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Unverified Commit 0a12cea2 authored by Andrii Skliar's avatar Andrii Skliar Committed by GitHub
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Order `config.py` in Lexicographical order (#35866) 


Signed-off-by: default avatarAndrii Skliar <askliar@nvidia.com>
Co-authored-by: default avatarAndrii Skliar <askliar@nvidia.com>
parent dd6dbd93
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Showing with 370 additions and 370 deletions
vllm/model_executor/models/config.py
View file @ 0a12cea2
......@@ -28,6 +28,36 @@ class VerifyAndUpdateConfig:
return
class DeepseekV32ForCausalLM(VerifyAndUpdateConfig):
@classmethod
def verify_and_update_config(cls, vllm_config: "VllmConfig") -> None:
"""
Updated fp8 cache to custom "fp8_ds_mla" format for DeepSeekV32
"""
hf_config = vllm_config.model_config.hf_config
# Mirror the check in vllm/model_executor/models/deepseek_v2.py
is_v32 = hasattr(hf_config, "index_topk")
assert is_v32
# For DeepSeekV3.2, a custom fp8 format is used when fp8 kv-cache is enabled.
cache_config = vllm_config.cache_config
if cache_config.cache_dtype.startswith("fp8"):
cache_config.cache_dtype = "fp8_ds_mla"
logger.info("Using custom fp8 kv-cache format for DeepSeekV3.2")
if cache_config.cache_dtype == "bfloat16":
cache_config.cache_dtype = "auto"
logger.info("Using bfloat16 kv-cache for DeepSeekV3.2")
class Ernie4_5_VLMoeForConditionalGenerationConfig(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_config(vllm_config: "VllmConfig") -> None:
# Ernie4.5-VL conditionally executes text/vision MoE branches, so
# fast_moe_cold_start can silently produce incorrect execution order.
vllm_config.compilation_config.fast_moe_cold_start = False
class Gemma3TextModelConfig(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_model_config(model_config: "ModelConfig") -> None:
......@@ -35,6 +65,29 @@ class Gemma3TextModelConfig(VerifyAndUpdateConfig):
hf_config.is_causal = not hf_config.use_bidirectional_attention
class GptOssForCausalLMConfig(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_config(vllm_config: "VllmConfig") -> None:
structured_outputs_config = vllm_config.structured_outputs_config
if structured_outputs_config.reasoning_parser == "":
structured_outputs_config.reasoning_parser = "openai_gptoss"
# Increase the max capture size from 512 to 1024 for performance.
# NOTE(woosuk): This will increase the number of CUDA graphs
# from 67 to 83.
compilation_config = vllm_config.compilation_config
# Only override when the user has not set either of
# cudagraph_capture_sizes or max_cudagraph_capture_size.
if (
compilation_config.cudagraph_capture_sizes is None
and compilation_config.max_cudagraph_capture_size is None
):
compilation_config.max_cudagraph_capture_size = 1024
logger.info(
"Overriding max cuda graph capture size to %d for performance.", 1024
)
class GteNewModelConfig(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_model_config(model_config: "ModelConfig") -> None:
......@@ -55,6 +108,166 @@ class GteNewModelConfig(VerifyAndUpdateConfig):
}
class HybridAttentionMambaModelConfig(VerifyAndUpdateConfig):
@classmethod
def verify_and_update_config(cls, vllm_config: "VllmConfig") -> None:
"""
Ensure that page size of attention layers is greater than or
equal to the mamba layers. If not, automatically set the attention
block size to ensure that it is. If the attention page size is
strictly greater than the mamba page size, we pad the mamba page size
to make them equal.
