
-        # Expected input shape [num_tokens, hidden_size]
-        assert len(i_shape) == 2
-        assert i_shape[0] == num_tokens
-        hidden_size = i_shape[1]
-        # Expected lora weight shape [num_loras, lora_rank, hidden_size]
-        assert len(lw_shape) == 3
-        assert lw_shape[2] == hidden_size
-        lora_rank = lw_shape[1]
-        # Expected output shape [num_tokens, lora_rank]
-        assert len(o_shape) == 2
-        assert o_shape == (num_tokens, lora_rank)
-
-        return {
-            'inputs': self.input,
-            'lora_a_weights': self.lora_weights_lst[0],
-            'output_tensor': self.output,
-            'lora_indices_tensor': self.token_lora_mapping,
-            'scaling': 1.0
-        }
-
-    def as_bgmv_expand_kwargs(self, add_inputs: bool):
-        assert len(self.lora_weights_lst) == 1
-        self.to_device(self.input.device)
-
-        _, num_tokens, _, _ = self.metadata()
-        # Sanity check shapes
-        i_shape, lw_shape, o_shape = self.input.shape, self.lora_weights_lst[
-            0].shape, self.output.shape
-        # Expected input shape [num_tokens, lora_rank]
-        assert len(i_shape) == 2
-        assert i_shape[0] == num_tokens
-        lora_rank = i_shape[1]
-        # Expected lora weight shape [num_loras, hidden_size, lora_rank]
-        assert len(lw_shape) == 3
-        assert lw_shape[2] == lora_rank
-        hidden_size = lw_shape[1]
-        # Expected output shape [num_tokens, hidden_size]
-        assert len(o_shape) == 2
-        assert o_shape == (num_tokens, hidden_size)
-
-        return {
-            'inputs': self.input,
-            'lora_b_weights': self.lora_weights_lst[0],
-            'output_tensor': self.output,
-            'lora_indices_tensor': self.token_lora_mapping,
-            'add_inputs': add_inputs
-        }
-
-    def as_bgmv_expand_slice_kwargs(self, add_inputs: bool) -> dict[str, Any]:
-
-        _, num_tokens, _, num_slices = self.metadata()
-        # Sanity check shapes
-        i_shape, lw_shape, o_shape = self.input.shape, self.lora_weights_lst[
-            0].shape, self.output.shape
-        # Expected input shape [num_slices, num_tokens, lora_rank]
-        assert len(i_shape) == 3
-        assert i_shape[0] == num_slices
-        assert i_shape[1] == num_tokens
-        lora_rank = i_shape[2]
-        # Expected lora weight shape [num_loras, hidden_size, lora_rank]
-        assert len(lw_shape) == 3
-        assert lw_shape[2] == lora_rank
-        hidden_size = lw_shape[1]
-        # Expected output shape [num_tokens, hidden_size * num_slices]
-        assert len(o_shape) == 2
-        assert o_shape == (num_tokens, hidden_size * num_slices)
-
-        self.to_device(self.input.device)
-
-        kwargs_list = []
-        for i in range(num_slices):
-            kwargs_list.append({
-                'inputs': self.input[i],
-                'lora_b_weights': self.lora_weights_lst[i],
-                'output_tensor': self.output,
-                'lora_indices_tensor': self.token_lora_mapping,
-                'slice_offset': i * hidden_size,
-                'slice_size': hidden_size,
-                'add_inputs': add_inputs,
-            })
-        return {'kwargs_list': kwargs_list}
-
-    def as_v1_shrink_kwargs(self) -> dict[str, Any]:
-        assert self.v1_kernel_meta is not None
+    def as_lora_shrink_kwargs(self) -> dict[str, Any]:
         self.sanity_check()
         self.to_device(self.input.device)
 
@@ -737,17 +442,16 @@ class BenchmarkTensors:
             'inputs': self.input,
             'lora_a_weights': self.lora_weights_lst,
             'output_tensor': self.output,
-            'token_lora_mapping': self.v1_kernel_meta.token_lora_mapping,
+            'token_lora_mapping': self.lora_kernel_meta.token_lora_mapping,
             'token_indices_sorted_by_lora_ids':
-            self.v1_kernel_meta.token_indices_sorted_by_lora_ids,
-            'num_tokens_per_lora': self.v1_kernel_meta.num_tokens_per_lora,
-            'lora_token_start_loc': self.v1_kernel_meta.lora_token_start_loc,
-            'lora_ids': self.v1_kernel_meta.active_lora_ids,
+            self.lora_kernel_meta.token_indices_sorted_by_lora_ids,
+            'num_tokens_per_lora': self.lora_kernel_meta.num_tokens_per_lora,
+            'lora_token_start_loc': self.lora_kernel_meta.lora_token_start_loc,
+            'lora_ids': self.lora_kernel_meta.active_lora_ids,
             'scaling': 1.0,
         }
 
-    def as_v1_expand_kwargs(self, add_inputs: bool) -> dict[str, Any]:
-        assert self.v1_kernel_meta is not None
+    def as_lora_expand_kwargs(self, add_inputs: bool) -> dict[str, Any]:
         self.sanity_check()
         self.to_device(self.input.device)
 
@@ -773,12 +477,12 @@ class BenchmarkTensors:
             'inputs': self.input,
             'lora_b_weights': self.lora_weights_lst,
             'output_tensor': self.output,
-            'token_lora_mapping': self.v1_kernel_meta.token_lora_mapping,
+            'token_lora_mapping': self.lora_kernel_meta.token_lora_mapping,
             'token_indices_sorted_by_lora_ids':
-            self.v1_kernel_meta.token_indices_sorted_by_lora_ids,
-            'num_tokens_per_lora': self.v1_kernel_meta.num_tokens_per_lora,
-            'lora_token_start_loc': self.v1_kernel_meta.lora_token_start_loc,
-            'lora_ids': self.v1_kernel_meta.active_lora_ids,
+            self.lora_kernel_meta.token_indices_sorted_by_lora_ids,
+            'num_tokens_per_lora': self.lora_kernel_meta.num_tokens_per_lora,
+            'lora_token_start_loc': self.lora_kernel_meta.lora_token_start_loc,
+            'lora_ids': self.lora_kernel_meta.active_lora_ids,
             'offset_start': 0,
             'add_inputs': add_inputs,
         }
@@ -791,20 +495,10 @@ class BenchmarkTensors:
         else:
             assert add_inputs is not None
 
-        if op_type == OpType.SGMV_SHRINK:
-            return self.as_sgmv_shrink_kwargs()
-        if op_type == OpType.SGMV_EXPAND:
-            return self.as_sgmv_expand_kwargs(add_inputs)
-        if op_type == OpType.BGMV_SHRINK:
-            return self.as_bgmv_shrink_kwargs()
-        if op_type == OpType.BGMV_EXPAND:
-            return self.as_bgmv_expand_kwargs(add_inputs)
-        if op_type == OpType.BGMV_EXPAND_SLICE:
-            return self.as_bgmv_expand_slice_kwargs(add_inputs)
-        if op_type == OpType.V1_SHRINK:
-            return self.as_v1_shrink_kwargs()
-        if op_type == OpType.V1_EXPAND:
-            return self.as_v1_expand_kwargs(add_inputs)
+        if op_type == OpType.LORA_SHRINK:
+            return self.as_lora_shrink_kwargs()
+        if op_type == OpType.LORA_EXPAND:
+            return self.as_lora_expand_kwargs(add_inputs)
         raise ValueError(f"Unrecognized optype {self}")
 
     def test_correctness(self, op_type: OpType,
@@ -993,10 +687,6 @@ def run(args: argparse.Namespace, bench_ctxs: list[BenchmarkContext]):
     for bench_ctx in bench_ctxs:
         for seq_len in args.seq_lengths:
             bench_ops: list[OpType] = args.op_types
-            if seq_len > 1:
-                # bench only prefill ops
-                bench_ops = [op for op in args.op_types if op.is_prefill_op()]
-
             seq_len_timers = []
             for bench_op in bench_ops:
                 for num_slices in bench_op.num_slices():
@@ -1206,13 +896,13 @@ Benchmark LoRA kernels:
     {use_cuda_graph_recommendation()}
 
     list_bench example:
-        python3 benchmarks/kernels/benchmark_lora.py list_bench --arg-pool-size 32 --batch-sizes 1 16 32 --dtype torch.float16 --hidden-sizes 2048 --lora-ranks 16 --num-loras 1 4 --op-types bgmv_shrink bgmv_expand sgmv_shrink sgmv_expand bgmv_expand_slice --seq-lengths 1 16 --sort-by-lora-id 1 --cuda-graph-nops 32
+        python3 benchmarks/kernels/benchmark_lora.py list_bench --arg-pool-size 32 --batch-sizes 1 16 32 --dtype torch.float16 --hidden-sizes 2048 --lora-ranks 16 --num-loras 1 4 --op-types lora_shrink lora_expand --seq-lengths 1 16 --sort-by-lora-id 1 --cuda-graph-nops 32
 
     model_bench example:
-        python3 benchmarks/kernels/benchmark_lora.py model_bench --models meta-llama/Llama-3-8b  --arg-pool-size 32 --batch-sizes 1 16 32 --dtype torch.float16  --lora-ranks 16 --num-loras 1 4 --op-types bgmv_shrink bgmv_expand sgmv_shrink sgmv_expand bgmv_expand_slice --seq-lengths 1 16 --sort-by-lora-id 1 --cuda-graph-nops 32 
+        python3 benchmarks/kernels/benchmark_lora.py model_bench --models meta-llama/Llama-3-8b  --arg-pool-size 32 --batch-sizes 1 16 32 --dtype torch.float16  --lora-ranks 16 --num-loras 1 4 --op-types lora_shrink lora_expand --seq-lengths 1 16 --sort-by-lora-id 1 --cuda-graph-nops 32 
 
     range_bench example:
-        python3 benchmarks/kernels/benchmark_lora.py range_bench  --arg-pool-size 32 --batch-sizes 1 16 32 --dtype torch.float16   --num-loras 1 4 --op-types bgmv_shrink bgmv_expand sgmv_shrink sgmv_expand bgmv_expand_slice --seq-lengths 1 16 --sort-by-lora-id 1 --cuda-graph-nops 32 --hidden-sizes-start 1024 --hidden-sizes-end 4096 --hidden-sizes-increment 1024 --lora-ranks-start 8 --lora-ranks-end 24 --lora-ranks-increment 8 
+        python3 benchmarks/kernels/benchmark_lora.py range_bench  --arg-pool-size 32 --batch-sizes 1 16 32 --dtype torch.float16   --num-loras 1 4 --op-types lora_shrink lora_expand --seq-lengths 1 16 --sort-by-lora-id 1 --cuda-graph-nops 32 --hidden-sizes-start 1024 --hidden-sizes-end 4096 --hidden-sizes-increment 1024 --lora-ranks-start 8 --lora-ranks-end 24 --lora-ranks-increment 8 
             """,  # noqa: E501
         formatter_class=argparse.RawTextHelpFormatter)
 

docs/source/models/supported_models.md

@@ -477,6 +477,11 @@ See [this page](#generative-models) for more information on how to use generativ
   * `xverse/XVERSE-7B-Chat`, `xverse/XVERSE-13B-Chat`, `xverse/XVERSE-65B-Chat`, etc.
   * ✅︎
   * ✅︎
+- * `Zamba2ForCausalLM`
+  * Zamba2
+  * `Zyphra/Zamba2-7B-instruct`, `Zyphra/Zamba2-2.7B-instruct`, `Zyphra/Zamba2-1.2B-instruct`, etc.
+  *
+  *
 :::
 
 :::{note}

examples/offline_inference/audio_language.py

@@ -93,7 +93,6 @@ def run_phi4mm(question: str, audio_count: int) -> ModelRequestData:
         max_num_seqs=2,
         enable_lora=True,
         max_lora_rank=320,
-        lora_extra_vocab_size=0,
         limit_mm_per_prompt={"audio": audio_count},
     )
 

examples/offline_inference/vision_language.py

@@ -682,7 +682,6 @@ def run_phi4mm(questions: list[str], modality: str) -> ModelRequestData:
         max_num_seqs=2,
         enable_lora=True,
         max_lora_rank=320,
-        lora_extra_vocab_size=0,
     )
 
     return ModelRequestData(

examples/offline_inference/vision_language_multi_image.py

@@ -342,7 +342,6 @@ def load_phi4mm(question: str, image_urls: list[str]) -> ModelRequestData:
         limit_mm_per_prompt={"image": len(image_urls)},
         enable_lora=True,
         max_lora_rank=320,
-        lora_extra_vocab_size=0,
     )
 
     placeholders = "".join(f"<|image_{i}|>"

tests/lora/test_punica_ops.py

@@ -4,18 +4,13 @@ from threading import Lock
 import pytest
 import torch
 
-import vllm.lora.ops.triton_ops  # noqa: F401
-import vllm.lora.ops.triton_ops.v1  # noqa: F401
-from vllm.lora.ops.torch_ops import (bgmv_expand, bgmv_expand_slice,
-                                     bgmv_shrink, sgmv_expand,
-                                     sgmv_expand_slice, sgmv_shrink)
+import vllm.lora.ops.torch_ops as torch_ops
+import vllm.lora.ops.triton_ops as triton_ops
+from vllm.lora.ops.triton_ops import LoRAKernelMeta
 from vllm.lora.ops.triton_ops.utils import _LORA_A_PTR_DICT, _LORA_B_PTR_DICT
-from vllm.lora.ops.triton_ops.v1 import V1KernelMeta
 from vllm.platforms import current_platform
 
-from .utils import (PunicaTensors, assert_close, generate_data,
-                    generate_data_for_expand_nslices,
-                    generate_data_for_nslices)
+from .utils import PunicaTensors, assert_close, generate_data_for_nslices
 
