[Models] Add remaining model PP support (#7168)

Signed-off-by: Muralidhar Andoorveedu <muralidhar.andoorveedu@centml.ai>
Signed-off-by: Murali Andoorveedu <muralidhar.andoorveedu@centml.ai>
Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>

vllm/model_executor/models/bloom.py

@@ -17,7 +17,7 @@
 # limitations under the License.
 """Inference-only BLOOM model compatible with HuggingFace weights."""
 import math
-from typing import Iterable, List, Optional, Tuple
+from typing import Iterable, List, Optional, Tuple, Union
 
 import torch
 from torch import nn
@@ -25,15 +25,14 @@ from transformers import BloomConfig
 
 from vllm.attention import Attention, AttentionMetadata
 from vllm.config import CacheConfig
-from vllm.distributed import (get_tensor_model_parallel_rank,
+from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size)
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead, VocabParallelEmbedding)
@@ -41,6 +40,10 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
+from .interfaces import SupportsPP
+from .utils import (is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers)
+
 
 def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
     closest_power_of_2 = 2**math.floor(math.log2(total_num_heads))
@@ -222,6 +225,7 @@ class BloomModel(nn.Module):
         config: BloomConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
     ):
         super().__init__()
         self.embed_dim = config.hidden_size
@@ -235,13 +239,16 @@ class BloomModel(nn.Module):
             self.embed_dim, eps=config.layer_norm_epsilon)
 
         # Transformer blocks
-        self.h = nn.ModuleList([
-            BloomBlock(config, cache_config, quant_config)
-            for _ in range(config.num_hidden_layers)
-        ])
+        self.start_layer, self.end_layer, self.h = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: BloomBlock(config, cache_config, quant_config),
+            prefix=f"{prefix}.h")
 
         # Final Layer Norm
         self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(["hidden_states"],
+                                                    config.hidden_size))
 
     def forward(
         self,
@@ -249,22 +256,29 @@ class BloomModel(nn.Module):
         position_ids: torch.Tensor,
         kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
-    ) -> torch.Tensor:
-        hidden_states = self.word_embeddings(input_ids)
-        hidden_states = self.word_embeddings_layernorm(hidden_states)
-        for i in range(len(self.h)):
+        intermediate_tensors: Optional[IntermediateTensors],
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            hidden_states = self.word_embeddings(input_ids)
+            hidden_states = self.word_embeddings_layernorm(hidden_states)
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+        for i in range(self.start_layer, self.end_layer):
             layer = self.h[i]
             hidden_states = layer(
                 position_ids,
                 hidden_states,
-                kv_caches[i],
+                kv_caches[i - self.start_layer],
                 attn_metadata,
             )
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({"hidden_states": hidden_states})
         hidden_states = self.ln_f(hidden_states)
         return hidden_states
 
 
-class BloomForCausalLM(nn.Module):
+class BloomForCausalLM(nn.Module, SupportsPP):
 
     def __init__(
         self,
@@ -284,6 +298,8 @@ class BloomForCausalLM(nn.Module):
 
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()
+        self.make_empty_intermediate_tensors = (
+            self.transformer.make_empty_intermediate_tensors)
 
     def forward(
         self,
@@ -292,9 +308,9 @@ class BloomForCausalLM(nn.Module):
         kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
         intermediate_tensors: Optional[IntermediateTensors] = None,
-    ) -> torch.Tensor:
+    ) -> Union[torch.Tensor, IntermediateTensors]:
         hidden_states = self.transformer(input_ids, positions, kv_caches,
-                                         attn_metadata)
+                                         attn_metadata, intermediate_tensors)
         return hidden_states
 
     def compute_logits(
@@ -321,6 +337,8 @@ class BloomForCausalLM(nn.Module):
                 continue
             if not name.startswith("transformer."):
                 name = "transformer." + name
+            if is_pp_missing_parameter(name, self):
+                continue
             param = params_dict[name]
 
             if "query_key_value" in name: