[Model] PP support for Mamba-like models

docs/source/models/supported_models.rst

@@ -128,7 +128,7 @@ Text Generation
     - FalconMamba
     - :code:`tiiuae/falcon-mamba-7b`, :code:`tiiuae/falcon-mamba-7b-instruct`, etc.
     - ✅︎
-    -  
+    - ✅︎
   * - :code:`GemmaForCausalLM`
     - Gemma
     - :code:`google/gemma-2b`, :code:`google/gemma-7b`, etc.
@@ -193,7 +193,7 @@ Text Generation
     - Jamba
     - :code:`ai21labs/AI21-Jamba-1.5-Large`, :code:`ai21labs/AI21-Jamba-1.5-Mini`, :code:`ai21labs/Jamba-v0.1`, etc.
     - ✅︎
-    - 
+    - ✅︎
   * - :code:`LlamaForCausalLM`
     - Llama 3.1, Llama 3, Llama 2, LLaMA, Yi
     - :code:`meta-llama/Meta-Llama-3.1-405B-Instruct`, :code:`meta-llama/Meta-Llama-3.1-70B`, :code:`meta-llama/Meta-Llama-3-70B-Instruct`, :code:`meta-llama/Llama-2-70b-hf`, :code:`01-ai/Yi-34B`, etc.
@@ -203,7 +203,7 @@ Text Generation
     - Mamba
     - :code:`state-spaces/mamba-130m-hf`, :code:`state-spaces/mamba-790m-hf`, :code:`state-spaces/mamba-2.8b-hf`, etc.
     -
-    -
+    - ✅︎
   * - :code:`MiniCPMForCausalLM`
     - MiniCPM
     - :code:`openbmb/MiniCPM-2B-sft-bf16`, :code:`openbmb/MiniCPM-2B-dpo-bf16`, :code:`openbmb/MiniCPM-S-1B-sft`, etc.

tests/distributed/test_pipeline_parallel.py

@@ -156,13 +156,13 @@ def iter_params(self, model_name: str):
     # "internlm/internlm-chat-7b": PPTestSettings.fast(),
     "internlm/internlm2-chat-7b": PPTestSettings.fast(trust_remote_code=True),
     "inceptionai/jais-13b-chat": PPTestSettings.fast(),
-    # TODO: Implement PP
-    # "ai21labs/AI21-Jamba-1.5-Mini": PPTestSettings.fast(),
+    "ai21labs/Jamba-tiny-dev": PPTestSettings.fast(),
     "meta-llama/Meta-Llama-3-8B": PPTestSettings.detailed(),
     "openbmb/MiniCPM-2B-sft-bf16": PPTestSettings.fast(trust_remote_code=True),
     "openbmb/MiniCPM3-4B": PPTestSettings.fast(trust_remote_code=True),
     # Uses Llama
     # "mistralai/Mistral-7B-Instruct-v0.1": PPTestSettings.fast(),
+    "state-spaces/mamba-130m-hf": PPTestSettings.fast(),
     "mistralai/Mixtral-8x7B-Instruct-v0.1": PPTestSettings.fast(tp_base=4),
     "mosaicml/mpt-7b": PPTestSettings.fast(),
     "nvidia/Minitron-8B-Base": PPTestSettings.fast(),
@@ -180,7 +180,7 @@ def iter_params(self, model_name: str):
     "Qwen/Qwen1.5-MoE-A2.7B-Chat": PPTestSettings.fast(),
     "stabilityai/stablelm-3b-4e1t": PPTestSettings.fast(),
     "bigcode/starcoder2-3b": PPTestSettings.fast(),
-    "upstage/solar-pro-preview-instruct": PPTestSettings.fast(tp_base=2),
+    "upstage/solar-pro-preview-instruct": PPTestSettings.fast(tp_base=2)
     # FIXME: Cannot load tokenizer in latest transformers version.
     # Need to use tokenizer from `meta-llama/Llama-2-7b-chat-hf`
     # "xverse/XVERSE-7B-Chat": PPTestSettings.fast(trust_remote_code=True),
@@ -234,6 +234,8 @@ def iter_params(self, model_name: str):
     "OpenGVLab/InternVL2-1B",
     "microsoft/Phi-3-vision-128k-instruct",
     "fixie-ai/ultravox-v0_3",
+    # [LANGUAGE GENERATION - HYBRID ARCH]
+    "ai21labs/Jamba-tiny-dev",
 ]
 
