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[V1] Remove V0 code paths for Hybrid models (#25400)

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Signed-off-by: Thomas Parnell <tpa@zurich.ibm.com>
This commit is contained in:
Thomas Parnell
2025-09-23 17:26:13 +02:00
committed by GitHub
parent 2c58742dff
commit a903669e10
31 changed files with 352 additions and 2296 deletions
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93
vllm/model_executor/models/zamba2.py
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@@ -15,12 +15,10 @@ import torch
from torch import nn
from transformers import Zamba2Config
from vllm import envs
from vllm.attention.layer import Attention
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, ModelConfig, VllmConfig
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.forward_context import get_forward_context
from vllm.model_executor.layers.activation import GeluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
@@ -29,8 +27,6 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
ReplicatedLinear,
RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mamba.mamba2_metadata import (
Mamba2Metadata, prepare_mamba2_metadata)
from vllm.model_executor.layers.mamba.mamba_mixer2 import MambaMixer2
from vllm.model_executor.layers.mamba.mamba_utils import (
MambaStateDtypeCalculator, MambaStateShapeCalculator)
@@ -39,8 +35,6 @@ from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import (
DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.mamba_cache import (MambaCacheManager,
MambaCacheParams)
from vllm.sequence import IntermediateTensors
from .interfaces import HasInnerState, IsHybrid
@@ -515,8 +509,6 @@ class Zamba2MambaDecoderLayer(nn.Module):
def forward(
self,
hidden_states: torch.Tensor,
mamba_cache_params: MambaCacheParams,
mamba2_metadata: Mamba2Metadata,
transformer_hidden_states: Optional[torch.Tensor] = None,
positions: Optional[torch.Tensor] = None,
original_hidden_states: Optional[torch.Tensor] = None,
@@ -525,8 +517,6 @@ class Zamba2MambaDecoderLayer(nn.Module):
Args:
hidden_states: Input tensor [batch_size, seq_len, hidden_size]
mamba_cache_params: Parameters for Mamba's state caches
(one for conv, one for ssm)
transformer_hidden_states: Optional output from transformer path
Added to input if provided (used in hybrid architecture)
positions: Optional position IDs (unused in Mamba)
@@ -555,8 +545,6 @@ class Zamba2MambaDecoderLayer(nn.Module):
self.mamba(
hidden_states,
output,
mamba_cache_params=mamba_cache_params,
mamba2_metadata=mamba2_metadata,
)
# residual connection after mamba
@@ -607,8 +595,6 @@ class Zamba2HybridLayer(nn.Module):
hidden_states: torch.Tensor,
original_hidden_states: torch.Tensor,
positions: torch.Tensor,
mamba_cache_params: MambaCacheParams,
mamba2_metadata: Mamba2Metadata,
) -> torch.Tensor:
"""Forward pass through the hybrid layer.
@@ -623,8 +609,6 @@ class Zamba2HybridLayer(nn.Module):
original_hidden_states: Original input for transformer residual
connection
positions: Position IDs for positional embeddings
mamba_cache_params: Parameters for Mamba's state caches
(one for conv, one for ssm)
Returns:
Output tensor combining transformer and Mamba representations
@@ -644,8 +628,6 @@ class Zamba2HybridLayer(nn.Module):
layer_outputs = self.mamba_decoder(
hidden_states,
transformer_hidden_states=transformer_hidden_states,
mamba_cache_params=mamba_cache_params,
mamba2_metadata=mamba2_metadata,
)
return layer_outputs
@@ -752,7 +734,6 @@ class Zamba2Model(nn.Module):
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
mamba_cache_params: MambaCacheParams,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
"""Forward pass through the model.