Args:
vllm_config: vLLM Config
"""
# Save the user input before it gets modified by MambaModelConfig
mamba_block_size = vllm_config.cache_config.mamba_block_size
# Enable FULL_AND_PIECEWISE by default
MambaModelConfig.verify_and_update_config(vllm_config)
attention_config = vllm_config.attention_config
cache_config = vllm_config.cache_config
model_config = vllm_config.model_config
parallel_config = vllm_config.parallel_config
if cache_config.cache_dtype == "auto":
kv_cache_dtype = model_config.dtype
else:
kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]
# get attention page size (for 1 token)
# Attention backend constraints:
# - FlashAttention (FA) requires block size to be multiple of 16
# - MLA (Multi-head Latent Attention) requires larger alignment:
# * CUTLASS_MLA backend: kernel_block_size 128 alignment
# * Other MLA backends: kernel_block_size 64 alignment
if model_config.use_mla:
use_cutlass_mla = (
attention_config.backend == AttentionBackendEnum.CUTLASS_MLA
)
kernel_block_alignment_size = 128 if use_cutlass_mla else 64
attn_page_size_1_token = MLAAttentionSpec(
block_size=1,
num_kv_heads=model_config.get_num_kv_heads(parallel_config),
head_size=model_config.get_head_size(),
dtype=kv_cache_dtype,
).page_size_bytes
else:
kernel_block_alignment_size = 16
if (
current_platform.is_device_capability_family(100)
and model_config.get_head_size() == 256
and (
attention_config.backend is None
or attention_config.backend == AttentionBackendEnum.FLASHINFER
)
):
# https://github.com/flashinfer-ai/flashinfer/issues/1993 reports that`
# head size 256 and block size 16 is not supported on blackwell.
kernel_block_alignment_size = 32
attn_page_size_1_token = FullAttentionSpec(
block_size=1,
num_kv_heads=model_config.get_num_kv_heads(parallel_config),
head_size=model_config.get_head_size(),
dtype=kv_cache_dtype,
).page_size_bytes
model_cls, _ = ModelRegistry.resolve_model_cls(
model_config.architecture,
model_config=model_config,
)
# get mamba page size
mamba_page_size = MambaSpec(
shapes=model_cls.get_mamba_state_shape_from_config(vllm_config),
dtypes=model_cls.get_mamba_state_dtype_from_config(vllm_config),
block_size=-1, # block_size doesn't matter for mamba page size
).page_size_bytes
# Model may be marked as is_hybrid
# but mamba is skipped via config,
# return directly
if mamba_page_size == 0:
return
if cache_config.mamba_cache_mode == "all":
# With prefix caching, select attention block size to
# optimize for mamba kernel performance
# Mamba2 SSD kernel uses a chunk_size, e.g. 256
# Align the block to the kernel: use lowest multiple of chunk_size
# of attention tokens that would fit mamba_page_size:
# e.g. for mamba page size = 788kB
# attn_1_token = 2kB -> fits ~394 tokens
# then round up to a multiple of 256 -> 512 tokens
# End result:
# attn_block_size = 512
# mamba_block_size = 512 (aligned to a multiple of chunk_size)
# TODO(tdoublep): this constraint can be relaxed fairly
# easily by changing the way we layout chunks in the
# mamba2 kernels.
base_chunk_size = mamba_block_size or model_config.get_mamba_chunk_size()
attn_tokens_per_mamba_state = cdiv(mamba_page_size, attn_page_size_1_token)
chunk_size = lcm(base_chunk_size, kernel_block_alignment_size)
attn_block_size = chunk_size * cdiv(attn_tokens_per_mamba_state, chunk_size)
cache_config.mamba_block_size = attn_block_size
else:
# Without prefix caching, select minimum valid attention block size
# to minimize mamba state padding
# Calculate minimum attention block size that satisfies both:
# 1. Backend alignment requirements (kernel_block_alignment_size)
# 2. Mamba page size compatibility (attn_page_size >= mamba_page_size)
attn_block_size = kernel_block_alignment_size * cdiv(
mamba_page_size, kernel_block_alignment_size * attn_page_size_1_token
)
# override attention block size if either (a) the
# user has not set it or (b) the user has set it
# too small.
if cache_config.block_size is None or cache_config.block_size < attn_block_size:
cache_config.block_size = attn_block_size
logger.info(
"Setting attention block size to %d tokens "
"to ensure that attention page size is >= mamba page size.",
attn_block_size,
)