 

vllm/config.py

@@ -27,8 +27,8 @@
     ConfigFormat, get_config, get_hf_image_processor_config,
     get_hf_text_config, get_pooling_config,
     get_sentence_transformer_tokenizer_config, is_encoder_decoder, uses_mrope)
-from vllm.utils import (GiB_bytes, cuda_device_count_stateless, get_cpu_memory,
-                        print_warning_once, random_uuid,
+from vllm.utils import (GiB_bytes, LayerBlockType, cuda_device_count_stateless,
+                        get_cpu_memory, print_warning_once, random_uuid,
                         resolve_obj_by_qualname)
 
 if TYPE_CHECKING:
@@ -680,26 +680,32 @@ def get_num_attention_heads(self,
         num_heads = getattr(self.hf_text_config, "num_attention_heads", 0)
         return num_heads // parallel_config.tensor_parallel_size
 
-    def get_num_layers(self, parallel_config: "ParallelConfig") -> int:
+    def get_layers_start_end_indices(
+            self, parallel_config: "ParallelConfig") -> Tuple[int, int]:
         from vllm.distributed.utils import get_pp_indices
         total_num_hidden_layers = getattr(self.hf_text_config,
                                           "num_hidden_layers", 0)
         pp_rank = parallel_config.rank // parallel_config.tensor_parallel_size
         pp_size = parallel_config.pipeline_parallel_size
         start, end = get_pp_indices(total_num_hidden_layers, pp_rank, pp_size)
-        return end - start
+        return start, end
 
-    def get_num_attention_layers(self,
-                                 parallel_config: "ParallelConfig") -> int:
-        if self.is_attention_free:
-            return 0
+    def get_num_layers(self, parallel_config: "ParallelConfig") -> int:
+        start, end = self.get_layers_start_end_indices(parallel_config)
+        return end - start
 
-        num_layers = self.get_num_layers(parallel_config)
+    def get_num_layers_by_block_type(
+        self,
+        parallel_config: "ParallelConfig",
+        block_type: LayerBlockType = LayerBlockType.attention,
+    ) -> int:
+        start, end = self.get_layers_start_end_indices(parallel_config)
 
         # Transformers supports layers_block_type @property
-        layers = getattr(self.hf_config, "layers_block_type",
-                         ["attention"] * num_layers)
-        return len([t for t in layers if t == "attention"])
+        layers_block_type_value = getattr(self.hf_config, "layers_block_type",
+                                          [block_type.value] * (end - start))
+        return sum(t == block_type.value
+                   for t in layers_block_type_value[start:end])
 
     def get_multimodal_config(self) -> "MultiModalConfig":
         """

vllm/model_executor/models/jamba.py

@@ -9,6 +9,7 @@
 from vllm.attention.layer import Attention
 from vllm.config import _BATCH_SIZES_TO_CAPTURE, CacheConfig, VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.distributed.parallel_state import get_pp_group
 from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (QKVParallelLinear,
@@ -25,9 +26,12 @@
                                                     MambaCacheParams)
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
+from vllm.utils import LayerBlockType
 
-from .interfaces import HasInnerState, SupportsLoRA
-from .utils import maybe_prefix
+from .interfaces import HasInnerState, SupportsLoRA, SupportsPP
+from .utils import (is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
 
@@ -281,16 +285,24 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
             org_num_embeddings=config.vocab_size,
         )
 
-        decoder_layers = []
-        for i in range(config.num_hidden_layers):
-            layer_class = ALL_DECODER_LAYER_TYPES[config.layers_block_type[i]]
-            decoder_layers.append(
-                layer_class(config,
-                            layer_idx=i,
-                            cache_config=cache_config,
-                            quant_config=quant_config,
-                            prefix=f"{prefix}.layers.{i}"))
-        self.layers = nn.ModuleList(decoder_layers)
+        def get_layer(prefix: str):
+            layer_idx = int(prefix.rsplit(".", 1)[1])
+            layer_class = ALL_DECODER_LAYER_TYPES[
+                config.layers_block_type[layer_idx]]
+            return layer_class(
+                config,
+                layer_idx,
+                cache_config,
+                quant_config=quant_config,
+                prefix=prefix,
+            )
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers, get_layer, prefix=f"{prefix}.layers")
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
         self.final_layernorm = RMSNorm(config.hidden_size,
                                        eps=config.rms_norm_eps)
 