@@ -760,8 +741,6 @@ class Zamba2Model(nn.Module):
Args:
input_ids: Input token IDs
positions: Position IDs for embeddings
mamba_cache_params: Parameters for Mamba's state caches
(one for conv, one for ssm)
inputs_embeds: Optional pre-computed input embeddings
Returns:
@@ -773,33 +752,13 @@ class Zamba2Model(nn.Module):
inputs_embeds = self.get_input_embeddings(input_ids)
hidden_states = inputs_embeds
attn_metadata = get_forward_context().attn_metadata
if not envs.VLLM_USE_V1:
mamba2_metadata = prepare_mamba2_metadata(
chunk_size=self.config.chunk_size,
attn_metadata=attn_metadata,
)
else:
# v1 get mamba2_metadata from forward_context
mamba2_metadata = None
# Process through layers
original_hidden_states = torch.clone(hidden_states)
for layer_idx, layer in enumerate(self.layers):
layer_mamba_cache_params = None
if (isinstance(layer, (Zamba2HybridLayer, Zamba2MambaDecoderLayer))
and mamba_cache_params):
layer_mamba_cache_params = mamba_cache_params.at_layer_idx(
layer_idx)
layer_outputs = layer(
hidden_states,
original_hidden_states=original_hidden_states,
positions=positions,
mamba_cache_params=layer_mamba_cache_params,
mamba2_metadata=mamba2_metadata,
)
hidden_states = layer_outputs
@@ -870,13 +829,11 @@ class Zamba2ForCausalLM(nn.Module, HasInnerState, IsHybrid):
def get_mamba_state_shape_from_config(
cls,
vllm_config: "VllmConfig",
use_v1: bool = True,
) -> tuple[tuple[int, int], tuple[int, int, int]]:
"""Calculate shapes for Mamba's convolutional and state caches.
Args:
vllm_config: vLLM config
use_v1: Get shapes for V1 (or V0)
Returns:
Tuple containing:
@@ -896,7 +853,6 @@ class Zamba2ForCausalLM(nn.Module, HasInnerState, IsHybrid):
head_dim=hf_config.mamba_headdim,
state_size=hf_config.mamba_d_state,
conv_kernel=hf_config.mamba_d_conv,
use_v1=use_v1,
)
def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
@@ -945,9 +901,6 @@ class Zamba2ForCausalLM(nn.Module, HasInnerState, IsHybrid):
# Tie weights with input embeddings if using same dimensions
self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)
# Used to track and store by the Mamba cache between steps.
self.mamba_cache: Optional[MambaCacheManager] = None
# Initialize logits processing and sampling
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
config.vocab_size)
@@ -977,61 +930,15 @@ class Zamba2ForCausalLM(nn.Module, HasInnerState, IsHybrid):
Returns:
Output hidden states
"""
# Initialize Mamba cache if needed
mamba_cache_params = None
if not envs.VLLM_USE_V1:
if self.mamba_cache is None:
num_mamba_layers = self.config.num_hidden_layers
mamba_state_shape = \
self.get_mamba_state_shape_from_config(
self.vllm_config, use_v1=False)
mamba_state_dtype = \
self.get_mamba_state_dtype_from_config(
self.vllm_config)
self.mamba_cache = MambaCacheManager(self.vllm_config,
num_mamba_layers,
*mamba_state_shape,
*mamba_state_dtype)
# Get cache parameters for current run
mamba_cache_params = self.mamba_cache.current_run_tensors(**kwargs)
# Forward pass through model
hidden_states = self.model(
input_ids,
positions,
mamba_cache_params,
inputs_embeds,
)
return hidden_states
def copy_inputs_before_cuda_graphs(
self, input_buffers: dict[str, torch.Tensor],
**kwargs: Any) -> dict[str, torch.Tensor]:
"""Copy inputs before CUDA graph capture.
Args:
input_buffers: Dictionary of input tensors
**kwargs: Additional arguments passed to cache manager
Returns:
Updated input buffers
"""
return self.mamba_cache.copy_inputs_before_cuda_graphs(
input_buffers, **kwargs)
def get_seqlen_agnostic_capture_inputs(
self, batch_size: int) -> dict[str, torch.Tensor]:
"""Get inputs for sequence-length-agnostic graph capture.
Args:
batch_size: Size of batch to capture
Returns:
Dictionary of capture inputs
"""
return self.mamba_cache.get_seqlen_agnostic_capture_inputs(batch_size)
def compute_logits(
self,
hidden_states: torch.Tensor,