# By default, mamba block size will be set to max_model_len.
# When enabling prefix caching and using align mamba cache
# mode, we align mamba block size to the block size as the
# basic granularity for prefix caching.
if cache_config.mamba_cache_mode == "align":
cache_config.mamba_block_size = cache_config.block_size
# compute new attention page size
attn_page_size = cache_config.block_size * attn_page_size_1_token
assert attn_page_size >= mamba_page_size
if attn_page_size == mamba_page_size:
# don't need to pad mamba page size
return
# pad mamba page size to exactly match attention
if (
cache_config.mamba_page_size_padded is None
or cache_config.mamba_page_size_padded != attn_page_size
):
cache_config.mamba_page_size_padded = attn_page_size
mamba_padding_pct = (
100 * (attn_page_size - mamba_page_size) / mamba_page_size
)
logger.info(
"Padding mamba page size by %.2f%% to ensure "
"that mamba page size and attention page size are "
"exactly equal.",
mamba_padding_pct,
)
class JambaForSequenceClassificationConfig(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_model_config(model_config: "ModelConfig") -> None:
......@@ -91,6 +304,16 @@ class JinaRobertaModelConfig(VerifyAndUpdateConfig):
}
class JinaVLForSequenceClassificationConfig(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_model_config(model_config: "ModelConfig") -> None:
config = model_config.hf_config
config.num_labels = 1
pooler_config = model_config.pooler_config
if pooler_config.logit_bias is None:
pooler_config.logit_bias = 2.65
class LlamaBidirectionalConfig(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_model_config(model_config: "ModelConfig") -> None:
......@@ -148,30 +371,119 @@ class LlamaNemotronVLConfig(VerifyAndUpdateConfig):
model_config.pooler_config.seq_pooling_type = pooling_type
class NomicBertModelConfig(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_model_config(model_config: "ModelConfig") -> None:
config = model_config.hf_config
assert config.__class__.__name__ == "NomicBertConfig"
assert config.activation_function in ["swiglu", "gelu"]
config.position_embedding_type = getattr(
config, "position_embedding_type", "rope"
)
if config.activation_function == "swiglu":
config.hidden_act = "silu"
else:
config.hidden_act = config.activation_function
assert config.mlp_fc1_bias == config.mlp_fc2_bias == config.qkv_proj_bias
config.bias = config.qkv_proj_bias
class MambaModelConfig(VerifyAndUpdateConfig):
@classmethod
def verify_and_update_config(cls, vllm_config: "VllmConfig") -> None:
"""
Enable FULL_AND_PIECEWISE cuda graph mode by default (required
to get good performance for mamba layers in V1).
assert config.rotary_emb_scale_base is None
assert not config.rotary_emb_interleaved
Args:
vllm_config: vLLM Config
"""
model_config = vllm_config.model_config
cache_config = vllm_config.cache_config
config.layer_norm_eps = config.layer_norm_epsilon
config.intermediate_size = config.n_inner
if cache_config.enable_prefix_caching:
if cache_config.mamba_cache_mode == "none":
cache_config.mamba_cache_mode = (
"all" if model_config.supports_mamba_prefix_caching else "align"
)
logger.warning(
"Mamba cache mode is set to '%s' for %s by default "
"when prefix caching is enabled",
cache_config.mamba_cache_mode,
model_config.architecture,
)
if (
cache_config.mamba_cache_mode == "all"
and not model_config.supports_mamba_prefix_caching
):
cache_config.mamba_cache_mode = "align"
logger.warning(
"Hybrid or mamba-based model detected without support "
"for prefix caching with Mamba cache 'all' mode: "
"falling back to 'align' mode."
)
if cache_config.mamba_cache_mode == "align":
assert vllm_config.scheduler_config.enable_chunked_prefill, (
"Chunked prefill is required for mamba cache mode 'align'."