 
 # Utility shrink and expand operations used as reference implementations.
@@ -26,10 +21,10 @@ def sgmv_shrink_for_nslices(
         prompt_lora_mapping: torch.Tensor, batches: int, max_seq_length: int,
         num_tokens: int, scaling: float):
     """
-    Wrapper around sgmv_shrink that handles any nslices.
+    Wrapper around torch_ops.sgmv_shrink that handles any nslices.
     """
     for index in range(nslices):
-        sgmv_shrink(
+        torch_ops.sgmv_shrink(
             inputs_tensor,
             lora_weights_lst[index],
             out_tensor[index],
@@ -53,11 +48,11 @@ def sgmv_expand_for_nslices(nslices: int, hidden_size: int,
                             max_seq_length: int, num_tokens: int,
                             add_inputs: bool) -> None:
     """
-    Wrapper around sgmv_expand that handles any nslices.
+    Wrapper around torch_ops.sgmv_expand that handles any nslices.
     """
     if nslices == 1:
         # Verify the torch's sgmv_expand op
-        sgmv_expand(
+        torch_ops.sgmv_expand(
             inputs_tensor[0],
             lora_weights_lst[0],
             out_tensor,
@@ -73,7 +68,7 @@ def sgmv_expand_for_nslices(nslices: int, hidden_size: int,
         slice_offset = 0
         for index in range(nslices):
             lora_weights = lora_weights_lst[index]
-            sgmv_expand_slice(
+            torch_ops.sgmv_expand_slice(
                 inputs_tensor[index],
                 lora_weights,
                 out_tensor,
@@ -93,12 +88,13 @@ def sgmv_expand_for_nslices(nslices: int, hidden_size: int,
 _dict_lock = Lock()
 
 
-def check_shrink_kernels(batches: int, num_loras: int, rank: int,
-                         hidden_size: int, nslices: int, dtype: torch.dtype,
-                         device: str, seq_length: int, scaling: float):
+def check_lora_shrink_kernel(batches: int, num_loras: int, rank: int,
+                             hidden_size: int, nslices: int,
+                             dtype: torch.dtype, device: str, seq_length: int,
+                             scaling: float):
     """
-    Compare outputs of vllm.sgmv_shrink and vllm.v1_shrink kernel against a
-    reference implementation.
+    Compare outputs of torch_ops.sgmv_shrink and triton_ops.lora_shrink
+    kernels.
     """
     data: PunicaTensors = generate_data_for_nslices(
         batches,
@@ -118,35 +114,24 @@ def check_shrink_kernels(batches: int, num_loras: int, rank: int,
                       data.prompt_lora_mapping, batches, max_seq_length,
                       token_nums)
 
-    # Setup metadata information for the V1 kernel.
-    v1_meta = V1KernelMeta.make(max_loras=num_loras,
+    # Setup metadata information for the LoRA kernel.
+    lora_meta = LoRAKernelMeta.make(max_loras=num_loras,
                                     max_num_tokens=token_nums,
                                     device='cuda')
-    v1_meta.prepare_tensors(data.token_lora_mapping)
+    lora_meta.prepare_tensors(data.token_lora_mapping)
 
     ref_out_tensor = data.ref_out_tensor
-    sgmv_out_tensor = data.our_out_tensor
-    v1_out_tensor = data.our_out_tensor.clone()
+    out_tensor = data.our_out_tensor.clone()
 
     # Preventing cache error pointer.
     with _dict_lock:
-        # SGMV shrink kernel
+        # lora_shrink kernel
         _LORA_A_PTR_DICT.clear()
-        torch.ops.vllm.sgmv_shrink(
+        triton_ops.lora_shrink(
             data.inputs_tensor,
             data.lora_weights,
-            sgmv_out_tensor,
-            *sgmv_meta_args,
-            scaling,
-        )
-
-        # V1 shrink kernel
-        _LORA_A_PTR_DICT.clear()
-        torch.ops.vllm.v1_shrink(
-            data.inputs_tensor,
-            data.lora_weights,
-            v1_out_tensor,
-            *v1_meta.meta_args(token_nums=token_nums),
+            out_tensor,
+            *lora_meta.meta_args(token_nums=token_nums),
             scaling,
         )
 
@@ -160,16 +145,16 @@ def check_shrink_kernels(batches: int, num_loras: int, rank: int,
         scaling,
     )
 
-    assert_close(sgmv_out_tensor, ref_out_tensor)
-    assert_close(v1_out_tensor, ref_out_tensor)
+    assert_close(out_tensor, ref_out_tensor)
 
 
-def check_expand_kernels(batches: int, num_loras: int, rank: int,
-                         hidden_size: int, nslices: int, dtype: torch.dtype,
-                         device: str, seq_length: int, add_inputs: bool):
+def check_lora_expand_kernel(batches: int, num_loras: int, rank: int,
+                             hidden_size: int, nslices: int,
+                             dtype: torch.dtype, device: str, seq_length: int,
+                             add_inputs: bool):
     """
-    Compare outputs of vllm.sgmv_expand and vllm.v1_expand kernels against a
-    reference implementation.
+    Compare outputs of torch_ops.sgmv_expand and triton_ops.lora_expand
+    kernels.
     """
     data: PunicaTensors = generate_data_for_nslices(
         batches,
@@ -190,35 +175,23 @@ def check_expand_kernels(batches: int, num_loras: int, rank: int,
                       data.prompt_lora_mapping, batches, max_seq_length,
                       token_nums)
 
-    # Setup metadata information for the V1 kernel.
-    v1_meta = V1KernelMeta.make(max_loras=num_loras,
+    # Setup metadata information for the LoRA kernel.
+    lora_meta = LoRAKernelMeta.make(max_loras=num_loras,
                                     max_num_tokens=token_nums,
                                     device='cuda')
-    v1_meta.prepare_tensors(data.token_lora_mapping)
+    lora_meta.prepare_tensors(data.token_lora_mapping)
 
     # Setup output tensors
     ref_out_tensor = data.ref_out_tensor
-    sgmv_out_tensor = data.our_out_tensor
-    v1_out_tensor = data.our_out_tensor.clone()
+    out_tensor = data.our_out_tensor.clone()
 
     with _dict_lock:
-        # SGMV expand kernel
+        # lora_expand kernel
         _LORA_B_PTR_DICT.clear()
-        torch.ops.vllm.sgmv_expand(
-            data.inputs_tensor,
+        triton_ops.lora_expand(data.inputs_tensor,
                                data.lora_weights,
-            sgmv_out_tensor,
-            *sgmv_meta_args,
-            offset_start=0,
-            add_inputs=add_inputs,
-        )
-
-        # V1 expand kernel
-        _LORA_B_PTR_DICT.clear()
-        torch.ops.vllm.v1_expand(data.inputs_tensor,
-                                 data.lora_weights,
-                                 v1_out_tensor,
-                                 *v1_meta.meta_args(token_nums=token_nums),
+                               out_tensor,
+                               *lora_meta.meta_args(token_nums=token_nums),
                                offset_start=0,
                                add_inputs=add_inputs)
 
@@ -231,124 +204,7 @@ def check_expand_kernels(batches: int, num_loras: int, rank: int,
                             *sgmv_meta_args,
                             add_inputs=add_inputs)
 
-    assert_close(sgmv_out_tensor, ref_out_tensor)
-    assert_close(v1_out_tensor, ref_out_tensor)
-
-
-def check_bgmv_shrink(batches: int, num_loras: int, rank: int,
-                      hidden_size: int, dtype: torch.dtype, device: str,
-                      scaling: float):
-    """
-    Compare vllm.bgmv_shrink against a reference implementation.
-    """
-    seq_length = 1
-    data: PunicaTensors = generate_data(
-        batches,
-        hidden_size,
-        num_loras,
-        rank,
-        seq_length,
-        dtype,
-        "shrink",
-        device,
-    )
-
-    torch.ops.vllm.bgmv_shrink(
-        data.inputs_tensor,
-        data.lora_weights,
-        data.our_out_tensor,
-        data.token_lora_mapping,
-        scaling,
-    )
-
-    bgmv_shrink(
-        data.inputs_tensor,
-        data.lora_weights,
-        data.ref_out_tensor,
-        data.token_lora_mapping,
-        scaling,
-    )
-
-    data.ref_out_tensor = data.ref_out_tensor.to(torch.float32)
-    assert_close(data.our_out_tensor, data.ref_out_tensor)
-
-
-def check_bgmv_expand(batches: int, num_loras: int, rank: int,
-                      hidden_size: int, dtype: torch.dtype, device: str,
-                      add_inputs: bool):
-    """
-    Compare vllm.bgmv_expand against a reference implementation.
-    """
-    seq_length = 1
-    data: PunicaTensors = generate_data(
-        batches,
-        hidden_size,
-        num_loras,
-        rank,
-        seq_length,
-        dtype,
-        "expand",
-        device,
-    )
-
-    torch.ops.vllm.bgmv_expand(
-        data.inputs_tensor,
-        data.lora_weights,
-        data.our_out_tensor,
-        data.token_lora_mapping,
-        add_inputs=add_inputs,
-    )
-    bgmv_expand(
-        data.inputs_tensor,
-        data.lora_weights,
-        data.ref_out_tensor,
-        data.token_lora_mapping,
-        add_inputs=add_inputs,
-    )
-    assert_close(data.our_out_tensor, data.ref_out_tensor)
-
-
-def check_bgmv_expand_slice(batches: int, num_loras: int, rank: int,
-                            hidden_size: int, nslices: int, dtype: torch.dtype,
-                            device: str, add_inputs: bool):
-    """
-    Compare vllm.bgmv_expand_slice against a reference implementation.
-    """
-    seq_length = 1
-    data: PunicaTensors = generate_data_for_expand_nslices(
-        batches,
-        hidden_size,
-        num_loras,
-        rank,
-        seq_length,
-        dtype,
-        nslices,
-        device,
-    )
-
-    slice_offset = 0
-    for index in range(nslices):
-        torch.ops.vllm.bgmv_expand_slice(
-            data.inputs_tensor,
-            data.lora_weights[index],
-            data.our_out_tensor,
-            data.token_lora_mapping,
-            slice_offset,
-            slice_size=hidden_size,
-            add_inputs=add_inputs,
-        )
-        bgmv_expand_slice(
-            data.inputs_tensor,
-            data.lora_weights[index],
-            data.ref_out_tensor,
-            data.token_lora_mapping,
-            slice_offset,
-            slice_size=hidden_size,
-            add_inputs=add_inputs,
-        )
-
-        slice_offset += hidden_size
-    assert_close(data.our_out_tensor, data.ref_out_tensor)
+    assert_close(out_tensor, ref_out_tensor)
 
 
 # Tests
@@ -490,13 +346,13 @@ def test_kernels(
     op_type: str,
 ):
     """
-    Tests SGMV and V1 kernels.
+    Tests LoRA kernels.
     """
     torch.set_default_device(device)
     current_platform.seed_everything(seed)
 
     if op_type == "shrink":
-        check_shrink_kernels(batches=batches,
+        check_lora_shrink_kernel(batches=batches,
                                  num_loras=num_loras,
                                  rank=rank,
                                  hidden_size=hidden_size,
@@ -506,7 +362,7 @@ def test_kernels(
                                  seq_length=128,
                                  scaling=0.5)
     else:
-        check_expand_kernels(batches=batches,
+        check_lora_expand_kernel(batches=batches,
                                  num_loras=num_loras,
                                  rank=rank,
                                  hidden_size=hidden_size,
@@ -538,13 +394,13 @@ def test_kernels_hidden_size(
     op_type: str,
 ):
     """
-    Tests SGMV and V1 kernels.
+    Tests SGMV and LoRA kernels.
     """
     torch.set_default_device(device)
     current_platform.seed_everything(seed)
 
     if op_type == "shrink":
-        check_shrink_kernels(batches=batches,
+        check_lora_shrink_kernel(batches=batches,
                                  num_loras=num_loras,
                                  rank=rank,
                                  hidden_size=hidden_size,
@@ -554,7 +410,7 @@ def test_kernels_hidden_size(
                                  seq_length=128,
                                  scaling=0.5)
     else:
-        check_expand_kernels(batches=batches,
+        check_lora_expand_kernel(batches=batches,
                                  num_loras=num_loras,
                                  rank=rank,
                                  hidden_size=hidden_size,
@@ -563,134 +419,3 @@ def test_kernels_hidden_size(
                                  device=device,
                                  seq_length=128,
                                  add_inputs=True)
-
-
-@pytest.mark.parametrize("batches", test_params['batches'])
-@pytest.mark.parametrize("num_loras", test_params['num_loras'])
-@pytest.mark.parametrize("rank", test_params['max_ranks'])
-@pytest.mark.parametrize("hidden_size", test_params['hidden_sizes'])
-@pytest.mark.parametrize("dtype", DTYPES)
-@pytest.mark.parametrize("device", DEVICES)
-@pytest.mark.parametrize("seed", SEED)
-@pytest.mark.parametrize("op_type", ["shrink", "expand"])
-def test_punica_bgmv(
-    batches: int,
-    num_loras: int,
-    rank: int,
-    hidden_size: int,
-    dtype: torch.dtype,
-    device: str,
-    seed: int,
-    op_type: str,
-):
-    torch.set_default_device(device)
-    current_platform.seed_everything(seed)
-
-    if op_type == "shrink":
-        check_bgmv_shrink(batches=batches,
-                          num_loras=num_loras,
-                          rank=rank,
-                          hidden_size=hidden_size,
-                          dtype=dtype,
-                          device=device,
-                          scaling=0.5)
-    else:
-        check_bgmv_expand(batches=batches,
-                          num_loras=num_loras,
-                          rank=rank,
-                          hidden_size=hidden_size,
-                          dtype=dtype,
-                          device=device,
-                          add_inputs=True)
-
-
-@pytest.mark.parametrize("batches", hs_test_params['batches'])
-@pytest.mark.parametrize("num_loras", hs_test_params['num_loras'])
-@pytest.mark.parametrize("rank", hs_test_params['max_ranks'])
-@pytest.mark.parametrize("hidden_size", hs_test_params['hidden_sizes'])
-@pytest.mark.parametrize("dtype", DTYPES)
-@pytest.mark.parametrize("device", DEVICES)
-@pytest.mark.parametrize("seed", SEED)
-@pytest.mark.parametrize("op_type", ["shrink", "expand"])
-def test_punica_bgmv_hidden_size(
-    batches: int,
-    num_loras: int,
-    rank: int,
-    hidden_size: int,
-    dtype: torch.dtype,
-    device: str,
-    seed: int,
-    op_type: str,
-):
-    torch.set_default_device(device)
-    current_platform.seed_everything(seed)
-
-    if op_type == "shrink":
-        check_bgmv_shrink(batches=batches,
-                          num_loras=num_loras,
-                          rank=rank,
-                          hidden_size=hidden_size,
-                          dtype=dtype,
-                          device=device,
-                          scaling=0.5)
-    else:
-        check_bgmv_expand(batches=batches,
-                          num_loras=num_loras,
-                          rank=rank,
-                          hidden_size=hidden_size,
-                          dtype=dtype,
-                          device=device,
-                          add_inputs=True)
-
-
-@pytest.mark.parametrize("batches", test_params['batches'])
-@pytest.mark.parametrize("num_loras", test_params['num_loras'])
-@pytest.mark.parametrize("rank", test_params['max_ranks'])
-@pytest.mark.parametrize("hidden_size", test_params['hidden_sizes'])
-@pytest.mark.parametrize("nslices", [2, 3])
-@pytest.mark.parametrize("dtype", DTYPES)
-@pytest.mark.parametrize("device", DEVICES)
-@pytest.mark.parametrize("seed", SEED)
-def test_punica_bgmv_expand_nslices(batches: int, num_loras: int, rank: int,
-                                    hidden_size: int, nslices: int,
-                                    dtype: torch.dtype, device: str,
-                                    seed: int):
-
-    torch.set_default_device(device)
-    current_platform.seed_everything(seed)
-
-    check_bgmv_expand_slice(batches=batches,
-                            num_loras=num_loras,
-                            rank=rank,
-                            hidden_size=hidden_size,
-                            nslices=nslices,
-                            dtype=dtype,
-                            device=device,
-                            add_inputs=True)
-
-
-@pytest.mark.parametrize("batches", hs_test_params['batches'])
-@pytest.mark.parametrize("num_loras", hs_test_params['num_loras'])
-@pytest.mark.parametrize("rank", hs_test_params['max_ranks'])
-@pytest.mark.parametrize("hidden_size", hs_test_params['hidden_sizes'])
-@pytest.mark.parametrize("nslices", [2, 3])
-@pytest.mark.parametrize("dtype", DTYPES)
-@pytest.mark.parametrize("device", DEVICES)
-@pytest.mark.parametrize("seed", SEED)
-def test_punica_bgmv_expand_nslices_hidden_size(batches: int, num_loras: int,
-                                                rank: int, hidden_size: int,
-                                                nslices: int,
-                                                dtype: torch.dtype,
-                                                device: str, seed: int):
-
-    torch.set_default_device(device)
-    current_platform.seed_everything(seed)
-
-    check_bgmv_expand_slice(batches=batches,
-                            num_loras=num_loras,
-                            rank=rank,
-                            hidden_size=hidden_size,
-                            nslices=nslices,
-                            dtype=dtype,
-                            device=device,
-                            add_inputs=True)

