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[model] Add mamba2 and Falcon-H1 support. (#10988)

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Co-authored-by: Younes Belkada <younes.belkada@tii.ae>
Co-authored-by: Younes B <49240599+younesbelkada@users.noreply.github.com>
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ilyasch2
2025-10-02 15:15:36 +04:00
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parent b658be6f6a
commit 083629c235
18 changed files with 4533 additions and 1 deletions
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2
python/sglang/srt/configs/__init__.py
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@@ -4,6 +4,7 @@ from sglang.srt.configs.deepseekvl2 import DeepseekVL2Config
from sglang.srt.configs.dots_ocr import DotsOCRConfig
from sglang.srt.configs.dots_vlm import DotsVLMConfig
from sglang.srt.configs.exaone import ExaoneConfig
from sglang.srt.configs.falcon_h1 import FalconH1Config
from sglang.srt.configs.janus_pro import MultiModalityConfig
from sglang.srt.configs.kimi_vl import KimiVLConfig
from sglang.srt.configs.kimi_vl_moonvit import MoonViTConfig
@@ -30,4 +31,5 @@ __all__ = [
"Qwen3NextConfig",
"DotsVLMConfig",
"DotsOCRConfig",
"FalconH1Config",
]

360
python/sglang/srt/configs/falcon_h1.py Normal file
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@@ -0,0 +1,360 @@
# coding=utf-8
# Copyright 2024 TII and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Falcon-H1 model configuration"""
import enum
import os
import numpy as np
import torch
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
from transformers.utils import logging
from sglang.srt.distributed.utils import divide
from sglang.srt.layers.attention.mamba.mamba_utils import MambaStateShapeCalculator
from sglang.srt.layers.dp_attention import (
get_attention_tp_size,
get_tensor_model_parallel_world_size,
)
logger = logging.get_logger(__name__)
class FalconH1Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FalconH1Model`]. It is used to instantiate a
FalconH1Model model according to the specified arguments, defining the model architecture. Instantiating a configuration
with defaults taken from [ibm-fms/FalconH1-9.8b-2.2T-hf](https://huggingface.co/ibm-fms/FalconH1-9.8b-2.2T-hf).
The FalconH1Model is a hybrid [mamba2](https://github.com/state-spaces/mamba) architecture with SwiGLU.
The checkpoints are jointly trained by IBM, Princeton, and UIUC.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 128000):
Vocabulary size of the FalconH1 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`FalconH1Model`]
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the
model has a output word embedding layer.
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 14336):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 8):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details, check out [this
paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `8`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
num_logits_to_keep (`int` or `None`, *optional*, defaults to 1):
Number of prompt logits to calculate during generation. If `None`, all logits will be calculated. If an
integer value, only last `num_logits_to_keep` logits will be calculated. Default is 1 because only the
logits of the last prompt token are needed for generation. For long sequences, the logits for the entire
sequence may use a lot of memory so, setting `num_logits_to_keep=1` will reduce memory footprint
significantly.
pad_token_id (`int`, *optional*, defaults to 0):
The id of the padding token.
bos_token_id (`int`, *optional*, defaults to 1):
The id of the "beginning-of-sequence" token.
eos_token_id (`int`, *optional*, defaults to 2):
The id of the "end-of-sequence" token.
max_position_embeddings (`int`, *optional*, defaults to 8192):
Max cached sequence length for the model
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
mamba_d_ssm (`int`, *optional*, defaults to 1024):
The dimension of the SSM state space latents.
mamba_n_heads (`int`, *optional*, defaults to 128):
The number of mamba heads used in the v2 implementation.
mamba_d_head (`int`, *optional*, defaults to `"auto"`):
Head embedding dimension size
mamba_n_groups (`int`, *optional*, defaults to 1):
The number of the mamba groups used in the v2 implementation.
mamba_d_state (`int`, *optional*, defaults to 256):
The dimension the mamba state space latents
mamba_d_conv (`int`, *optional*, defaults to 4):
The size of the mamba convolution kernel
mamba_expand (`int`, *optional*, defaults to 2):
Expanding factor (relative to hidden_size) used to determine the mamba intermediate size
mamba_chunk_size (`int`, *optional*, defaults to 256):
The chunks in which to break the sequence when doing prefill/training
mamba_conv_bias (`bool`, *optional*, defaults to `True`):
Flag indicating whether or not to use bias in the convolution layer of the mamba mixer block.
mamba_proj_bias (`bool`, *optional*, defaults to `False`):
Flag indicating whether or not to use bias in the input and output projections (["in_proj", "out_proj"]) of the mamba mixer block
mamba_norm_before_gate (`bool`, *optional*, defaults to `True`):
Whether to use RMSNorm before the gate in the Mamba block
mamba_rms_norm (`bool`, *optional*, defaults to `False`):
Whether to use RMSNorm instead of LayerNorm in the Mamba block
projectors_bias (`bool`, *optional*, defaults to `False`):
Flag indicating whether or not to use bias in the input and output projections (["in_proj", "out_proj"]) of the attention block
rope_theta (`float`, *optional*, defaults to 100000.0):
The theta value used for the RoPE embeddings.
rope_scaling (`float`, *optional*):
The scaling value used for the RoPE embeddings. If `None`, no scaling is applied.