)
logger.info(
"Warning: Prefix caching in Mamba cache '%s' "
"mode is currently enabled. "
"Its support for Mamba layers is experimental. "
"Please report any issues you may observe.",
cache_config.mamba_cache_mode,
)
# By default, mamba block size will be set to max_model_len (see
# below). When enabling prefix caching, we align mamba block size
# to the block size as the basic granularity for prefix caching.
if cache_config.mamba_block_size is None:
cache_config.mamba_block_size = cache_config.block_size
else:
if cache_config.mamba_cache_mode != "none":
cache_config.mamba_cache_mode = "none"
logger.warning(
"Mamba cache mode is set to 'none' when prefix caching is disabled"
)
if cache_config.mamba_block_size is None:
cache_config.mamba_block_size = model_config.max_model_len
class NemotronHForCausalLMConfig(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_config(vllm_config: "VllmConfig") -> None:
"""Update mamba_ssm_cache_dtype for NemotronH models when set to 'auto'
(or not explicitly set), to the value specified in the HF config, or to
float16 if not specified.
"""
cache_config = vllm_config.cache_config
if cache_config.mamba_ssm_cache_dtype == "auto":
hf_config = vllm_config.model_config.hf_config
mamba_ssm_cache_dtype = getattr(
hf_config, "mamba_ssm_cache_dtype", "float16"
)
logger.info(
"Updating mamba_ssm_cache_dtype to '%s' for NemotronH model",
mamba_ssm_cache_dtype,
)
cache_config.mamba_ssm_cache_dtype = mamba_ssm_cache_dtype
class NemotronHNanoVLV2Config(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_model_config(model_config: "ModelConfig") -> None:
mm_config = model_config.multimodal_config
if mm_config is not None:
video_kwargs = mm_config.media_io_kwargs.setdefault("video", {})
video_kwargs.setdefault("video_backend", "nemotron_vl")
class NomicBertModelConfig(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_model_config(model_config: "ModelConfig") -> None:
config = model_config.hf_config
assert config.__class__.__name__ == "NomicBertConfig"
assert config.activation_function in ["swiglu", "gelu"]
config.position_embedding_type = getattr(
config, "position_embedding_type", "rope"
)
if config.activation_function == "swiglu":
config.hidden_act = "silu"
else:
config.hidden_act = config.activation_function
assert config.mlp_fc1_bias == config.mlp_fc2_bias == config.qkv_proj_bias
config.bias = config.qkv_proj_bias
assert config.rotary_emb_scale_base is None
assert not config.rotary_emb_interleaved
config.layer_norm_eps = config.layer_norm_epsilon
config.intermediate_size = config.n_inner
config.hidden_size = config.n_embd
config.num_hidden_layers = config.n_layer
model_config.model_arch_config.hidden_size = config.hidden_size
......@@ -299,338 +611,6 @@ class Qwen3VLForSequenceClassificationConfig(Qwen3ForSequenceClassificationConfi
pass
class JinaVLForSequenceClassificationConfig(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_model_config(model_config: "ModelConfig") -> None:
config = model_config.hf_config
config.num_labels = 1
pooler_config = model_config.pooler_config
if pooler_config.logit_bias is None:
pooler_config.logit_bias = 2.65
class SnowflakeGteNewModelConfig(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_model_config(model_config: "ModelConfig") -> None:
config = model_config.hf_config
assert config.__class__.__name__ == "GteConfig"
assert config.hidden_act == "gelu"
config.hidden_act = "geglu"
head_dim = config.hidden_size // config.num_attention_heads
rotary_dim = getattr(config, "rotary_emb_dim", head_dim)
config.rope_parameters["partial_rotary_factor"] = rotary_dim / head_dim
config.rotary_kwargs = {
"head_size": head_dim,
"max_position": config.max_position_embeddings,
"rope_parameters": config.rope_parameters,
}
class Ernie4_5_VLMoeForConditionalGenerationConfig(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_config(vllm_config: "VllmConfig") -> None:
# Ernie4.5-VL conditionally executes text/vision MoE branches, so
# fast_moe_cold_start can silently produce incorrect execution order.
vllm_config.compilation_config.fast_moe_cold_start = False
class GptOssForCausalLMConfig(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_config(vllm_config: "VllmConfig") -> None:
structured_outputs_config = vllm_config.structured_outputs_config
if structured_outputs_config.reasoning_parser == "":
structured_outputs_config.reasoning_parser = "openai_gptoss"