tests/models/decoder_only/language/test_hybrid.py

@@ -9,7 +9,7 @@ from vllm.sampling_params import SamplingParams
 from ...utils import check_outputs_equal
 
 # This test is for the hybrid models
-MODELS = ["ai21labs/Jamba-tiny-dev"]
+MODELS = ["ai21labs/Jamba-tiny-dev", "Zyphra/Zamba2-1.2B-instruct"]
 # Bamba at Fp32 is too big for the CI (L4 GPU).
 # MODELS = ["ai21labs/Jamba-tiny-dev", "ibm-ai-platform/Bamba-9B"]
 
@@ -27,17 +27,19 @@ def test_models(
 ) -> None:
 
     # numeric error produces different generation
-    if 'Bamba' in model:
+    if "Bamba" in model:
         example_prompts.pop(3)
 
-    with hf_runner(
-            model,
-            dtype=dtype,
-            model_kwargs={
-                "use_mamba_kernels":
-                False,  # mamba kernels are not installed so HF 
+    model_kwargs = {
+        "use_mamba_kernels": False,  # mamba kernels are not installed so HF 
         # don't use them
-            }) as hf_model:
+    }
+    if "Zamba2" in model:
+        # Zamba2 HF implementation automatically checks if mamba kernels are
+        # installed
+        model_kwargs = {}
+
+    with hf_runner(model, dtype=dtype, model_kwargs=model_kwargs) as hf_model:
         hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
 
     with vllm_runner(model, dtype=dtype) as vllm_model:
@@ -112,26 +114,31 @@ def test_mamba_prefill_chunking_with_parallel_sampling(
 def test_mamba_prefill_chunking(hf_runner, vllm_runner, example_prompts,
                                 model: str, dtype: str,
                                 max_tokens: int) -> None:
-    # numeric error during prefill chucking produces different generation
+    # numeric error during prefill chunking produces different generation
     # compared to w/o prefill chunking for those examples, removed them for now
-    if 'Jamba' in model:
+    if "Jamba" in model:
         example_prompts.pop(7)
         example_prompts.pop(2)
         example_prompts.pop(1)
-    elif 'Bamba' in model:
+    elif "Bamba" in model:
         example_prompts.pop(6)
         example_prompts.pop(3)
         example_prompts.pop(2)
         dtype = "half"  # use a different dtype for Bamba
+    elif "Zamba2" in model:
+        example_prompts.pop(7)
+        dtype = "half"
 
-    with hf_runner(
-            model,
-            dtype=dtype,
-            model_kwargs={
-                "use_mamba_kernels":
-                False,  # mamba kernels are not installed so HF 
+    model_kwargs = {
+        "use_mamba_kernels": False,  # mamba kernels are not installed so HF 
         # don't use them
-            }) as hf_model:
+    }
+    if "Zamba2" in model:
+        # Zamba2 HF implementation automatically checks if mamba kernels are
+        # installed
+        model_kwargs = {}
+
+    with hf_runner(model, dtype=dtype, model_kwargs=model_kwargs) as hf_model:
         non_chunked = hf_model.generate_greedy(example_prompts, max_tokens)
 
     with vllm_runner(model,

tests/models/decoder_only/vision_language/test_phi4mm.py

@@ -100,7 +100,6 @@ def run_test(
             distributed_executor_backend=distributed_executor_backend,
             enable_lora=True,
             max_lora_rank=320,
-            lora_extra_vocab_size=0,
             gpu_memory_utilization=0.8,  # set to 0.8 to avoid OOM in CI
             enforce_eager=True,
     ) as vllm_model:

tests/models/registry.py

@@ -195,6 +195,8 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "XverseForCausalLM": _HfExamplesInfo("xverse/XVERSE-7B-Chat",
                                          is_available_online=False,
                                          trust_remote_code=True),
+    "Zamba2ForCausalLM": _HfExamplesInfo("Zyphra/Zamba2-7B-instruct",
+                                         min_transformers_version="4.49"),
     # [Encoder-decoder]
     "BartModel": _HfExamplesInfo("facebook/bart-base"),
     "BartForConditionalGeneration": _HfExamplesInfo("facebook/bart-large-cnn"),

vllm/config.py

@@ -821,6 +821,11 @@ class ModelConfig:
                 if qk_rope_head_dim and qk_nope_head_dim:
                     return qk_rope_head_dim + qk_nope_head_dim
 
+        if hasattr(self.hf_text_config,
+                   "model_type") and (self.hf_text_config.model_type
+                                      == "zamba2"):
+            return self.hf_text_config.attention_head_dim
+
         if self.is_attention_free:
             return 0
 
@@ -904,7 +909,9 @@ class ModelConfig:
         else:
             total_num_hidden_layers = getattr(self.hf_text_config,
                                               "num_hidden_layers", 0)
-        pp_rank = parallel_config.rank // parallel_config.tensor_parallel_size
+        # the layout order is: DP x PP x TP
+        pp_rank = (parallel_config.rank // parallel_config.tensor_parallel_size
+                   ) % parallel_config.pipeline_parallel_size
         pp_size = parallel_config.pipeline_parallel_size
         start, end = get_pp_indices(total_num_hidden_layers, pp_rank, pp_size)
         return start, end
@@ -942,6 +949,15 @@ class ModelConfig:
                                  "cannot determine the num of "
                                  f"{block_type.value} layers")
 
+            if hasattr(self.hf_text_config,
+                       "model_type") and (self.hf_text_config.model_type
+                                          == "zamba2"):
+                if attn_block_type:
+                    return sum(t == "hybrid"
+                               for t in layers_block_type_value[start:end])
+                else:
+                    return self.get_num_layers(parallel_config)
+
             return sum(t == block_type.value
                        for t in layers_block_type_value[start:end])
 
@@ -2308,7 +2324,7 @@ class LoRAConfig:
         # Setting the maximum rank to 512 should be able to satisfy the vast
         # majority of applications.
         possible_max_ranks = (8, 16, 32, 64, 128, 256, 320, 512)
-        possible_lora_extra_vocab_size = (0, 256, 512)
+        possible_lora_extra_vocab_size = (256, 512)
         if self.max_lora_rank not in possible_max_ranks:
             raise ValueError(
                 f"max_lora_rank ({self.max_lora_rank}) must be one of "

vllm/distributed/parallel_state.py

@@ -897,9 +897,22 @@ def initialize_model_parallel(
         get_world_group().device_group)
 
     data_parallel_size = 1
+    has_external_dp = False
     from vllm.config import get_current_vllm_config
     config = get_current_vllm_config()
     if config is not None:
+        if config.parallel_config.world_size != world_size:
+            # detect external data parallelism.
+            # dp in vllm means all dp instances need to run together.
+            # if the world size does not match, it means this dp is external,
+            # and the dp instances can run independently, e.g. in rlhf workflow
+            # from https://github.com/volcengine/verl .
+            # in that case, we treat the rest dimensions as if they are
+            # data parallel, and create a dummy dp group that is not used.
+            data_parallel_size = world_size // (pipeline_model_parallel_size *
+                                                tensor_model_parallel_size)
+            has_external_dp = True
+        else:
             data_parallel_size = config.parallel_config.data_parallel_size
 
     # the layout order is: DP x PP x TP
@@ -940,6 +953,12 @@ def initialize_model_parallel(
                                       2).reshape(-1,
                                                  data_parallel_size).unbind(0)
     group_ranks = [x.tolist() for x in group_ranks]
+    if has_external_dp:
+        # create a dummy dp group that is not used actually,
+        # since this dp is external.
+        # a dummy dp group means every rank is a group itself.
+        # this way, no communication is needed, no memory is wasted.
+        group_ranks = [[x] for x in range(world_size)]
     _DP = init_model_parallel_group(group_ranks,
                                     get_world_group().local_rank,
                                     backend,

vllm/engine/arg_utils.py

@@ -3,6 +3,7 @@
 import argparse
 import dataclasses
 import json
+import threading
 from dataclasses import dataclass
 from typing import (TYPE_CHECKING, Any, Dict, List, Literal, Mapping, Optional,
                     Tuple, Type, Union, cast, get_args)
@@ -1576,6 +1577,11 @@ class EngineArgs:
         #############################################################
         # Experimental Features - allow users to opt in.
 
+        # Signal Handlers requires running in main thread.
+        if (threading.current_thread() != threading.main_thread()
+                and _warn_or_fallback("Engine in background thread")):
+            return False
+
         # LoRA is supported on V1, but off by default for now.
         if self.enable_lora and _warn_or_fallback("LORA"):
             return False

vllm/engine/multiprocessing/engine.py

@@ -29,6 +29,8 @@ from vllm.engine.multiprocessing import (ENGINE_DEAD_ERROR, IPC_DATA_EXT,
 # yapf: enable
 from vllm.logger import init_logger
 from vllm.outputs import RequestOutput
+from vllm.transformers_utils.config import (
+    maybe_register_config_serialize_by_value)
 from vllm.usage.usage_lib import UsageContext
 from vllm.worker.model_runner_base import InputProcessingError
 
@@ -428,6 +430,9 @@ def run_mp_engine(vllm_config: VllmConfig, usage_context: UsageContext,
                   ipc_path: str, disable_log_stats: bool,
                   disable_log_requests: bool, engine_alive):
     try:
+        # Ensure we can serialize transformer config before spawning
+        maybe_register_config_serialize_by_value()
+
         engine = MQLLMEngine.from_vllm_config(
             vllm_config=vllm_config,
             usage_context=usage_context,

vllm/entrypoints/openai/api_server.py

@@ -82,6 +82,8 @@ from vllm.entrypoints.openai.serving_transcription import (
 from vllm.entrypoints.openai.tool_parsers import ToolParserManager
 from vllm.entrypoints.utils import load_aware_call, with_cancellation
 from vllm.logger import init_logger
+from vllm.transformers_utils.config import (
+    maybe_register_config_serialize_by_value)
 from vllm.usage.usage_lib import UsageContext
 from vllm.utils import (FlexibleArgumentParser, get_open_zmq_ipc_path,
                         is_valid_ipv6_address, set_ulimit)
@@ -221,6 +223,9 @@ async def build_async_engine_client_from_engine_args(
         # so we need to spawn a new process
         context = multiprocessing.get_context("spawn")
 
+        # Ensure we can serialize transformer config before spawning
+        maybe_register_config_serialize_by_value()
+
         # The Process can raise an exception during startup, which may
         # not actually result in an exitcode being reported. As a result
         # we use a shared variable to communicate the information.

vllm/lora/ops/triton_ops/__init__.py

@@ -1,15 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 
-from vllm.lora.ops.triton_ops.bgmv_expand import bgmv_expand
-from vllm.lora.ops.triton_ops.bgmv_expand_slice import bgmv_expand_slice
-from vllm.lora.ops.triton_ops.bgmv_shrink import bgmv_shrink
-from vllm.lora.ops.triton_ops.sgmv_expand import sgmv_expand
-from vllm.lora.ops.triton_ops.sgmv_shrink import sgmv_shrink  # noqa: F401
+from vllm.lora.ops.triton_ops.lora_expand import lora_expand
+from vllm.lora.ops.triton_ops.lora_kernel_metadata import LoRAKernelMeta
+from vllm.lora.ops.triton_ops.lora_shrink import lora_shrink
 