@@ -304,26 +316,34 @@ def forward(
         kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
         mamba_cache_params: MambaCacheParams,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
         inputs_embeds: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
-        if inputs_embeds is not None:
-            hidden_states = inputs_embeds
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
         else:
-            hidden_states = self.get_input_embeddings(input_ids)
-        residual = None
-        for i in range(len(self.layers)):
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        kv_cache_index = 0
+        mamba_cache_index = 0
+        for i in range(self.start_layer, self.end_layer):
             layer = self.layers[i]
             kv_cache = None
             layer_mamba_cache_params = None
             if isinstance(layer, JambaAttentionDecoderLayer):
-                kv_cache = kv_caches[(i - self.config.attn_layer_offset) //
-                                     self.config.attn_layer_period]
+                kv_cache = kv_caches[kv_cache_index]
+                kv_cache_index += 1
             if isinstance(layer, JambaMambaDecoderLayer):
-                current_state_layer = i - (1 +
-                                           (i - self.config.attn_layer_offset)
-                                           // self.config.attn_layer_period)
+                current_state_layer = mamba_cache_index
                 layer_mamba_cache_params = mamba_cache_params.at_layer_idx(
                     current_state_layer)
+                mamba_cache_index += 1
 
             hidden_states, residual = layer(
                 positions=positions,
@@ -332,11 +352,16 @@ def forward(
                 attn_metadata=attn_metadata,
                 residual=residual,
                 mamba_cache_params=layer_mamba_cache_params)
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
         hidden_states, _ = self.final_layernorm(hidden_states, residual)
         return hidden_states
 
 
-class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA):
+class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -368,6 +393,8 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
 
         super().__init__()
         self.config = config
+        self.vllm_config = vllm_config
+        self.model_config = vllm_config.model_config
         self.scheduler_config = scheduler_config
         self.model = JambaModel(vllm_config=vllm_config,
                                 prefix=maybe_prefix(prefix, "model"))
@@ -390,6 +417,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
                                                 config.vocab_size)
         self.sampler = get_sampler()
 
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
     def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.model.get_input_embeddings(input_ids)
 
@@ -406,10 +436,8 @@ def forward(self,
                 self.scheduler_config.max_num_seqs) if self.scheduler_config
                               else max(_BATCH_SIZES_TO_CAPTURE) + 2)
 
-            layers_type = self.config.layers_block_type
-            num_mamba_layers = sum(
-                [layer_type == "mamba" for layer_type in layers_type])
-
+            num_mamba_layers = self.model_config.get_num_layers_by_block_type(
+                self.vllm_config.parallel_config, LayerBlockType.mamba)
             self.mamba_cache = MambaCacheManager(
                 self.lm_head.weight.dtype, num_mamba_layers, max_batch_size,
                 *self._get_mamba_cache_shape())
@@ -423,7 +451,7 @@ def forward(self,
                                               state_indices_tensor)
         hidden_states = self.model(input_ids, positions, kv_caches,
                                    attn_metadata, mamba_cache_params,
-                                   inputs_embeds)
+                                   intermediate_tensors, inputs_embeds)
         return hidden_states
 
     def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
@@ -504,8 +532,12 @@ def load_weights(self, weights: Iterable[Tuple[str,
                     continue
                 name = name.replace(weight_name, param_name)
                 # Skip loading extra bias for GPTQ models.
+
                 if name.endswith(".bias") and name not in params_dict:
                     continue
+                # Skip layers on other devices.
+                if is_pp_missing_parameter(name, self):
+                    continue
                 param = params_dict[name]
                 weight_loader = param.weight_loader
                 weight_loader(param, loaded_weight, shard_id)
@@ -520,6 +552,8 @@ def load_weights(self, weights: Iterable[Tuple[str,
                     if weight_name not in name:
                         continue
 
+                    if is_pp_missing_parameter(name, self):
+                        continue
                     name = name.replace(weight_name, param_name)
                     param = params_dict[name]
                     weight_loader = param.weight_loader
@@ -533,6 +567,8 @@ def load_weights(self, weights: Iterable[Tuple[str,
                     # Skip loading extra bias for GPTQ models.
                     if name.endswith(".bias") and name not in params_dict:
                         continue
+                    if is_pp_missing_parameter(name, self):
+                        continue
 
                     param = params_dict[name]
                     weight_loader = getattr(param, "weight_loader",