# Increase the max capture size from 512 to 1024 for performance.
# NOTE(woosuk): This will increase the number of CUDA graphs
# from 67 to 83.
compilation_config = vllm_config.compilation_config
# Only override when the user has not set either of
# cudagraph_capture_sizes or max_cudagraph_capture_size.
if (
compilation_config.cudagraph_capture_sizes is None
and compilation_config.max_cudagraph_capture_size is None
):
compilation_config.max_cudagraph_capture_size = 1024
logger.info(
"Overriding max cuda graph capture size to %d for performance.", 1024
)
class MambaModelConfig(VerifyAndUpdateConfig):
@classmethod
def verify_and_update_config(cls, vllm_config: "VllmConfig") -> None:
"""
Enable FULL_AND_PIECEWISE cuda graph mode by default (required
to get good performance for mamba layers in V1).
Args:
vllm_config: vLLM Config
"""
model_config = vllm_config.model_config
cache_config = vllm_config.cache_config
if cache_config.enable_prefix_caching:
if cache_config.mamba_cache_mode == "none":
cache_config.mamba_cache_mode = (
"all" if model_config.supports_mamba_prefix_caching else "align"
)
logger.warning(
"Mamba cache mode is set to '%s' for %s by default "
"when prefix caching is enabled",
cache_config.mamba_cache_mode,
model_config.architecture,
)
if (
cache_config.mamba_cache_mode == "all"
and not model_config.supports_mamba_prefix_caching
):
cache_config.mamba_cache_mode = "align"
logger.warning(
"Hybrid or mamba-based model detected without support "
"for prefix caching with Mamba cache 'all' mode: "
"falling back to 'align' mode."
)
if cache_config.mamba_cache_mode == "align":
assert vllm_config.scheduler_config.enable_chunked_prefill, (
"Chunked prefill is required for mamba cache mode 'align'."
)
logger.info(
"Warning: Prefix caching in Mamba cache '%s' "
"mode is currently enabled. "
"Its support for Mamba layers is experimental. "
"Please report any issues you may observe.",
cache_config.mamba_cache_mode,
)
# By default, mamba block size will be set to max_model_len (see
# below). When enabling prefix caching, we align mamba block size
# to the block size as the basic granularity for prefix caching.
if cache_config.mamba_block_size is None:
cache_config.mamba_block_size = cache_config.block_size
else:
if cache_config.mamba_cache_mode != "none":
cache_config.mamba_cache_mode = "none"
logger.warning(
"Mamba cache mode is set to 'none' when prefix caching is disabled"
)
if cache_config.mamba_block_size is None:
cache_config.mamba_block_size = model_config.max_model_len
class HybridAttentionMambaModelConfig(VerifyAndUpdateConfig):
@classmethod
def verify_and_update_config(cls, vllm_config: "VllmConfig") -> None:
"""
Ensure that page size of attention layers is greater than or
equal to the mamba layers. If not, automatically set the attention
block size to ensure that it is. If the attention page size is
strictly greater than the mamba page size, we pad the mamba page size
to make them equal.