 __all__ = [
-    "bgmv_expand",
-    "bgmv_expand_slice",
-    "bgmv_shrink",
-    "sgmv_expand",
-    "sgmv_shrink",
+    "lora_expand",
+    "lora_shrink",
+    "LoRAKernelMeta",
 ]

vllm/lora/ops/triton_ops/bgmv_expand.py

@@ -1,188 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Based on:
-Chen, L., Ye, Z., Wu, Y., Zhuo, D., Ceze, L., & Krishnamurthy, A. (2023). 
-Punica: Multi-Tenant LoRA Serving. 
-https://arxiv.org/abs/2310.18547
-"""
-
-import torch
-import triton
-import triton.language as tl
-
-from vllm.utils import direct_register_custom_op
-
-from .utils import get_lora_op_configs
-
-
-@triton.jit
-def _bgmv_expand_kernel(
-    input_ptr,
-    lora_ptr,
-    out_ptr,
-    N,
-    K,
-    lora_indices,
-    xm_stride,
-    xk_stride,
-    l0_stride,
-    lora_k_stride,
-    lora_n_stride,
-    cm_stride,
-    cn_stride,
-    BLOCK_N: tl.constexpr,
-    BLOCK_K: tl.constexpr,
-    SPLIT_N: tl.constexpr,
-    EVEN_K: tl.constexpr,
-    ADD_INPUTS: tl.constexpr,
-    CAST_TYPE: tl.constexpr,
-):
-    """
-    GroupGEMV, additionally, introducing SPLIT_N can improve large hidden_size's
-    performance
-    """
-    pid_sn = tl.program_id(axis=0)
-    cur_batch = tl.program_id(axis=1)
-    lora_index = tl.load(lora_indices + cur_batch)
-    if lora_index == -1:
-        return
-    offset_k = tl.arange(0, BLOCK_K)
-    offset_n = tl.arange(0, BLOCK_N)
-    if EVEN_K:
-        tiled_a = tl.load(input_ptr + cur_batch * xm_stride +
-                          offset_k * xk_stride, )  # [BLOCK_K]
-    else:
-        tiled_a = tl.load(
-            input_ptr + cur_batch * xm_stride + offset_k * xk_stride,
-            mask=offset_k < K,
-            other=0,
-        )  # [BLOCK_K]
-    # N must be divisible by SPLIT_N
-    split_n_length = tl.cdiv(N, SPLIT_N)
-    if CAST_TYPE:
-        tiled_a = tiled_a.to(lora_ptr.dtype.element_ty)
-    # sliding  to  next row-block
-    b_ptr = (lora_ptr + l0_stride * lora_index +
-             pid_sn * split_n_length * lora_k_stride)
-    c_ptr = out_ptr + cur_batch * cm_stride + pid_sn * split_n_length
-    for n in range(0, split_n_length, BLOCK_N):
-        current_n = n + offset_n
-        current_n_c = tl.max_contiguous(current_n, BLOCK_N)
-        b_ptr_mask = (current_n[:, None] < split_n_length) & (offset_k[None, :]
-                                                              < K)
-        c_mask = current_n < split_n_length
-        tiled_b = tl.load(
-            b_ptr + current_n_c[:, None] * lora_k_stride +
-            offset_k[None, :] * lora_n_stride,
-            mask=b_ptr_mask,
-            other=0.0,
-        )  # [BLOCK_N,BLOCK_K]
-        if ADD_INPUTS:
-            tiled_out = tl.load(c_ptr + current_n * cn_stride,
-                                mask=c_mask,
-                                other=0.0)
-            accumulator = tl.sum(tiled_a * tiled_b, 1) + tiled_out
-        else:
-            accumulator = tl.sum(tiled_a * tiled_b, 1)
-
-        tl.store(c_ptr + current_n * cn_stride, accumulator, mask=c_mask)
-
-
-@torch.inference_mode()
-def _bgmv_expand(
-    inputs: torch.Tensor,
-    lora_b_weights: torch.Tensor,
-    output_tensor: torch.Tensor,
-    lora_indices_tensor: torch.Tensor,
-    add_inputs: bool = True,
-) -> None:
-    """
-    Args:
-        inputs (torch.Tensor): input tensor
-        lora_b_weights (torch.Tensor): lora'a weight
-        output_tensor (torch.Tensor): output tensor
-        lora_indices_tensor (torch.Tensor): (batch_size,). The LoRA index
-            corresponding to each batch, An index of -1 means no lora should be
-            applied.
-        batches (int): batch size
-        add_inputs (bool, optional):  Defaults to False, adds the final lora 
-            results to the output.
-    """
-    assert inputs.dtype in [torch.float16, torch.bfloat16, torch.float32]
-    assert lora_b_weights.dtype in [
-        torch.float16,
-        torch.bfloat16,
-    ]
-    assert inputs.size(1) == lora_b_weights.size(-1)
-
-    assert inputs.is_contiguous()
-    assert output_tensor.is_contiguous()
-
-    if lora_b_weights.ndim == 4:  # shape:(lora_num,1,size,rank)
-        assert lora_b_weights.size(1) == 1
-        lora_b_weights = lora_b_weights.squeeze(dim=1)
-    else:
-        assert lora_b_weights.ndim == 3  # shape:(lora_num,size,rank)
-    assert lora_b_weights.is_contiguous()
-
-    # TODO tuning this config
-    N, K = lora_b_weights.shape[-2:]  # K= rank,N=hidden_size
-    BLOCK_K = triton.next_power_of_2(K)
-    EVEN_K = K % BLOCK_K == 0
-    ADD_INPUTS = add_inputs
-    CAST_TYPE = False
-    if inputs.dtype == torch.float32 and lora_b_weights.dtype in [
-            torch.float16,
-            torch.bfloat16,
-    ]:
-        CAST_TYPE = True
-    batches = lora_indices_tensor.size(0)
-    config = get_lora_op_configs("expand", batches, N)
-    grid = lambda META: (
-        META["SPLIT_N"],
-        batches,
-    )
-    _bgmv_expand_kernel[grid](
-        inputs,
-        lora_b_weights,
-        output_tensor,
-        N,
-        K,
-        lora_indices_tensor,
-        inputs.stride(0),
-        inputs.stride(1),
-        lora_b_weights.stride(0),
-        lora_b_weights.stride(1),
-        lora_b_weights.stride(2),
-        output_tensor.stride(0),
-        output_tensor.stride(1),
-        BLOCK_K=BLOCK_K,
-        EVEN_K=EVEN_K,
-        ADD_INPUTS=ADD_INPUTS,
-        CAST_TYPE=CAST_TYPE,
-        **config,
-    )
-    return
-
-
-def bgmv_expand_fake(
-    inputs: torch.Tensor,
-    lora_b_weights: torch.Tensor,
-    output_tensor: torch.Tensor,
-    lora_indices_tensor: torch.Tensor,
-    add_inputs: bool = True,
-) -> None:
-    return
-
-
-try:
-    direct_register_custom_op(
-        op_name="bgmv_expand",
-        op_func=_bgmv_expand,
-        mutates_args=["output_tensor"],
-        fake_impl=bgmv_expand_fake,
-    )
-    bgmv_expand = torch.ops.vllm.bgmv_expand
-
-except AttributeError:
-    bgmv_expand = _bgmv_expand

vllm/lora/ops/triton_ops/bgmv_expand_slice.py

@@ -1,207 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Based on:
-Chen, L., Ye, Z., Wu, Y., Zhuo, D., Ceze, L., & Krishnamurthy, A. (2023). 
-Punica: Multi-Tenant LoRA Serving. 
-https://arxiv.org/abs/2310.18547
-"""
-
-import torch
-import triton
-import triton.language as tl
-
-from vllm.utils import direct_register_custom_op
-
-from .utils import get_lora_op_configs
-
-
-@triton.jit
-def _bgmv_expand_slice_kernel(
-    input_ptr,
-    lora_ptr,
-    out_ptr,
-    N,
-    K,
-    lora_indices,
-    xm_stride,
-    xk_stride,
-    l0_stride,
-    lora_k_stride,
-    lora_n_stride,
-    cm_stride,
-    cn_stride,
-    slice_offset,
-    BLOCK_N: tl.constexpr,
-    BLOCK_K: tl.constexpr,
-    SPLIT_N: tl.constexpr,
-    EVEN_K: tl.constexpr,
-    ADD_INPUTS: tl.constexpr,
-    CAST_TYPE: tl.constexpr,
-):
-    """
-    GroupGEMV, additionally, introducing SPLIT_N can improve large hidden_size's
-    performance
-    """
-    pid_sn = tl.program_id(axis=0)
-    cur_batch = tl.program_id(axis=1)
-    lora_index = tl.load(lora_indices + cur_batch)
-    if lora_index == -1:
-        return
-    offset_k = tl.arange(0, BLOCK_K)
-    offset_n = tl.arange(0, BLOCK_N)
-    if EVEN_K:
-        tiled_a = tl.load(input_ptr + cur_batch * xm_stride +
-                          offset_k * xk_stride, )  # [BLOCK_K]
-    else:
-        tiled_a = tl.load(
-            input_ptr + cur_batch * xm_stride + offset_k * xk_stride,
-            mask=offset_k < K,
-            other=0,
-        )  # [BLOCK_K]
-    # N must be divisible by SPLIT_N
-    split_n_length = tl.cdiv(N, SPLIT_N)
-    if CAST_TYPE:
-        tiled_a = tiled_a.to(lora_ptr.dtype.element_ty)
-    # sliding  to  next row-block
-    b_ptr = (lora_ptr + l0_stride * lora_index +
-             pid_sn * split_n_length * lora_k_stride)
-    c_ptr = (out_ptr + cur_batch * cm_stride + pid_sn * split_n_length +
-             slice_offset * cn_stride)
-
-    for n in range(0, split_n_length, BLOCK_N):
-        current_n = n + offset_n
-        b_ptr_mask = (current_n[:, None] < split_n_length) & (offset_k[None, :]
-                                                              < K)
-        c_mask = current_n < split_n_length
-        tiled_b = tl.load(
-            b_ptr + current_n[:, None] * lora_k_stride +
-            offset_k[None, :] * lora_n_stride,
-            mask=b_ptr_mask,
-            other=0.0,
-        )  # [BLOCK_N,BLOCK_K]
-
-        if ADD_INPUTS:
-            # explicitly pass in other=None to tell triton that masked values
-            # can be uninitialized. This is OK because the later tl.store
-            # operation uses the same mask, eliminating the risk of garbage
-            # values propagating
-            tiled_out = tl.load(c_ptr + current_n * cn_stride,
-                                mask=c_mask,
-                                other=None)
-            accumulator = tl.sum(tiled_a * tiled_b, 1) + tiled_out
-        else:
-            accumulator = tl.sum(tiled_a * tiled_b, 1)
-
-        tl.store(c_ptr + current_n * cn_stride, accumulator, mask=c_mask)
-
-
-@torch.inference_mode()
-def _bgmv_expand_slice(
-    inputs: torch.Tensor,
-    lora_b_weights: torch.Tensor,
-    output_tensor: torch.Tensor,
-    lora_indices_tensor: torch.Tensor,
-    slice_offset: int,
-    slice_size: int,
-    add_inputs: bool = True,
-) -> None:
-    """
-    Args:
-        inputs (torch.Tensor): input tensor
-        lora_b_weights (torch.Tensor): lora'b weight
-        output_tensor (torch.Tensor): output tensor
-        lora_indices_tensor (torch.Tensor): (batch_size,). The LoRA index
-            corresponding to each batch, An index of -1 means no lora should be
-            applied.
-        slice_offset (int): output_tensor's offset
-        slice_size (int): current output_tensor's size
-        batches (int): batch size
-        add_inputs (bool, optional): Defaults to False.
-    """
-    assert inputs.dtype in [torch.float16, torch.bfloat16, torch.float32]
-    assert lora_b_weights.dtype in [
-        torch.float16,
-        torch.bfloat16,
-    ]
-    assert inputs.size(1) == lora_b_weights.size(-1)
-
-    assert slice_size == lora_b_weights.size(-2)
-    assert inputs.is_contiguous()
-    assert output_tensor.is_contiguous()
-
-    if lora_b_weights.ndim == 4:  # shape:(lora_num,1,size,rank)
-        assert lora_b_weights.size(1) == 1
-        lora_b_weights = lora_b_weights.squeeze(dim=1)
-    else:
-        assert lora_b_weights.ndim == 3  # shape:(lora_num,size,rank)
-
-    assert lora_b_weights.is_contiguous()
-
-    # TODO tuning this config
-
-    N, K = lora_b_weights.shape[-2:]  # K= rank,N=hidden_size
-    BLOCK_K = triton.next_power_of_2(K)
-    EVEN_K = K % BLOCK_K == 0
-    ADD_INPUTS = add_inputs
-    CAST_TYPE = False
-    if inputs.dtype == torch.float32 and lora_b_weights.dtype in [
-            torch.float16,
-            torch.bfloat16,
-    ]:
-        CAST_TYPE = True
-
-    batches = lora_indices_tensor.size(0)
-
-    config = get_lora_op_configs("expand", batches, N)
-
-    grid = lambda META: (
-        META["SPLIT_N"],
-        batches,
-    )
-    _bgmv_expand_slice_kernel[grid](
-        inputs,
-        lora_b_weights,
-        output_tensor,
-        N,
-        K,
-        lora_indices_tensor,
-        inputs.stride(0),
-        inputs.stride(1),
-        lora_b_weights.stride(0),
-        lora_b_weights.stride(1),
-        lora_b_weights.stride(2),
-        output_tensor.stride(0),
-        output_tensor.stride(1),
-        slice_offset,
-        BLOCK_K=BLOCK_K,
-        EVEN_K=EVEN_K,
-        ADD_INPUTS=ADD_INPUTS,
-        CAST_TYPE=CAST_TYPE,
-        **config,
-    )
-    return
-
-
-def bgmv_expand_slice_fake(
-    inputs: torch.Tensor,
-    lora_b_weights: torch.Tensor,
-    output_tensor: torch.Tensor,
-    lora_indices_tensor: torch.Tensor,
-    slice_offset: int,
-    slice_size: int,
-    add_inputs: bool = True,
-) -> None:
-    return
-
-
-try:
-    direct_register_custom_op(
-        op_name="bgmv_expand_slice",
-        op_func=_bgmv_expand_slice,
-        mutates_args=["output_tensor"],
-        fake_impl=bgmv_expand_slice_fake,
-    )
-    bgmv_expand_slice = torch.ops.vllm.bgmv_expand_slice
-
-except AttributeError:
-    bgmv_expand_slice = _bgmv_expand_slice

