[Model] Support Mamba (#6484)

vllm/worker/cpu_model_runner.py

@@ -418,13 +418,12 @@ class CPUModelRunner(ModelRunnerBase[ModelInputForCPU]):
         self.sliding_window = model_config.get_sliding_window()
         self.block_size = cache_config.block_size
         self.attn_backend = get_attn_backend(
-            self.model_config.get_num_attention_heads(self.parallel_config),
             self.model_config.get_head_size(),
-            self.model_config.get_num_kv_heads(self.parallel_config),
             self.model_config.get_sliding_window(),
             self.model_config.dtype,
             self.kv_cache_dtype,
             self.block_size,
+            self.model_config.is_attention_free,
         )
 
         # Multi-modal data support

vllm/worker/cpu_worker.py

@@ -56,13 +56,12 @@ class CPUCacheEngine:
 
         # Get attention backend.
         self.attn_backend = get_attn_backend(
-            self.model_config.get_num_attention_heads(self.parallel_config),
             self.model_config.get_head_size(),
-            self.model_config.get_num_kv_heads(self.parallel_config),
             self.model_config.get_sliding_window(),
             self.model_config.dtype,
             cache_config.cache_dtype,
             self.block_size,
+            self.model_config.is_attention_free,
         )
 
         # Initialize the cache.

vllm/worker/enc_dec_model_runner.py

@@ -196,7 +196,7 @@ class EncoderDecoderModelRunner(GPUModelRunnerBase[EncoderDecoderModelInput]):
         seqlen_agnostic_kwargs = {
             "finished_requests_ids": model_input.finished_requests_ids,
             "request_ids_to_seq_ids": model_input.request_ids_to_seq_ids,
-        } if self.has_seqlen_agnostic else {}
+        } if self.has_inner_state else {}
 
         multi_modal_kwargs = model_input.multi_modal_kwargs or {}
         with set_forward_context(model_input.attn_metadata):

vllm/worker/model_runner.py

@@ -17,7 +17,6 @@ import torch.nn as nn
 import vllm.envs as envs
 from vllm.attention import AttentionMetadata, get_attn_backend
 from vllm.attention.backends.abstract import AttentionState
-from vllm.attention.backends.utils import CommonAttentionState
 from vllm.compilation.compile_context import set_compile_context
 from vllm.compilation.levels import CompilationLevel
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
@@ -991,8 +990,7 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
         self.graph_memory_pool: Optional[Tuple[
             int, int]] = None  # Set during graph capture.
 
-        self.has_seqlen_agnostic = model_config.contains_seqlen_agnostic_layers(
-            parallel_config)
+        self.has_inner_state = model_config.has_inner_state
 
         # When using CUDA graph, the input block tables must be padded to
         # max_seq_len_to_capture. However, creating the block table in
@@ -1003,22 +1001,16 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
         self.graph_block_tables = np.zeros(
             (self.max_batchsize_to_capture, self.get_max_block_per_batch()),
             dtype=np.int32)
-        num_attn_heads = self.model_config.get_num_attention_heads(
-            self.parallel_config)
         self.attn_backend = get_attn_backend(
-            num_attn_heads,
             self.model_config.get_head_size(),
-            self.model_config.get_num_kv_heads(self.parallel_config),
             self.model_config.get_sliding_window(),
             self.model_config.dtype,
             self.kv_cache_dtype,
             self.block_size,
-        ) if num_attn_heads else None
-        if self.attn_backend:
+            self.model_config.is_attention_free,
+        )
         self.attn_state = self.attn_backend.get_state_cls()(
             weakref.proxy(self))
-        else:
-            self.attn_state = CommonAttentionState(weakref.proxy(self))
 
         # Multi-modal data support
         self.input_registry = input_registry
@@ -1498,7 +1490,7 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
                             "previous_hidden_states"] = previous_hidden_states[:
                                                                                batch_size]
 
-                    if self.has_seqlen_agnostic:
+                    if self.has_inner_state:
                         # Only used by Mamba-based models CUDA graph atm (Jamba)
                         capture_inputs.update({
                             "seqlen_agnostic_capture_inputs":
@@ -1647,7 +1639,7 @@ class ModelRunner(GPUModelRunnerBase[ModelInputForGPUWithSamplingMetadata]):
         seqlen_agnostic_kwargs = {
             "finished_requests_ids": model_input.finished_requests_ids,
             "request_ids_to_seq_ids": model_input.request_ids_to_seq_ids,
-        } if self.has_seqlen_agnostic else {}
+        } if self.has_inner_state else {}
         if (self.observability_config is not None
                 and self.observability_config.collect_model_forward_time):
             model_forward_start = torch.cuda.Event(enable_timing=True)
@@ -1852,10 +1844,14 @@ class CUDAGraphRunner:
         # Copy the input tensors to the input buffers.
         self.input_buffers["input_ids"].copy_(input_ids, non_blocking=True)
         self.input_buffers["positions"].copy_(positions, non_blocking=True)
-        self.input_buffers["slot_mapping"].copy_(attn_metadata.slot_mapping,
-                                                 non_blocking=True)
+
+        if self.backend_name != "placeholder-attn":
+            self.input_buffers["slot_mapping"].copy_(
+                attn_metadata.slot_mapping, non_blocking=True)
+
         self.attn_state.prepare_graph_input_buffers(
             self.input_buffers, attn_metadata, self._is_encoder_decoder_model)
+
         if "seqlen_agnostic_capture_inputs" in self.input_buffers:
             self.model.copy_inputs_before_cuda_graphs(self.input_buffers,
                                                       **kwargs)