Args:
vllm_config: vLLM Config
"""
# Save the user input before it gets modified by MambaModelConfig
mamba_block_size = vllm_config.cache_config.mamba_block_size
# Enable FULL_AND_PIECEWISE by default
MambaModelConfig.verify_and_update_config(vllm_config)
attention_config = vllm_config.attention_config
cache_config = vllm_config.cache_config
model_config = vllm_config.model_config
parallel_config = vllm_config.parallel_config
if cache_config.cache_dtype == "auto":
kv_cache_dtype = model_config.dtype
else:
kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]
# get attention page size (for 1 token)
# Attention backend constraints:
# - FlashAttention (FA) requires block size to be multiple of 16
# - MLA (Multi-head Latent Attention) requires larger alignment:
# * CUTLASS_MLA backend: kernel_block_size 128 alignment
# * Other MLA backends: kernel_block_size 64 alignment
if model_config.use_mla:
use_cutlass_mla = (
attention_config.backend == AttentionBackendEnum.CUTLASS_MLA
)
kernel_block_alignment_size = 128 if use_cutlass_mla else 64
attn_page_size_1_token = MLAAttentionSpec(
block_size=1,
num_kv_heads=model_config.get_num_kv_heads(parallel_config),
head_size=model_config.get_head_size(),
dtype=kv_cache_dtype,
).page_size_bytes
else:
kernel_block_alignment_size = 16
if (
current_platform.is_device_capability_family(100)
and model_config.get_head_size() == 256
and (
attention_config.backend is None
or attention_config.backend == AttentionBackendEnum.FLASHINFER
)
):
# https://github.com/flashinfer-ai/flashinfer/issues/1993 reports that`
# head size 256 and block size 16 is not supported on blackwell.
kernel_block_alignment_size = 32
attn_page_size_1_token = FullAttentionSpec(
block_size=1,
num_kv_heads=model_config.get_num_kv_heads(parallel_config),
head_size=model_config.get_head_size(),
dtype=kv_cache_dtype,
).page_size_bytes
model_cls, _ = ModelRegistry.resolve_model_cls(
model_config.architecture,
model_config=model_config,
)
# get mamba page size
mamba_page_size = MambaSpec(
shapes=model_cls.get_mamba_state_shape_from_config(vllm_config),
dtypes=model_cls.get_mamba_state_dtype_from_config(vllm_config),
block_size=-1, # block_size doesn't matter for mamba page size
).page_size_bytes
# Model may be marked as is_hybrid
# but mamba is skipped via config,
# return directly
if mamba_page_size == 0:
return
if cache_config.mamba_cache_mode == "all":
# With prefix caching, select attention block size to
# optimize for mamba kernel performance
# Mamba2 SSD kernel uses a chunk_size, e.g. 256
# Align the block to the kernel: use lowest multiple of chunk_size
# of attention tokens that would fit mamba_page_size:
# e.g. for mamba page size = 788kB
# attn_1_token = 2kB -> fits ~394 tokens
# then round up to a multiple of 256 -> 512 tokens
# End result:
# attn_block_size = 512
# mamba_block_size = 512 (aligned to a multiple of chunk_size)
# TODO(tdoublep): this constraint can be relaxed fairly
# easily by changing the way we layout chunks in the
# mamba2 kernels.
base_chunk_size = mamba_block_size or model_config.get_mamba_chunk_size()
attn_tokens_per_mamba_state = cdiv(mamba_page_size, attn_page_size_1_token)
chunk_size = lcm(base_chunk_size, kernel_block_alignment_size)
attn_block_size = chunk_size * cdiv(attn_tokens_per_mamba_state, chunk_size)
cache_config.mamba_block_size = attn_block_size
else:
# Without prefix caching, select minimum valid attention block size
# to minimize mamba state padding
# Calculate minimum attention block size that satisfies both:
# 1. Backend alignment requirements (kernel_block_alignment_size)
# 2. Mamba page size compatibility (attn_page_size >= mamba_page_size)
attn_block_size = kernel_block_alignment_size * cdiv(
mamba_page_size, kernel_block_alignment_size * attn_page_size_1_token
)
# override attention block size if either (a) the
# user has not set it or (b) the user has set it
# too small.
if cache_config.block_size is None or cache_config.block_size < attn_block_size:
cache_config.block_size = attn_block_size
logger.info(
"Setting attention block size to %d tokens "
"to ensure that attention page size is >= mamba page size.",
attn_block_size,
)