vllm/lora/ops/triton_ops/bgmv_shrink.py

@@ -1,168 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Based on:
-Chen, L., Ye, Z., Wu, Y., Zhuo, D., Ceze, L., & Krishnamurthy, A. (2023). 
-Punica: Multi-Tenant LoRA Serving. 
-https://arxiv.org/abs/2310.18547
-"""
-
-import torch
-import triton
-import triton.language as tl
-
-from vllm.utils import direct_register_custom_op
-
-from .utils import get_lora_op_configs
-
-
-@triton.jit
-def _bgmv_shrink_kernel(
-    input_ptr,
-    lora_ptr,
-    out_ptr,
-    N,
-    K,
-    lora_indices,
-    scaling,
-    xm_stride,
-    xk_stride,
-    l0_stride,
-    lora_k_stride,
-    lora_n_stride,
-    cm_stride,
-    cn_stride,
-    BLOCK_N: tl.constexpr,
-    BLOCK_K: tl.constexpr,
-    SPLIT_K: tl.constexpr,
-):
-    """
-    GroupGEMV, additionally, introducing SPLIT-K can improve large hidden_size's
-    performance
-    """
-    pid_sk = tl.program_id(axis=0)
-    cur_batch = tl.program_id(axis=1)
-    lora_index = tl.load(lora_indices + cur_batch)
-    if lora_index == -1:
-        return
-
-    offset_n = tl.arange(0, BLOCK_N)
-    offset_k = tl.arange(0, BLOCK_K) + pid_sk * BLOCK_K
-    a_ptr = input_ptr + cur_batch * xm_stride
-    b_ptr = lora_ptr + l0_stride * lora_index
-    accumulator = tl.zeros((BLOCK_N, ), dtype=tl.float32)
-    for k in range(0, K, BLOCK_K * SPLIT_K):
-        current_k = k + offset_k
-        current_k_c = tl.max_contiguous(current_k, BLOCK_K)
-        tiled_a = tl.load(
-            a_ptr + current_k_c,
-            mask=current_k < K,
-            other=0.0,
-        )  # [BLOCK_K]
-        b_ptr_mask = (offset_n[:, None] < N) & (current_k[None, :] < K)
-
-        tiled_b = tl.load(
-            b_ptr + offset_n[:, None] * lora_k_stride +
-            current_k[None, :] * lora_n_stride,
-            mask=b_ptr_mask,
-            other=0.0,
-        )  # [BLOCK_N,BLOCK_K]
-
-        accumulator += tl.sum(tiled_a * tiled_b, 1)
-    accumulator *= scaling
-    offset_cn = tl.arange(0, BLOCK_N)
-    c_ptr = out_ptr + cur_batch * cm_stride + offset_cn * cn_stride
-    c_mask = offset_cn < N
-    if SPLIT_K == 1:
-        tl.store(c_ptr, accumulator, mask=c_mask)
-    else:
-        tl.atomic_add(c_ptr, accumulator, mask=c_mask)
-
-
-@torch.inference_mode()
-def _bgmv_shrink(
-    inputs: torch.Tensor,
-    lora_a_weights: torch.Tensor,
-    output_tensor: torch.Tensor,
-    lora_indices_tensor: torch.Tensor,
-    scaling: float = 1.0,
-) -> None:
-    """
-    Args:
-        inputs (torch.Tensor): input tensor
-        lora_a_weights (torch.Tensor): lora'a weight
-        output_tensor (torch.Tensor): output tensor
-        lora_indices_tensor (torch.Tensor): (batch_size,). The LoRA index
-            corresponding to each batch. An index of -1 means no lora should be
-            applied.
-        batches (int): batch size
-        scaling (float):  Scaling factor.
-    """
-    assert inputs.dtype == lora_a_weights.dtype
-    assert inputs.dtype in [torch.float16, torch.bfloat16]
-    assert lora_a_weights.dtype in [
-        torch.float16,
-        torch.bfloat16,
-    ]
-    assert inputs.size(1) == lora_a_weights.size(-1)
-    assert inputs.is_contiguous()
-
-    if lora_a_weights.ndim == 4:  # shape:(lora_num,1,rank, size)
-        assert lora_a_weights.size(1) == 1
-        lora_a_weights = lora_a_weights.squeeze(dim=1)
-    else:
-        assert lora_a_weights.ndim == 3  # shape:(lora_num,rank, size)
-    assert lora_a_weights.is_contiguous()
-    assert output_tensor.is_contiguous()
-    # TODO tuning this config
-    batches = lora_indices_tensor.size(0)
-    N, K = lora_a_weights.shape[-2:]  # K=hidden_size,N=rank
-    BLOCK_N = triton.next_power_of_2(N)
-    # First try to load optimal config from the file
-    config = get_lora_op_configs("bgmv_shrink", batches, K)
-
-    grid = lambda META: (
-        META["SPLIT_K"],
-        batches,
-    )
-    _bgmv_shrink_kernel[grid](
-        inputs,
-        lora_a_weights,
-        output_tensor,
-        N,
-        K,
-        lora_indices_tensor,
-        scaling,
-        inputs.stride(0),
-        inputs.stride(1),
-        lora_a_weights.stride(0),
-        lora_a_weights.stride(1),
-        lora_a_weights.stride(2),
-        output_tensor.stride(0),
-        output_tensor.stride(1),
-        BLOCK_N=BLOCK_N,
-        **config,
-    )
-    return
-
-
-def bgmv_shrink_fake(
-    inputs: torch.Tensor,
-    lora_a_weights: torch.Tensor,
-    output_tensor: torch.Tensor,
-    lora_indices_tensor: torch.Tensor,
-    scaling: float = 1.0,
-) -> None:
-    return
-
-
-try:
-    direct_register_custom_op(
-        op_name="bgmv_shrink",
-        op_func=_bgmv_shrink,
-        mutates_args=["output_tensor"],
-        fake_impl=bgmv_shrink_fake,
-    )
-    bgmv_shrink = torch.ops.vllm.bgmv_shrink
-
-except AttributeError:
-    bgmv_shrink = _bgmv_shrink

vllm/lora/ops/triton_ops/lora_expand.py

@@ -18,7 +18,7 @@ from vllm.utils import direct_register_custom_op
 
 
 @triton.jit
-def _v1_expand_kernel(
+def _lora_expand_kernel(
         input_ptr,
         lora_ptr,
         out_ptr,
@@ -125,7 +125,7 @@ def _v1_expand_kernel(
 
 
 @torch.inference_mode()
-def _v1_expand(
+def _lora_expand(
     inputs: torch.Tensor,  # shape [num_slices, num_tokens, lora_rank]
     lora_b_weights: List[
         torch.Tensor],  # shape [num_lora, hidden_size, lora_rank]
@@ -216,7 +216,7 @@ def _v1_expand(
         MAX_LORAS,
     )
 
-    _v1_expand_kernel[grid](
+    _lora_expand_kernel[grid](
         inputs,
         lora_ptr_tensor,
         output_tensor,
@@ -254,7 +254,7 @@ def _v1_expand(
     return
 
 
-def _v1_expand_fake(
+def _lora_expand_fake(
     inputs: torch.Tensor,
     lora_b_weights: List[torch.Tensor],
     output_tensor: torch.Tensor,
@@ -271,12 +271,12 @@ def _v1_expand_fake(
 
 try:
     direct_register_custom_op(
-        op_name="v1_expand",
-        op_func=_v1_expand,
+        op_name="lora_expand",
+        op_func=_lora_expand,
         mutates_args=["output_tensor"],
-        fake_impl=_v1_expand_fake,
+        fake_impl=_lora_expand_fake,
     )
-    v1_expand = torch.ops.vllm.v1_expand
+    lora_expand = torch.ops.vllm.lora_expand
 
 except AttributeError:
-    v1_expand = _v1_expand
+    lora_expand = _lora_expand

vllm/lora/ops/triton_ops/lora_kernel_metadata.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 """
-V1 LoRA kernels metadata preparation utilities.
+LoRA kernels metadata preparation utilities.
 """
 
 from dataclasses import dataclass
@@ -10,7 +10,7 @@ import torch
 
 
 @dataclass
-class V1KernelMeta:
+class LoRAKernelMeta:
     token_lora_mapping: torch.Tensor
     token_indices_sorted_by_lora_ids: torch.Tensor
     active_lora_ids: torch.Tensor
@@ -19,7 +19,7 @@ class V1KernelMeta:
 
     @staticmethod
     def make(max_loras: int, max_num_tokens: int,
-             device: Union[torch.device, str]) -> "V1KernelMeta":
+             device: Union[torch.device, str]) -> "LoRAKernelMeta":
 
         token_lora_mapping = torch.empty(max_num_tokens,
                                          dtype=torch.int32,
@@ -47,7 +47,7 @@ class V1KernelMeta:
         lora_token_start_loc = torch.zeros(max_loras + 2,
                                            dtype=torch.int32,
                                            device=device)
-        return V1KernelMeta(
+        return LoRAKernelMeta(
             token_lora_mapping=token_lora_mapping,
             token_indices_sorted_by_lora_ids=token_indices_sorted_by_lora_ids,
             active_lora_ids=active_lora_ids,
@@ -105,7 +105,7 @@ class V1KernelMeta:
         This function returns the kernel metadata required for the current
         forward pass execution of the kernel. The function returns all the
         metadata required by the kernel, in order, as a tuple, so it can be
-        unpacked directly during the v1_shrink/v1_expand function call.
+        unpacked directly during the lora_shrink/lora_expand function call.
 
         Args:
             token_nums (int): Number of input tokens in the current forward

vllm/lora/ops/triton_ops/lora_shrink.py

@@ -18,15 +18,15 @@ from vllm.utils import direct_register_custom_op
 
 
 @triton.jit
-def _v1_shrink_kernel(input_ptr, lora_ptr, out_ptr, M, N, K,
+def _lora_shrink_kernel(input_ptr, lora_ptr, out_ptr, M, N, K,
                         token_indices_sorted_by_lora_ids, num_tokens_per_lora,
-                      lora_token_start_loc, lora_ids, scaling, input_d0_stride,
-                      input_d1_stride, lora_d0_stride, lora_d1_stride,
-                      lora_d2_stride, output_d0_stride, output_d1_stride,
-                      output_d2_stride, BLOCK_M: tl.constexpr,
-                      BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
-                      EVEN_K: tl.constexpr, SPLIT_K: tl.constexpr,
-                      SLICE_NUM: tl.constexpr):
+                        lora_token_start_loc, lora_ids, scaling,
+                        input_d0_stride, input_d1_stride, lora_d0_stride,
+                        lora_d1_stride, lora_d2_stride, output_d0_stride,
+                        output_d1_stride, output_d2_stride,
+                        BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr,
+                        BLOCK_K: tl.constexpr, EVEN_K: tl.constexpr,
+                        SPLIT_K: tl.constexpr, SLICE_NUM: tl.constexpr):
 
     cta_n_num = tl.cdiv(N, BLOCK_N)
     cta_m_num = tl.cdiv(M, BLOCK_M)
@@ -96,7 +96,7 @@ def _v1_shrink_kernel(input_ptr, lora_ptr, out_ptr, M, N, K,
 
 
 @torch.inference_mode()
-def _v1_shrink(
+def _lora_shrink(
     inputs: torch.Tensor,  #  shape [num_tokens, hidden_size]
     lora_a_weights: List[
         torch.Tensor],  # shape [num_loras, lora_rank, hidden_size]
@@ -174,7 +174,7 @@ def _v1_shrink(
         MAX_LORAS,
     )
 
-    _v1_shrink_kernel[grid](
+    _lora_shrink_kernel[grid](
         inputs,
         lora_ptr_tensor,
         output_tensor,
@@ -209,7 +209,7 @@ def _v1_shrink(
     return
 
 
-def _v1_shrink_fake(
+def _lora_shrink_fake(
     inputs: torch.Tensor,
     lora_a_weights: List[torch.Tensor],
     output_tensor: torch.Tensor,
@@ -225,12 +225,12 @@ def _v1_shrink_fake(
 
 try:
     direct_register_custom_op(
-        op_name="v1_shrink",
-        op_func=_v1_shrink,
+        op_name="lora_shrink",
+        op_func=_lora_shrink,
         mutates_args=["output_tensor"],
-        fake_impl=_v1_shrink_fake,
+        fake_impl=_lora_shrink_fake,
     )
-    v1_shrink = torch.ops.vllm.v1_shrink
+    lora_shrink = torch.ops.vllm.lora_shrink
 
 except AttributeError:
-    v1_shrink = _v1_shrink
+    lora_shrink = _lora_shrink

