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[V1] [Hybrid] Support using float32 for state in Hybrid Models (Mamba2, Mamba1, Minimax) (#22928)

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Signed-off-by: Daniel Afrimi <danielafrimi8@gmail.com>
Signed-off-by: Thomas Parnell <tpa@zurich.ibm.com>
Signed-off-by: Chen Zhang <zhangch99@outlook.com>
Co-authored-by: Daniel Afrimi <danielafrimi8@gmail.com>
Co-authored-by: Burkhard Ringlein <ngl@zurich.ibm.com>
Co-authored-by: Chen Zhang <zhangch99@outlook.com>
This commit is contained in:
Thomas Parnell
2025-08-15 14:57:06 +02:00
committed by GitHub
parent 22341b996e
commit 75531a6c13
23 changed files with 467 additions and 87 deletions
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38
vllm/model_executor/models/zamba2.py
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@@ -18,7 +18,7 @@ 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, VllmConfig
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
@@ -33,7 +33,7 @@ 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 (
MambaStateShapeCalculator)
MambaStateDtypeCalculator, MambaStateShapeCalculator)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -478,6 +478,8 @@ class Zamba2MambaDecoderLayer(nn.Module):
def __init__(self,
config: Zamba2Config,
model_config: Optional[ModelConfig] = None,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "") -> None:
"""Initialize the Mamba decoder layer.
@@ -502,6 +504,8 @@ class Zamba2MambaDecoderLayer(nn.Module):
config.n_mamba_heads,
rms_norm_eps=config.rms_norm_eps,
activation="silu",
model_config=model_config,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.mixer")
@@ -578,6 +582,8 @@ class Zamba2HybridLayer(nn.Module):
shared_transformer: Zamba2AttentionDecoderLayer,
config: Zamba2Config,
block_idx: int,
model_config: Optional[ModelConfig] = None,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
@@ -596,6 +602,8 @@ class Zamba2HybridLayer(nn.Module):
bias=False,
quant_config=quant_config)
self.mamba_decoder = Zamba2MambaDecoderLayer(config,
model_config=model_config,
cache_config=cache_config,
quant_config=quant_config,
prefix=prefix)
@@ -669,6 +677,7 @@ class Zamba2Model(nn.Module):
super().__init__()
config = vllm_config.model_config.hf_config
model_config = vllm_config.model_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
@@ -718,11 +727,15 @@ class Zamba2Model(nn.Module):
Zamba2HybridLayer(block,
config,
block_idx,
quant_config,
model_config=model_config,
cache_config=cache_config,
quant_config=quant_config,
prefix=prefix))
else:
layers.append(
Zamba2MambaDecoderLayer(config,
model_config=model_config,
cache_config=cache_config,
quant_config=quant_config,
prefix=prefix))
self.layers = nn.ModuleList(layers)
@@ -848,6 +861,18 @@ class Zamba2ForCausalLM(nn.Module, HasInnerState, IsHybrid):
"1.weight": "B.weight",
})
@classmethod
def get_mamba_state_dtype_from_config(
cls,
vllm_config: "VllmConfig",
) -> tuple[torch.dtype, torch.dtype]:
return MambaStateDtypeCalculator.mamba2_state_dtype(
vllm_config.model_config.dtype,
vllm_config.cache_config.mamba_cache_dtype,
vllm_config.cache_config.mamba_ssm_cache_dtype,
)
@classmethod
def get_mamba_state_shape_from_config(
cls,
@@ -966,10 +991,13 @@ class Zamba2ForCausalLM(nn.Module, HasInnerState, IsHybrid):
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,
self.lm_head.weight.dtype,
num_mamba_layers,
*mamba_state_shape)
*mamba_state_shape,
*mamba_state_dtype)
# Get cache parameters for current run
mamba_cache_params = self.mamba_cache.current_run_tensors(**kwargs)