# By default, mamba block size will be set to max_model_len.
# When enabling prefix caching and using align mamba cache
# mode, we align mamba block size to the block size as the
# basic granularity for prefix caching.
if cache_config.mamba_cache_mode == "align":
cache_config.mamba_block_size = cache_config.block_size
# compute new attention page size
attn_page_size = cache_config.block_size * attn_page_size_1_token
assert attn_page_size >= mamba_page_size
if attn_page_size == mamba_page_size:
# don't need to pad mamba page size
return
# pad mamba page size to exactly match attention
if (
cache_config.mamba_page_size_padded is None
or cache_config.mamba_page_size_padded != attn_page_size
):
cache_config.mamba_page_size_padded = attn_page_size
mamba_padding_pct = (
100 * (attn_page_size - mamba_page_size) / mamba_page_size
)
logger.info(
"Padding mamba page size by %.2f%% to ensure "
"that mamba page size and attention page size are "
"exactly equal.",
mamba_padding_pct,
)
class DeepseekV32ForCausalLM(VerifyAndUpdateConfig):
@classmethod
def verify_and_update_config(cls, vllm_config: "VllmConfig") -> None:
"""
Updated fp8 cache to custom "fp8_ds_mla" format for DeepSeekV32
"""
hf_config = vllm_config.model_config.hf_config
# Mirror the check in vllm/model_executor/models/deepseek_v2.py
is_v32 = hasattr(hf_config, "index_topk")
assert is_v32
# For DeepSeekV3.2, a custom fp8 format is used when fp8 kv-cache is enabled.
cache_config = vllm_config.cache_config
if cache_config.cache_dtype.startswith("fp8"):
cache_config.cache_dtype = "fp8_ds_mla"
logger.info("Using custom fp8 kv-cache format for DeepSeekV3.2")
if cache_config.cache_dtype == "bfloat16":
cache_config.cache_dtype = "auto"
logger.info("Using bfloat16 kv-cache for DeepSeekV3.2")
class NemotronHForCausalLMConfig(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_config(vllm_config: "VllmConfig") -> None:
"""Update mamba_ssm_cache_dtype for NemotronH models when set to 'auto'
(or not explicitly set), to the value specified in the HF config, or to
float16 if not specified.
"""
cache_config = vllm_config.cache_config
if cache_config.mamba_ssm_cache_dtype == "auto":
hf_config = vllm_config.model_config.hf_config
mamba_ssm_cache_dtype = getattr(
hf_config, "mamba_ssm_cache_dtype", "float16"
)
logger.info(
"Updating mamba_ssm_cache_dtype to '%s' for NemotronH model",
mamba_ssm_cache_dtype,
)
cache_config.mamba_ssm_cache_dtype = mamba_ssm_cache_dtype
class NemotronHNanoVLV2Config(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_model_config(model_config: "ModelConfig") -> None:
mm_config = model_config.multimodal_config
if mm_config is not None:
video_kwargs = mm_config.media_io_kwargs.setdefault("video", {})
video_kwargs.setdefault("video_backend", "nemotron_vl")
class Qwen3_5ForConditionalGenerationConfig(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_config(vllm_config: "VllmConfig") -> None:
......@@ -658,6 +638,26 @@ class Qwen3_5ForConditionalGenerationConfig(VerifyAndUpdateConfig):
)
class SnowflakeGteNewModelConfig(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_model_config(model_config: "ModelConfig") -> None:
config = model_config.hf_config
assert config.__class__.__name__ == "GteConfig"