vllm/lora/ops/triton_ops/sgmv_expand.py

@@ -1,249 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Based on:
-Chen, L., Ye, Z., Wu, Y., Zhuo, D., Ceze, L., & Krishnamurthy, A. (2023).
-Punica: Multi-Tenant LoRA Serving.
-https://arxiv.org/abs/2310.18547
-"""
-
-from typing import List
-
-import torch
-import triton
-import triton.language as tl
-
-from vllm.utils import direct_register_custom_op
-
-from .kernel_utils import do_expand_kernel
-from .utils import _get_lora_b_ptr
-
-
-@triton.jit
-def _sgmv_expand_kernel(
-        input_ptr,
-        lora_ptr,
-        out_ptr,
-        N,
-        K,
-        b_seq_start_loc,
-        seq_lens,
-        lora_indices,
-        slice_start_loc,
-        input_d0_stride,
-        input_d1_stride,
-        input_d2_stride,  # 1
-        ls_d0_ptr,
-        ls_d1_ptr,
-        ls_d2_ptr,  # 1
-        output_d0_stride,
-        output_d1_stride,  # 1
-        output_hs_ptr,
-        BLOCK_M: tl.constexpr,
-        BLOCK_N: tl.constexpr,
-        BLOCK_K: tl.constexpr,
-        EVEN_K: tl.constexpr,
-        ADD_INPUTS: tl.constexpr,
-        CAST_TYPE: tl.constexpr,
-        SLICE_NUM: tl.constexpr,
-        SAME_STRIDE: tl.constexpr):
-    """
-
-    Similar to the 'sgmv_expand' operator, but with an added parameter
-    'slice_offset'. The reason for not reusing the 'sgmv_expand' operator
-    might be that in the future, we could implement a fusion operator to
-    achieve the current functionality instead of having to call it multiple
-    times.
-    """
-    pid = tl.program_id(axis=0)
-    cur_batch = tl.program_id(axis=1)
-    slice_id = tl.program_id(axis=2)
-    cta_n_num = tl.cdiv(N, BLOCK_N)
-    # When the output dimensions of each slice are the same,cur_n=N, otherwise
-    # cur_n=tl.load(output_hs_ptr + slice_id), this situation exists in GQA's
-    # qkv linear.
-    curr_N = N if SAME_STRIDE else tl.load(output_hs_ptr + slice_id)
-    pid_m = pid // cta_n_num
-    pid_n = pid % cta_n_num
-
-    M = tl.load(seq_lens + cur_batch)
-    if pid_m * BLOCK_M >= M:
-        return
-    if pid_n * BLOCK_N >= curr_N:
-        return
-    lora_index = tl.load(lora_indices + cur_batch)
-    if lora_index == -1:
-        return
-
-    m_offset = tl.load(b_seq_start_loc + cur_batch)
-
-    cta_m_len = min(BLOCK_M, M - (pid_m * BLOCK_M))
-    cta_m_offset = m_offset + (pid_m * BLOCK_M)
-    offset_m = tl.arange(0, BLOCK_M)
-    ram = cta_m_offset + tl.max_contiguous(
-        tl.multiple_of(offset_m % cta_m_len, BLOCK_M), BLOCK_M)
-    do_expand_kernel(
-        pid_n,
-        lora_index,
-        slice_id,
-        input_ptr,
-        lora_ptr,
-        out_ptr,
-        curr_N,
-        K,
-        cta_m_len,
-        ram,  # array identifying the rows of Input ptr to operate on
-        slice_start_loc,
-        # input ptr strides
-        input_d0_stride,
-        input_d1_stride,
-        input_d2_stride,
-        # lora ptr strides
-        ls_d0_ptr,
-        ls_d1_ptr,
-        ls_d2_ptr,
-        # out ptr strides
-        output_d0_stride,
-        output_d1_stride,
-        # constants
-        BLOCK_M,
-        BLOCK_N,
-        BLOCK_K,
-        SAME_STRIDE,
-        SLICE_NUM,
-        EVEN_K,
-        CAST_TYPE,
-        ADD_INPUTS,
-    )
-
-
-@torch.inference_mode()
-def _sgmv_expand(
-    inputs: torch.Tensor,
-    lora_b_weights: List[torch.Tensor],
-    output_tensor: torch.Tensor,
-    b_seq_start_loc: torch.Tensor,
-    seq_len_tensor: torch.Tensor,
-    lora_indices_tensor: torch.Tensor,
-    batches: int,
-    max_seq_length: int,
-    token_nums: int,
-    offset_start: int = 0,
-    add_inputs: bool = False,
-) -> None:
-    """
-    Args:
-        inputs (torch.Tensor): input tensor
-        lora_b_weights (List[torch.Tensor]): lora'b weight
-        output_tensor (torch.Tensor): output tensor
-        b_seq_start_loc (torch.Tensor): (batch_size,). The cumulative
-            sequence lengths of the sequences in the batch, used to index
-            into sequence. E.g., if the sequence length is [4, 6], it is
-            [0, 4].
-        seq_len_tensor (torch.Tensor): (batch_size,). Record the sequence
-            length of the sequences in the batch.
-        lora_indices_tensor (torch.Tensor): (batch_size,). The LoRA index
-            corresponding to each batch. An index of -1 means no lora should be
-            applied.
-        batches (int): batch size
-        max_seq_length (int): The max sequence lengths of the sequences in the 
-            batch.
-        token_nums (int): The token numbers in the batch. Used to verify if the 
-            token numbers in the inputs matches the one in the metadata.
-        offset_start (int, optional): Offset start for output_tensor. 
-            Defaults to 0.
-        add_inputs (bool, optional): Whether to add the input tensor to the 
-            output tensor. Defaults to False.
-    """
-    assert inputs.dtype in [torch.float16, torch.bfloat16, torch.float32]
-    for weight in lora_b_weights:
-        assert weight.dtype in [torch.float16, torch.bfloat16]
-
-    assert inputs.size(1) == token_nums
-    assert inputs.size(0) == len(lora_b_weights)
-
-    assert b_seq_start_loc.size(0) == batches
-    assert lora_indices_tensor.size(0) == batches
-    assert output_tensor.is_contiguous()
-    (slice_start_tensor, lora_ptr_tensor, lora_strides_d0_tensor,
-     lora_strides_d1_tensor, lora_strides_d2_tensor, hidden_sizes_tensor,
-     same_stride, MAX_N) = _get_lora_b_ptr(lora_b_weights, offset_start,
-                                           b_seq_start_loc.device)
-
-    # TODO tuning this config
-    K = lora_b_weights[0].shape[-1]  # K= rank
-
-    BLOCK_M = 64
-    BLOCK_N = 128
-    BLOCK_K = 16
-    EVEN_K = K % BLOCK_K == 0
-    ADD_INPUTS = add_inputs
-    CAST_TYPE = False
-
-    if inputs.dtype == torch.float32 and lora_b_weights[0].dtype in [
-            torch.float16,
-            torch.bfloat16,
-    ]:
-        CAST_TYPE = True
-    grid = (
-        triton.cdiv(max_seq_length, BLOCK_M) * triton.cdiv(MAX_N, BLOCK_N),
-        batches,
-        len(lora_b_weights),
-    )
-    _sgmv_expand_kernel[grid](
-        inputs,
-        lora_ptr_tensor,
-        output_tensor,
-        MAX_N,
-        K,
-        b_seq_start_loc,
-        seq_len_tensor,
-        lora_indices_tensor,
-        slice_start_tensor,
-        inputs.stride(0),
-        inputs.stride(1),
-        inputs.stride(2),
-        lora_strides_d0_tensor,
-        lora_strides_d1_tensor,
-        lora_strides_d2_tensor,
-        output_tensor.stride(0),
-        output_tensor.stride(1),
-        hidden_sizes_tensor,
-        BLOCK_M,
-        BLOCK_N,
-        BLOCK_K,
-        EVEN_K,
-        ADD_INPUTS,
-        CAST_TYPE,
-        len(lora_b_weights),
-        same_stride,
-    )
-    return
-
-
-def _sgmv_expand_fake(
-    inputs: torch.Tensor,
-    lora_b_weights: List[torch.Tensor],
-    output_tensor: torch.Tensor,
-    b_seq_start_loc: torch.Tensor,
-    seq_len_tensor: torch.Tensor,
-    lora_indices_tensor: torch.Tensor,
-    batches: int,
-    max_seq_length: int,
-    token_nums: int,
-    offset_start: int = 0,
-    add_inputs: bool = False,
-) -> None:
-    return
-
-
-try:
-    direct_register_custom_op(
-        op_name="sgmv_expand",
-        op_func=_sgmv_expand,
-        mutates_args=["output_tensor"],
-        fake_impl=_sgmv_expand_fake,
-    )
-    sgmv_expand = torch.ops.vllm.sgmv_expand
-
-except AttributeError:
-    sgmv_expand = _sgmv_expand

vllm/lora/ops/triton_ops/sgmv_shrink.py

@@ -1,224 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Based on:
-Chen, L., Ye, Z., Wu, Y., Zhuo, D., Ceze, L., & Krishnamurthy, A. (2023). 
-Punica: Multi-Tenant LoRA Serving. 
-https://arxiv.org/abs/2310.18547
-"""
-
-from typing import List
-
-import torch
-import triton
-import triton.language as tl
-
-from vllm.utils import direct_register_custom_op
-
-from .kernel_utils import do_shrink_kernel
-from .utils import _get_lora_a_ptr
-
-
-@triton.jit
-def _sgmv_shrink_kernel(
-        input_ptr,
-        lora_ptr,  #1-3
-        out_ptr,
-        N,
-        K,
-        b_seq_start_loc,
-        seq_lens,
-        lora_indices,
-        scaling,
-        input_d0_stride,
-        input_d1_stride,  # 1
-        lora_d0_stride,
-        lora_d1_stride,
-        lora_d2_stride,  # 1
-        output_d0_stride,
-        output_d1_stride,
-        output_d2_stride,  # 1 
-        BLOCK_M: tl.constexpr,
-        BLOCK_N: tl.constexpr,
-        BLOCK_K: tl.constexpr,
-        EVEN_K: tl.constexpr,
-        SPLIT_K: tl.constexpr,
-        SLICE_NUM: tl.constexpr):
-    """
-    The sgmv's shrink triton kernel is based on GroupGEMM+SPLIT-K.
-    The GEMM of Multi-LoRA can be considered as GroupGEMM. Additionally,
-    introducing SPLIT-K can improve performance
-    """
-    pid = tl.program_id(axis=0)
-    pid_mix = tl.program_id(axis=1)
-    cur_batch = tl.program_id(axis=2)
-    cta_n_num = tl.cdiv(N, BLOCK_N)
-    pid_m = pid // cta_n_num
-    pid_n = pid % cta_n_num
-    if SLICE_NUM == 1:
-        slice_id: tl.constexpr = 0
-        pid_sk = tl.program_id(axis=1)
-    else:
-        pid_mix = tl.program_id(axis=1)
-        slice_id = pid_mix // SPLIT_K
-        pid_sk = pid_mix % SPLIT_K
-
-    M = tl.load(seq_lens + cur_batch)
-    if pid_m * BLOCK_M >= M:
-        return
-    lora_index = tl.load(lora_indices + cur_batch)
-    if lora_index == -1:
-        return
-
-    m_offset = tl.load(b_seq_start_loc + cur_batch)
-
-    cta_m_len = min(BLOCK_M, M - (pid_m * BLOCK_M))
-    cta_m_offset = m_offset + (pid_m * BLOCK_M)
-    offset_m = tl.arange(0, BLOCK_M)
-    ram = cta_m_offset + tl.max_contiguous(
-        tl.multiple_of(offset_m % cta_m_len, BLOCK_M), BLOCK_M)
-
-    do_shrink_kernel(
-        pid_n,
-        pid_sk,
-        slice_id,
-        lora_index,
-        input_ptr,
-        lora_ptr,
-        out_ptr,
-        N,
-        K,
-        cta_m_len,
-        ram,
-        # input strides
-        input_d0_stride,
-        input_d1_stride,
-        # lora strides
-        lora_d0_stride,
-        lora_d1_stride,
-        lora_d2_stride,
-        # output strides
-        output_d0_stride,
-        output_d1_stride,
-        output_d2_stride,
-        scaling,
-        BLOCK_M,
-        BLOCK_N,
-        BLOCK_K,
-        EVEN_K,
-        SPLIT_K,
-        SLICE_NUM)
-
-
-@torch.inference_mode()
-def _sgmv_shrink(
-    inputs: torch.Tensor,
-    lora_a_weights: List[torch.Tensor],
-    output_tensor: torch.Tensor,
-    b_seq_start_loc: torch.Tensor,
-    seq_len_tensor: torch.Tensor,
-    lora_indices_tensor: torch.Tensor,
-    batches: int,
-    max_seq_length: int,
-    token_nums: int,
-    scaling: float,
-) -> None:
-    """
-    Args:
-        inputs (torch.Tensor): input tensor
-        lora_a_weights (List[torch.Tensor]): lora'a weight
-        output_tensor (torch.Tensor): output tensor
-        b_seq_start_loc (torch.Tensor): (batch_size,). The cumulative
-            sequence lengths of the sequences in the batch, used to index
-            into sequence. E.g., if the sequence length is [4, 6], it is
-            [0, 4].
-        seq_len_tensor (torch.Tensor): (batch_size,). Record the sequence
-            length of the sequences in the batch.
-        lora_indices_tensor (torch.Tensor): (batch_size,). The LoRA index
-            corresponding to each batch. An index of -1 means no lora should be
-            applied.
-        batches (int): batch size
-        max_seq_length (int): The max sequence lengths of the sequences in the 
-            batch.
-        token_nums (int): The token numbers in the batch. Used to verify if the 
-            token numbers in the inputs matches the one in the metadata.
-        scaling (float): Scaling factor.
-    """
-    assert inputs.dtype == lora_a_weights[0].dtype
-    assert inputs.dtype in [torch.float16, torch.bfloat16]
-    for weight in lora_a_weights:
-        assert weight.dtype in [torch.float16, torch.bfloat16]
-
-    assert inputs.size(0) == token_nums
-    assert inputs.size(1) == lora_a_weights[0].size(-1)
-    assert b_seq_start_loc.size(0) == batches
-    assert lora_indices_tensor.size(0) == batches
-    assert inputs.is_contiguous()
-    assert output_tensor.is_contiguous()
-    (lora_ptr_tensor, lora_strides_d0, lora_strides_d1,
-     lora_strides_d2) = _get_lora_a_ptr(lora_a_weights, b_seq_start_loc.device)
-    # TODO tuning this config
-    N, K = lora_a_weights[0].shape[-2:]  # K=hidden_size,N=rank
-    BLOCK_M = 32
-    BLOCK_N = 16
-    BLOCK_K = 32
-    SPLIT_K = 8
-    EVEN_K = K % (BLOCK_K * SPLIT_K) == 0
-    grid = (
-        triton.cdiv(max_seq_length, BLOCK_M) * triton.cdiv(N, BLOCK_N),
-        SPLIT_K * len(lora_a_weights),
-        batches,
-    )
-    _sgmv_shrink_kernel[grid](
-        inputs,
-        lora_ptr_tensor,
-        output_tensor,
-        N,
-        K,
-        b_seq_start_loc,
-        seq_len_tensor,
-        lora_indices_tensor,
-        scaling,
-        inputs.stride(0),
-        inputs.stride(1),
-        lora_strides_d0,
-        lora_strides_d1,
-        lora_strides_d2,
-        output_tensor.stride(0),
-        output_tensor.stride(1),
-        output_tensor.stride(2),
-        BLOCK_M,
-        BLOCK_N,
-        BLOCK_K,
-        EVEN_K,
-        SPLIT_K,
-        len(lora_a_weights),
-    )
-    return
-
-
-def sgmv_shrink_fake(
-    inputs: torch.Tensor,
-    lora_a_weights: List[torch.Tensor],
-    output_tensor: torch.Tensor,
-    b_seq_start_loc: torch.Tensor,
-    seq_len_tensor: torch.Tensor,
-    lora_indices_tensor: torch.Tensor,
-    batches: int,
-    max_seq_length: int,
-    token_nums: int,
-    scaling: float,
-) -> None:
-    return
-
-
-try:
-    direct_register_custom_op(
-        op_name="sgmv_shrink",
-        op_func=_sgmv_shrink,
-        mutates_args=["output_tensor"],
-        fake_impl=sgmv_shrink_fake,
-    )
-    sgmv_shrink = torch.ops.vllm.sgmv_shrink
-
-except AttributeError:
-    sgmv_shrink = _sgmv_shrink