vllm/worker/openvino_model_runner.py

@@ -74,13 +74,12 @@ class OpenVINOModelRunner:
         self.block_size = cache_config.block_size
 
         self.attn_backend = get_attn_backend(
-            self.model_config.get_num_attention_heads(self.parallel_config),
             self.model_config.get_head_size(),
-            self.model_config.get_num_kv_heads(self.parallel_config),
             self.model_config.get_sliding_window(),
             self.model_config.dtype,
             self.kv_cache_dtype,
             self.block_size,
+            self.model_config.is_attention_free,
         )
 
         # Multi-modal data support

vllm/worker/openvino_worker.py

@@ -70,13 +70,12 @@ class OpenVINOCacheEngine:
 
         # Get attention backend.
         self.attn_backend = get_attn_backend(
-            self.model_config.get_num_attention_heads(self.parallel_config),
             self.head_size,
-            self.model_config.get_num_kv_heads(self.parallel_config),
             self.model_config.get_sliding_window(),
             self.model_config.dtype,
             self.cache_config.cache_dtype,
             self.block_size,
+            self.model_config.is_attention_free,
         )
 
         # Initialize the cache.

vllm/worker/tpu_model_runner.py

@@ -113,13 +113,12 @@ class TPUModelRunner(ModelRunnerBase[ModelInputForTPU]):
             (self.scheduler_config.max_num_seqs, self.max_num_blocks_per_seq),
             dtype=np.int32)
         self.attn_backend = get_attn_backend(
-            self.model_config.get_num_attention_heads(self.parallel_config),
             self.model_config.get_head_size(),
-            self.model_config.get_num_kv_heads(self.parallel_config),
             self.model_config.get_sliding_window(),
             self.model_config.dtype,
             self.cache_config.cache_dtype,
             self.block_size,
+            self.model_config.is_attention_free,
             False,
         )
         self.cached_step_outputs: List[torch.Tensor] = []

vllm/worker/worker.py

@@ -236,6 +236,10 @@ class Worker(LocalOrDistributedWorkerBase):
             "not properly cleaned up before initializing the vLLM instance.")
 
         cache_block_size = self.get_cache_block_size_bytes()
+        if cache_block_size == 0:
+            num_gpu_blocks = 0
+            num_cpu_blocks = 0
+        else:
             num_gpu_blocks = int(
                 (total_gpu_memory * self.cache_config.gpu_memory_utilization -
                  peak_memory) // cache_block_size)
@@ -257,6 +261,7 @@ class Worker(LocalOrDistributedWorkerBase):
         """
         raise_if_cache_size_invalid(num_gpu_blocks,
                                     self.cache_config.block_size,
+                                    self.cache_config.is_attention_free,
                                     self.model_config.max_model_len)
 
         self.cache_config.num_gpu_blocks = num_gpu_blocks
@@ -472,14 +477,18 @@ def _check_if_gpu_supports_dtype(torch_dtype: torch.dtype):
                 "`dtype` flag in CLI, for example: --dtype=half.")
 
 
-def raise_if_cache_size_invalid(num_gpu_blocks, block_size,
+def raise_if_cache_size_invalid(num_gpu_blocks, block_size, is_attention_free,
                                 max_model_len) -> None:
-    if num_gpu_blocks <= 0:
+    if is_attention_free and num_gpu_blocks != 0:
+        raise ValueError("No memory should be allocated for the cache blocks "
+                         f"for an attention-free model, but {num_gpu_blocks}"
+                         "blocks are allocated.")
+    if not is_attention_free and num_gpu_blocks <= 0:
         raise ValueError("No available memory for the cache blocks. "
                          "Try increasing `gpu_memory_utilization` when "
                          "initializing the engine.")
     max_seq_len = block_size * num_gpu_blocks
-    if max_model_len > max_seq_len:
+    if not is_attention_free and max_model_len > max_seq_len:
         raise ValueError(
             f"The model's max seq len ({max_model_len}) "
             "is larger than the maximum number of tokens that can be "

vllm/worker/xpu_model_runner.py

@@ -372,13 +372,12 @@ class XPUModelRunner(ModelRunnerBase[ModelInputForXPUWithSamplingMetadata]):
         self.block_size = cache_config.block_size
 
         self.attn_backend = get_attn_backend(
-            self.model_config.get_num_attention_heads(self.parallel_config),
             self.model_config.get_head_size(),
-            self.model_config.get_num_kv_heads(self.parallel_config),
             self.model_config.get_sliding_window(),
             self.model_config.dtype,
             self.kv_cache_dtype,
             self.block_size,
+            self.model_config.is_attention_free,
         )
 
         # Multi-modal data support