assert config.hidden_act == "gelu"
config.hidden_act = "geglu"
head_dim = config.hidden_size // config.num_attention_heads
rotary_dim = getattr(config, "rotary_emb_dim", head_dim)
config.rope_parameters["partial_rotary_factor"] = rotary_dim / head_dim
config.rotary_kwargs = {
"head_size": head_dim,
"max_position": config.max_position_embeddings,
"rope_parameters": config.rope_parameters,
}
class VoyageQwen3BidirectionalEmbedModelConfig(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_model_config(model_config: "ModelConfig") -> None:
......@@ -666,33 +666,33 @@ class VoyageQwen3BidirectionalEmbedModelConfig(VerifyAndUpdateConfig):
MODELS_CONFIG_MAP: dict[str, type[VerifyAndUpdateConfig]] = {
"ColBERTJinaRobertaModel": JinaRobertaModelConfig,
"DeepseekV32ForCausalLM": DeepseekV32ForCausalLM,
"Ernie4_5_VLMoeForConditionalGeneration": Ernie4_5_VLMoeForConditionalGenerationConfig, # noqa: E501
"FalconMambaForCausalLM": MambaModelConfig,
"Gemma3TextModel": Gemma3TextModelConfig,
"GptOssForCausalLM": GptOssForCausalLMConfig,
"GteModel": SnowflakeGteNewModelConfig,
"GteNewModel": GteNewModelConfig,
"GteNewForSequenceClassification": GteNewModelConfig,
"Gemma3TextModel": Gemma3TextModelConfig,
"NemotronH_Nano_VL_V2": NemotronHNanoVLV2Config,
"GteNewModel": GteNewModelConfig,
"JambaForSequenceClassification": JambaForSequenceClassificationConfig,
"JinaVLForRanking": JinaVLForSequenceClassificationConfig,
"LlamaBidirectionalForSequenceClassification": LlamaBidirectionalConfig,
"LlamaBidirectionalModel": LlamaBidirectionalConfig,
"LlamaNemotronVLModel": LlamaNemotronVLConfig,
"LlamaNemotronVLForSequenceClassification": LlamaNemotronVLConfig,
"LlamaNemotronVLModel": LlamaNemotronVLConfig,
"Mamba2ForCausalLM": MambaModelConfig,
"MambaForCausalLM": MambaModelConfig,
"NemotronHForCausalLM": NemotronHForCausalLMConfig,
"NemotronHPuzzleForCausalLM": NemotronHForCausalLMConfig,
"NemotronH_Nano_VL_V2": NemotronHNanoVLV2Config,
"NomicBertModel": NomicBertModelConfig,
"Qwen2ForProcessRewardModel": Qwen2ForProcessRewardModelConfig,
"Qwen2ForRewardModel": Qwen2ForRewardModelConfig,
"Qwen3ForSequenceClassification": Qwen3ForSequenceClassificationConfig,
"Qwen3VLForSequenceClassification": Qwen3VLForSequenceClassificationConfig,
"Ernie4_5_VLMoeForConditionalGeneration": Ernie4_5_VLMoeForConditionalGenerationConfig, # noqa: E501
"XLMRobertaModel": JinaRobertaModelConfig,
"ColBERTJinaRobertaModel": JinaRobertaModelConfig,
"JinaVLForRanking": JinaVLForSequenceClassificationConfig,
"JambaForSequenceClassification": JambaForSequenceClassificationConfig,
"GptOssForCausalLM": GptOssForCausalLMConfig,
"MambaForCausalLM": MambaModelConfig,
"Mamba2ForCausalLM": MambaModelConfig,
"FalconMambaForCausalLM": MambaModelConfig,
"DeepseekV32ForCausalLM": DeepseekV32ForCausalLM,
"NemotronHForCausalLM": NemotronHForCausalLMConfig,
"NemotronHPuzzleForCausalLM": NemotronHForCausalLMConfig,
"Qwen3_5ForConditionalGeneration": Qwen3_5ForConditionalGenerationConfig,
"Qwen3_5MoeForConditionalGeneration": Qwen3_5ForConditionalGenerationConfig,
"VoyageQwen3BidirectionalEmbedModel": VoyageQwen3BidirectionalEmbedModelConfig,
"XLMRobertaModel": JinaRobertaModelConfig,
}
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