vllm/lora/ops/triton_ops/utils.py

@@ -1,55 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
 
-import functools
 from typing import Dict, List, Tuple
 
 import torch
 
-
-@functools.lru_cache
-def _get_op_configs(op_type: str, batch: int, hidden_size: int):
-    # TODO: add optimal configurations
-    return None
-
-
-def _check_divisibility(hidden_size: int):
-    # The bgmv_expand kernel requires that the hidden_size be divisible by
-    # the number below.
-    divisibility = [2, 4, 8, 16, 32, 64]
-    divisibility.sort(reverse=True)
-    for div in divisibility:
-        if hidden_size % div == 0:
-            return div
-    # hidden_size is an odd number
-    return 1
-
-
-def _get_default_config(op_type: str, batch: int, hidden_size: int):
-    if op_type == "expand":
-        return {
-            "BLOCK_N": 256,
-            "SPLIT_N": _check_divisibility(hidden_size),
-            "num_warps": 8
-        }
-    else:
-        return {"BLOCK_K": 256, "SPLIT_K": 64, "num_warps": 8}
-
-
-def get_lora_op_configs(op_type: str, batch: int,
-                        hidden_size: int) -> Dict[str, int]:
-    """Inspired by `fused_moe_kernel`
-    The return value will be a dictionary mapping an irregular grid of batch 
-    sizes and hidden_size to configurations of the bgmv-related kernel. 
-    NOTE: It currently only supports the default configuration. We plan to 
-    generate optimal configurations for different hardware in the future using 
-    scripts similar to `benchmark_moe.py`.
-    """
-    config = _get_op_configs(op_type, batch, hidden_size)
-    if not config:
-        config = _get_default_config(op_type, batch, hidden_size)
-    return config
-
-
 _LORA_A_PTR_DICT: Dict[Tuple[int, ...], Tuple[torch.tensor, ...]] = {}
 _LORA_B_PTR_DICT: Dict[Tuple[int, ...], Tuple[torch.tensor, ...]] = {}
 

vllm/lora/ops/triton_ops/v1/__init__.py

@@ -1,11 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-from vllm.lora.ops.triton_ops.v1.v1_expand import v1_expand
-from vllm.lora.ops.triton_ops.v1.v1_kernel_metadata import V1KernelMeta
-from vllm.lora.ops.triton_ops.v1.v1_shrink import v1_shrink
-
-__all__ = [
-    "v1_expand",
-    "v1_shrink",
-    "V1KernelMeta",
-]

vllm/lora/punica_wrapper/punica_gpu.py

@@ -10,20 +10,12 @@ from typing import TYPE_CHECKING, List, Optional, Tuple, Union, final
 
 import torch
 
-import vllm.envs as env
 from vllm.lora.layers import LoRAMapping
 from vllm.triton_utils import HAS_TRITON
 
 if HAS_TRITON:
-    if env.VLLM_USE_V1:
-        from vllm.lora.ops.triton_ops.v1 import (V1KernelMeta, v1_expand,
-                                                 v1_shrink)
-    else:
-        from vllm.lora.ops.triton_ops import bgmv_expand
-        from vllm.lora.ops.triton_ops import bgmv_expand_slice
-        from vllm.lora.ops.triton_ops import bgmv_shrink
-        from vllm.lora.ops.triton_ops import sgmv_expand
-        from vllm.lora.ops.triton_ops import sgmv_shrink
+    from vllm.lora.ops.triton_ops import (LoRAKernelMeta, lora_expand,
+                                          lora_shrink)
 
 from .punica_base import PunicaWrapperBase
 
@@ -32,57 +24,8 @@ if TYPE_CHECKING:
     from vllm.lora.models import LongContextLoRAContext
 
 
-class V1KernelMixin:
-
-    def _v1_make_metadata(self, max_loras: int, max_num_batched_tokens: int,
-                          max_batches: int, device: Union[torch.device, str]):
-        self.token_mapping_v1_meta = V1KernelMeta.make(max_loras,
-                                                       max_num_batched_tokens,
-                                                       device=device)
-        self.prompt_mapping_v1_meta = V1KernelMeta.make(max_loras,
-                                                        max_batches,
-                                                        device=device)
-
-    def _v1_prepare_metadata_tensors(self, token_lora_indices: torch.Tensor,
-                                     sampler_indices: torch.Tensor):
-        self.token_mapping_v1_meta.prepare_tensors(token_lora_indices)
-        self.prompt_mapping_v1_meta.prepare_tensors(sampler_indices)
-
-    def _v1_apply_shrink(
-        self,
-        y: torch.Tensor,
-        x: torch.Tensor,
-        w_t_all: Tuple[torch.Tensor, ...],
-        scale: float,
-    ):
-        v1_shrink(
-            x,
-            w_t_all,
-            y,
-            *self.token_mapping_v1_meta.meta_args(x.size(0)),
-            scale,
-        )
-
-    def _v1_apply_expand(
-        self,
-        y: torch.Tensor,
-        x: torch.Tensor,
-        w_t_all: Tuple[torch.Tensor, ...],
-        offset_start: int,
-        add_inputs: bool,
-    ):
-        v1_expand(
-            x,
-            w_t_all,
-            y,
-            *self.token_mapping_v1_meta.meta_args(x.size(0)),
-            offset_start=offset_start,
-            add_inputs=add_inputs,
-        )
-
-
 @final
-class PunicaWrapperGPU(PunicaWrapperBase, V1KernelMixin):
+class PunicaWrapperGPU(PunicaWrapperBase):
     """
     PunicaWrapperGPU is designed to manage and provide metadata for the punica 
     kernel. The main function is to maintain the state information for 
@@ -96,9 +39,12 @@ class PunicaWrapperGPU(PunicaWrapperBase, V1KernelMixin):
 
         self.max_loras = kwargs['max_loras']
 
-        if env.VLLM_USE_V1:
-            self._v1_make_metadata(self.max_loras, max_num_batched_tokens,
-                                   max_batches, device)
+        self.token_mapping_meta = LoRAKernelMeta.make(self.max_loras,
+                                                      max_num_batched_tokens,
+                                                      device=device)
+        self.prompt_mapping_meta = LoRAKernelMeta.make(self.max_loras,
+                                                       max_batches,
+                                                       device=device)
 
     def update_metadata(
             self,
@@ -110,83 +56,18 @@ class PunicaWrapperGPU(PunicaWrapperBase, V1KernelMixin):
             long_lora_context: Optional["LongContextLoRAContext"] = None,
             **kwargs):
 
-        if env.VLLM_USE_V1:
         self.is_prefill = mapping.is_prefill
         self._update_base_metadata(mapping, lora_index_to_id, max_loras,
                                    vocab_size, extra_vocab_size,
                                    long_lora_context)
-            self._v1_prepare_metadata_tensors(self.token_lora_indices,
-                                              self.sampler_indices)
-        else:
-            # Forward to base class update_metadata
-            PunicaWrapperBase.update_metadata(self, mapping, lora_index_to_id,
-                                              max_loras, vocab_size,
-                                              extra_vocab_size,
-                                              long_lora_context, **kwargs)
 
-    def _apply_shrink_prefill(
-        self,
-        y: torch.Tensor,
-        x: torch.Tensor,
-        w_t_all: Tuple[torch.Tensor, ...],
-        scale: float,
-    ):
-        #No LoRA request, so return directly
-        if self.no_lora:
-            return
-        sgmv_shrink(
-            x,
-            w_t_all,
-            y,
-            *self.prefill_metadata,
-            scale,
-        )
+        # Prepare cuda kernel metadata tensors
+        self.token_mapping_meta.prepare_tensors(self.token_lora_indices)
+        self.prompt_mapping_meta.prepare_tensors(self.sampler_indices)
 
-    def _apply_shrink_decode(
-        self,
-        y: torch.Tensor,
-        x: torch.Tensor,
-        w_t_all: torch.Tensor,
-        scale: float,
-    ):
-        bgmv_shrink(x, w_t_all, y, self.token_lora_indices, scale)
-
-    def _apply_expand_prefill(
-        self,
-        y: torch.Tensor,
-        x: torch.Tensor,
-        w_t_all: Tuple[torch.Tensor, ...],
-        offset_start: int,
-        add_inputs: bool,
-    ):
-        #No LoRA request, so return directly
-        if self.no_lora:
-            return
-
-        sgmv_expand(
-            x,
-            w_t_all,
-            y,
-            *self.prefill_metadata,
-            offset_start=offset_start,
-            add_inputs=add_inputs,
-        )
-
-    def _apply_expand_decode(
-        self,
-        y: torch.Tensor,
-        x: torch.Tensor,
-        w_t_all: torch.Tensor,
-        y_offset: Optional[int],
-        y_slice_size: Optional[int],
-        add_inputs: bool,
-    ):
-        bgmv_expand_slice(x, w_t_all, y, self.token_lora_indices, y_offset,
-                          y_slice_size, add_inputs)
-
-    def add_shrink(self, y: Union[Tuple[torch.Tensor, ...], torch.Tensor],
-                   x: torch.Tensor, lora_a_stacked: Tuple[torch.Tensor, ...],
-                   scale: float, **kwargs):
+    def add_shrink(self, y: torch.Tensor, x: torch.Tensor,
+                   lora_a_stacked: Tuple[torch.Tensor,
+                                         ...], scale: float, **kwargs):
         """
         Performs GEMM  for multiple slices of lora_a.
         When `is_prefill is` true, it indicates that it is currently the
@@ -199,33 +80,24 @@ class PunicaWrapperGPU(PunicaWrapperBase, V1KernelMixin):
             y[i] += (x @ lora_a_stacked[i]) * scale
         
         Args:
-            y (Union[Tuple[torch.Tensor, ...], torch.Tensor]): Output tensors
+            y (torch.Tensor): Output tensors
             x (torch.Tensor): Input tensor
             lora_a_stacked (Tuple[torch.Tensor, ...]): lora_a's weights
             scale (float): Scaling factor for the operation
         """
 
         x = x.view(-1, x.shape[-1])
-
-        if env.VLLM_USE_V1:
-            self._v1_apply_shrink(y, x, lora_a_stacked, scale)  # type: ignore
-        else:
-            if self.is_prefill:
-                # NOTE fused kernel
-                self._apply_shrink_prefill(
-                    y,  # type: ignore
+        lora_shrink(
             x,
             lora_a_stacked,
-                    scale)
-            else:
-                # TODO fuse these kernels
-                for slice_idx in range(len(lora_a_stacked)):
-                    self._apply_shrink_decode(y[slice_idx], x,
-                                              lora_a_stacked[slice_idx], scale)
+            y,
+            *self.token_mapping_meta.meta_args(x.size(0)),
+            scale,
+        )
 
     def add_expand(self,
                    y: torch.Tensor,
-                   x: Union[Tuple[torch.Tensor, ...], torch.Tensor],
+                   x: torch.Tensor,
                    lora_b_stacked: Tuple[torch.Tensor, ...],
                    lora_bias_stacked: Optional[Tuple[torch.Tensor, ...]],
                    output_slices: Tuple[int, ...],
@@ -244,7 +116,7 @@ class PunicaWrapperGPU(PunicaWrapperBase, V1KernelMixin):
             
         Args:
             y (torch.Tensor): Output tensor.
-            x (Union[Tuple[torch.Tensor, ...], torch.Tensor]): Input tensors
+            x (torch.Tensor): Input tensors
             lora_b_stacked (Tuple[torch.Tensor, ...]): lora_b's weight
             lora_bias_stacked (Optional[Tuple[torch.Tensor, ...]]): 
                 bias's weight
@@ -259,37 +131,19 @@ class PunicaWrapperGPU(PunicaWrapperBase, V1KernelMixin):
             self._apply_bias(token_lora_indices, y, output_slices,
                              lora_bias_stacked)
 
-        if env.VLLM_USE_V1:
-            # TODO (varun): Profile with add_inputs = False. i.e. move the
-            # addition out of the kernel
-            self._v1_apply_expand(
-                y,
-                x,  # type: ignore
-                lora_b_stacked,
-                offset_start,
-                add_inputs=True)
-        else:
+        assert x.ndim == 3
+        assert x.size(0) == len(output_slices)
+        num_tokens = x.size(1)  # first dimension is the num slices
 
-            if self.is_prefill:
-                # NOTE fused kernel
-                self._apply_expand_prefill(
-                    y,
-                    x,  # type: ignore
+        lora_expand(
+            x,
             lora_b_stacked,
-                    offset_start,
-                    add_inputs=True)
-            else:
-                # TODO fuse these kernels
-                for slice_idx in range(len(lora_b_stacked)):
-                    self._apply_expand_decode(
             y,
-                        x[slice_idx],
-                        lora_b_stacked[slice_idx],
-                        offset_start,
-                        output_slices[slice_idx],
-                        add_inputs=add_inputs,
+            *self.token_mapping_meta.meta_args(num_tokens),
+            offset_start=offset_start,
+            add_inputs=True,
         )
-                    offset_start += output_slices[slice_idx]
+
         y = y.view_as(y_org)
 
     def add_lora_embedding(self,
@@ -311,24 +165,14 @@ class PunicaWrapperGPU(PunicaWrapperBase, V1KernelMixin):
             add_inputs (bool): Default to True.
         """
 
-        if env.VLLM_USE_V1:
-            self._v1_apply_expand(y,
-                                  x.unsqueeze(dim=0), (lora_b_stacked, ),
-                                  offset_start=0,
-                                  add_inputs=add_inputs)
-        else:
-            if self.is_prefill:
-                sgmv_expand(
+        lora_expand(
             x.unsqueeze(dim=0),
             (lora_b_stacked, ),
             y,
-                    *self.prefill_metadata,
+            *self.token_mapping_meta.meta_args(x.size(0)),
             offset_start=0,
             add_inputs=add_inputs,
         )
-            else:
-                bgmv_expand(x, lora_b_stacked, y, self.token_lora_indices,
-                            add_inputs)
 
     def add_lora_linear(self,
                         y: torch.Tensor,
@@ -339,7 +183,7 @@ class PunicaWrapperGPU(PunicaWrapperBase, V1KernelMixin):
                         scale: float,
                         output_slices: Tuple[int, ...],
                         *,
-                        buffer: Optional[Tuple[torch.Tensor, ...]] = None,
+                        buffer: Optional[torch.Tensor] = None,
                         **kwargs) -> None:
         """
         Applicable to linear-related lora. 
@@ -361,7 +205,7 @@ class PunicaWrapperGPU(PunicaWrapperBase, V1KernelMixin):
             lora_bias_stacked (Optional[Tuple[torch.Tensor, ...]]): lora's bias.
             scale (float): Scaling factor.
             output_slices (Tuple[int, ...]): Every slice's size.
-            buffer (Optional[Tuple[torch.Tensor, ...]]): Defaults to None.
+            buffer (Optional[torch.Tensor]): Defaults to None.
         """
 
         assert len(lora_a_stacked) == len(lora_b_stacked) == len(output_slices)
@@ -431,21 +275,11 @@ class PunicaWrapperGPU(PunicaWrapperBase, V1KernelMixin):
                                  dtype=torch.float32,
                                  device=x.device)
 
-        if env.VLLM_USE_V1:
-            v1_shrink(x, [lora_a_stacked], buffer.unsqueeze(dim=0),
-                      *self.prompt_mapping_v1_meta.meta_args(x.size(0)), scale)
+        lora_shrink(x, [lora_a_stacked], buffer.unsqueeze(dim=0),
+                    *self.prompt_mapping_meta.meta_args(x.size(0)), scale)
 
-            v1_expand(buffer.unsqueeze(dim=0), [lora_b_stacked],
+        lora_expand(buffer.unsqueeze(dim=0), [lora_b_stacked],
                     y,
-                      *self.prompt_mapping_v1_meta.meta_args(buffer.size(0)),
-                      add_inputs=True)
-        else:
-
-            # V0 LogitsProcessorWithLoRA always using bgmv.
-            bgmv_shrink(x, lora_a_stacked, buffer, self.sampler_indices, scale)
-            bgmv_expand(buffer,
-                        lora_b_stacked,
-                        y,
-                        self.sampler_indices,
+                    *self.prompt_mapping_meta.meta_args(buffer.size(0)),
                     add_inputs=True)
         y = y.view_as(y_org)

vllm/model_executor/layers/mamba/mamba_mixer2.py

@@ -245,7 +245,6 @@ class MambaMixer2(CustomOp):
         assert num_heads % self.tp_size == 0, \
             "Tensor parallel world size must divide num heads."
 
-
         assert (n_groups % self.tp_size) == 0 or n_groups == 1, \
             (
                 "If tensor parallel world size does not divide num_heads, "

vllm/model_executor/layers/quantization/gguf.py

@@ -10,7 +10,6 @@ from torch.nn.parameter import Parameter, UninitializedParameter
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.fused_moe.fused_moe import moe_align_block_size
 from vllm.model_executor.layers.fused_moe.layer import (FusedMoE,
                                                         FusedMoEMethodBase)
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
@@ -140,6 +139,10 @@ def _fused_moe_gguf(
     qweight_type2: int,
     act,
 ) -> torch.Tensor:
+    # lazy import to avoid triggering triton import in CPU backend
+    from vllm.model_executor.layers.fused_moe.fused_moe import (
+        moe_align_block_size)
+
     out_hidden_states = torch.empty_like(x)
     if qweight_type2 in MMQ_QUANT_TYPES and qweight_type in MMQ_QUANT_TYPES:
         num_tokens, _ = x.shape

vllm/model_executor/models/bamba.py

@@ -38,8 +38,6 @@ from .utils import (is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
 
-KVCache = Tuple[torch.Tensor, torch.Tensor]
-
 
 class BambaMLP(nn.Module):
 

vllm/model_executor/models/blip2.py

@@ -25,7 +25,7 @@ from vllm.sequence import IntermediateTensors
 
 from .blip import BlipVisionModel
 from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
-from .utils import (AutoWeightsLoader, init_vllm_registered_model,
+from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
                     maybe_prefix, merge_multimodal_embeddings)
 
 # We use this internally as placeholders since there is no image token
@@ -565,12 +565,11 @@ class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
             return None
 
         if pixel_values is not None:
-            if not isinstance(pixel_values, torch.Tensor):
+            if not isinstance(pixel_values, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of pixel values. "
                                  f"Got type: {type(pixel_values)}")
 
-            # Remove the N dimension until multiple images are supported.
-            pixel_values = pixel_values.squeeze(1)
+            pixel_values = flatten_bn(pixel_values, concat=True)
 
             return Blip2ImagePixelInputs(
                 type="pixel_values",
@@ -578,12 +577,11 @@ class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
             )
 
         if image_embeds is not None:
-            if not isinstance(image_embeds, torch.Tensor):
+            if not isinstance(image_embeds, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of image embeddings. "
                                  f"Got type: {type(image_embeds)}")
 
-            # Remove the N dimension until multiple images are supported.
-            image_embeds = image_embeds.squeeze(1)
+            image_embeds = flatten_bn(image_embeds, concat=True)
 
             return Blip2ImageEmbeddingInputs(
                 type="image_embeds",

vllm/model_executor/models/chameleon.py

@@ -39,7 +39,7 @@ from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
-from .utils import (is_pp_missing_parameter,
+from .utils import (flatten_bn, is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix, merge_multimodal_embeddings)
 
@@ -972,12 +972,11 @@ class ChameleonForConditionalGeneration(nn.Module, SupportsMultiModal,
         if pixel_values is None:
             return None
 
-        if not isinstance(pixel_values, torch.Tensor):
+        if not isinstance(pixel_values, (torch.Tensor, list)):
             raise ValueError("Incorrect type of pixel values. "
                              f"Got type: {type(pixel_values)}")
 
-        # Remove the N dimension until multiple images are supported.
-        pixel_values = pixel_values.squeeze(1)
+        pixel_values = flatten_bn(pixel_values, concat=True)
 
         return ChameleonImagePixelInputs(
             type="pixel_values",

vllm/model_executor/models/deepseek_vl2.py

@@ -478,7 +478,7 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
                     flatten_bn(images_spatial_crop, concat=True)))
 
         if image_embeds is not None:
-            if not isinstance(image_embeds, torch.Tensor):
+            if not isinstance(image_embeds, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of image embeddings. "
                                  f"Got type: {type(image_embeds)}")
 

vllm/model_executor/models/glm4v.py

@@ -578,7 +578,7 @@ class GLM4VForCausalLM(ChatGLMBaseModel, SupportsLoRA, SupportsPP,
         pixel_values = kwargs.pop("pixel_values", None)
 
         if pixel_values is not None:
-            if not isinstance(pixel_values, torch.Tensor):
+            if not isinstance(pixel_values, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of pixel values. "
                                  f"Got type: {type(pixel_values)}")
 

vllm/model_executor/models/internvl.py

@@ -838,7 +838,7 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP):
             return None
 
         if image_embeds is not None:
-            if not isinstance(image_embeds, torch.Tensor):
+            if not isinstance(image_embeds, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of image embeddings. "
                                  f"Got type: {type(image_embeds)}")
 
@@ -856,7 +856,9 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP):
                 raise ValueError("Incorrect type of pixel values. "
                                  f"Got type: {type(pixel_values_flat)}")
 
-            assert isinstance(image_num_patches, (torch.Tensor, list))
+            if not isinstance(image_num_patches, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image_num_patches. "
+                                 f"Got type: {type(pixel_values_flat)}")
 
             return InternVLImagePixelInputs(
                 type="pixel_values",

vllm/model_executor/models/jamba.py

@@ -36,8 +36,6 @@ from .utils import (is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
 
-KVCache = Tuple[torch.Tensor, torch.Tensor]
-
 
 class JambaMoE(nn.Module):
 

vllm/model_executor/models/llava_next_video.py

@@ -349,21 +349,18 @@ class LlavaNextVideoForConditionalGeneration(nn.Module, SupportsMultiModal,
                 List[b, Tensor(nb_frames, nb_channels, height, width)]
         }
         """
-        pixel_values = kwargs.pop("pixel_values_videos", None)
+        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
 
-        if pixel_values is None:
+        if pixel_values_videos is None:
             return None
 
-        if not (is_list_of(pixel_values,
-                           (torch.Tensor))  # different shape videos 
-                or isinstance(pixel_values,
-                              torch.Tensor)):  # same shape videos
-            raise ValueError("Incorrect type of pixel values. "
-                             f"Got type: {type(pixel_values)}")
+        if not isinstance(pixel_values_videos, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of pixel_values_videos. "
+                             f"Got type: {type(pixel_values_videos)}")
 
         return LlavaNextVideoPixelInputs(
             type="pixel_values_videos",
-            data=pixel_values,
+            data=pixel_values_videos,
         )
 
     def _select_image_features(self, image_features: torch.Tensor, *,

vllm/model_executor/models/llava_onevision.py

@@ -574,10 +574,7 @@ class LlavaOnevisionForConditionalGeneration(nn.Module, SupportsMultiModal,
         if pixel_values_videos is None:
             return None
 
-        if not (is_list_of(pixel_values_videos,
-                           torch.Tensor)  # different shape videos 
-                or isinstance(pixel_values_videos,
-                              torch.Tensor)):  # same shape videos
+        if not isinstance(pixel_values_videos, (torch.Tensor, list)):
             raise ValueError("Incorrect type of pixel_values_videos. "
                              f"Got type: {type(pixel_values_videos)}")
 

vllm/model_executor/models/olmo2.py

@@ -42,7 +42,7 @@ from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.rotary_embedding import get_rope
-from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
+from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
@@ -283,17 +283,19 @@ class Olmo2Model(nn.Module):
         input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
     ) -> Union[torch.Tensor, IntermediateTensors]:
         """
         :param input_ids: A tensor of shape `(batch_size, seq_len)`.
         """
         if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
             # Get embeddings of input.
             # shape: (batch_size, seq_len, d_model)
-            inputs_embeds = self.embed_tokens(input_ids)
+            else:
+                hidden_states = self.embed_tokens(input_ids)
 
-            # embed positions
-            hidden_states = inputs_embeds
         else:
             assert intermediate_tensors is not None
             hidden_states = intermediate_tensors["hidden_states"]
@@ -337,7 +339,7 @@ class Olmo2ForCausalLM(nn.Module, SupportsPP):
                 prefix=maybe_prefix(prefix, "lm_head"),
             )
         self.logits_processor = LogitsProcessor(config.vocab_size)
-        self.sampler = Sampler()
+        self.sampler = get_sampler()
         self.make_empty_intermediate_tensors = (
             self.model.make_empty_intermediate_tensors)
 
@@ -346,11 +348,13 @@ class Olmo2ForCausalLM(nn.Module, SupportsPP):
         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=input_ids,
             positions=positions,
             intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
         )
         return hidden_states
 

vllm/model_executor/models/paligemma.py

@@ -23,7 +23,7 @@ from vllm.sequence import IntermediateTensors
 
 from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
 from .siglip import SiglipVisionModel
-from .utils import (AutoWeightsLoader, init_vllm_registered_model,
+from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
                     maybe_prefix, merge_multimodal_embeddings)
 from .vision import get_vision_encoder_info
 
@@ -270,12 +270,11 @@ class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal,
             return None
 
         if pixel_values is not None:
-            if not isinstance(pixel_values, torch.Tensor):
+            if not isinstance(pixel_values, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of pixel values. "
                                  f"Got type: {type(pixel_values)}")
 
-            # Remove the N dimension until multiple images are supported.
-            pixel_values = pixel_values.squeeze(1)
+            pixel_values = flatten_bn(pixel_values, concat=True)
 
             return PaliGemmaImagePixelInputs(
                 type="pixel_values",
@@ -287,8 +286,7 @@ class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal,
                 raise ValueError("Incorrect type of image embeddings. "
                                  f"Got type: {type(image_embeds)}")
 
-            # Remove the N dimension until multiple images are supported.
-            image_embeds = image_embeds.squeeze(1)
+            image_embeds = flatten_bn(image_embeds, concat=True)
 
             return PaliGemmaImageEmbeddingInputs(
                 type="image_embeds",

vllm/model_executor/models/qwen_vl.py

@@ -711,7 +711,7 @@ class QwenVLForConditionalGeneration(QWenBaseModel, SupportsPP, SupportsLoRA,
         image_embeds = kwargs.pop("image_embeds", None)
 
         if pixel_values is not None:
-            if not isinstance(pixel_values, torch.Tensor):
+            if not isinstance(pixel_values, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of pixel values. "
                                  f"Got type: {type(pixel_values)}")
 
@@ -722,13 +722,13 @@ class QwenVLForConditionalGeneration(QWenBaseModel, SupportsPP, SupportsLoRA,
             )
 
         if image_embeds is not None:
-            if not isinstance(image_embeds, torch.Tensor):
+            if not isinstance(image_embeds, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of image embeddings. "
                                  f"Got type: {type(image_embeds)}")
 
             return QwenImageEmbeddingInputs(
                 type="image_embeds",
-                data=flatten_bn(image_embeds),
+                data=flatten_bn(image_embeds, concat=True),
             )
 
         return None

vllm/model_executor/models/registry.py

@@ -105,6 +105,7 @@ _TEXT_GENERATION_MODELS = {
     "SolarForCausalLM": ("solar", "SolarForCausalLM"),
     "TeleChat2ForCausalLM": ("telechat2", "TeleChat2ForCausalLM"),
     "XverseForCausalLM": ("llama", "LlamaForCausalLM"),
+    "Zamba2ForCausalLM": ("zamba2", "Zamba2ForCausalLM"),
     # [Encoder-decoder]
     "BartModel": ("bart", "BartForConditionalGeneration"),
     "BartForConditionalGeneration": ("bart", "BartForConditionalGeneration"),

vllm/v1/worker/lora_model_runner_mixin.py

@@ -62,9 +62,10 @@ class LoRAModelRunnerMixin:
         if not self.lora_manager:
             raise RuntimeError("LoRA is not enabled.")
 
-        # Set is_prefill to True, so we always use the SGMV kernels.
-        # For cuda platforms, we have specialized triton kernels, and
-        # the cuda path ignores `is_prefill`.
+        # Set is_prefill to True, so we always use the SGMV kernels on
+        # non-cuda platforms.
+        # On cuda platforms we use the same kernels for prefill and
+        # decode and this flag is generally ignored.
         lora_mapping = LoRAMapping(token_lora_mapping,
                                    prompt_lora_mapping,
                                    is_prefill=True)