lm_head_multiplier (`float`, *optional*, defaults to 1.0):
The multiplier for the LM head. This is used to scale the output of the LM head.
embedding_multiplier (`float`, *optional*, defaults to 1.0):
The multiplier for the embedding layer. This is used to scale the output of the embedding layer.
mlp_multipliers (`list[float]`, *optional*):
The multipliers for the MLP layers. This is used to scale the output of the MLP layers. The first value is
the multiplier of gate layer, the second value is the multiplier of the down_proj layer.
key_multiplier (`float`, *optional*):
The multiplier for the key layer. This is used to scale the output of the key layer.
attention_out_multiplier (`float`, *optional*):
The multiplier for the attention output layer. This is used to scale the output of the attention output
attention_in_multiplier (`float`, *optional*):
The multiplier for the attention input layer. This is used to scale the output of the attention input layer.
ssm_multipliers (`list[float]`, *optional*):
The multipliers for the SSM layers. This is used to scale the output of the SSM layers.
ssm_in_multiplier (`float`, *optional*):
The multiplier for the SSM input layer. This is used to scale the output of the SSM input layer.
ssm_out_multiplier (`float`, *optional*):
The multiplier for the SSM output layer. This is used to scale the output of the SSM output layer.
"""
model_type = "falcon_h1"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=128000,
tie_word_embeddings=False,
hidden_size=4096,
intermediate_size=14336,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=8,
hidden_act="silu",
initializer_range=0.02,
rms_norm_eps=1e-5,
use_cache=True,
num_logits_to_keep=1,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
max_position_embeddings=8192,
attention_dropout=0.0,
mamba_d_ssm=1024,
mamba_n_heads=128,
mamba_d_head="auto",
mamba_n_groups=1,
mamba_d_state=256,
mamba_d_conv=4,
mamba_expand=2,
mamba_chunk_size=256,
mamba_conv_bias=True,
mamba_proj_bias=False,
mamba_norm_before_gate=True,
mamba_rms_norm=False,
projectors_bias=False,
rope_theta=100000.0,
rope_scaling=None,
lm_head_multiplier=1.0,
embedding_multiplier=1.0,
mlp_multipliers=None,
key_multiplier=None,
attention_out_multiplier=None,
attention_in_multiplier=None,
ssm_multipliers=None,
ssm_in_multiplier=None,
ssm_out_multiplier=None,
**kwargs,
):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.max_position_embeddings = max_position_embeddings
self.attention_dropout = attention_dropout
self.attention_bias = False
self.mlp_bias = False
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.num_logits_to_keep = num_logits_to_keep
self.rope_theta = rope_theta
self.rope_scaling = None
self.rope_scaling = rope_scaling
self.projectors_bias = projectors_bias
mamba_intermediate = (
mamba_expand * hidden_size if mamba_d_ssm is None else mamba_d_ssm
)
if mamba_intermediate % mamba_n_heads != 0:
raise ValueError("mamba_n_heads must divide mamba_expand * hidden_size")
# for the mamba_v2, must satisfy the following
if mamba_d_head == "auto":
mamba_d_head = mamba_intermediate // mamba_n_heads
if mamba_d_head * mamba_n_heads != mamba_intermediate:
raise ValueError(
"The dimensions for the Mamba head state do not match the model intermediate_size"
)
self.mamba_d_ssm = mamba_d_ssm
self.mamba_n_heads = mamba_n_heads
self.mamba_d_head = mamba_d_head
self.mamba_n_groups = mamba_n_groups
self.mamba_d_state = mamba_d_state
self.mamba_d_conv = mamba_d_conv
self.mamba_expand = mamba_expand
self.mamba_chunk_size = mamba_chunk_size
self.mamba_conv_bias = mamba_conv_bias
self.mamba_proj_bias = mamba_proj_bias
self.mamba_norm_before_gate = mamba_norm_before_gate
self.mamba_rms_norm = mamba_rms_norm
self.lm_head_multiplier = lm_head_multiplier
self.embedding_multiplier = embedding_multiplier
if mlp_multipliers is not None:
self.mlp_multipliers = mlp_multipliers
else:
self.mlp_multipliers = [1.0, 1.0]
if attention_out_multiplier is not None:
self.attention_out_multiplier = attention_out_multiplier
else:
self.attention_out_multiplier = 1.0
if attention_in_multiplier is not None:
self.attention_in_multiplier = attention_in_multiplier
else:
self.attention_in_multiplier = 1.0
if key_multiplier is not None:
self.key_multiplier = key_multiplier
else:
self.key_multiplier = 1.0
if ssm_multipliers is not None:
self.ssm_multipliers = ssm_multipliers
else:
self.ssm_multipliers = [1.0, 1.0, 1.0, 1.0, 1.0]
if ssm_in_multiplier is not None:
self.ssm_in_multiplier = ssm_in_multiplier
else:
self.ssm_in_multiplier = 1.0
if ssm_out_multiplier is not None:
self.ssm_out_multiplier = ssm_out_multiplier
else:
self.ssm_out_multiplier = 1.0
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
@property
def layers_block_type(self):
return ["falcon_h1" for i in range(self.num_hidden_layers)]
@property
def mamba_cache_per_req(self):
conv_state_shape, temporal_state_shape, conv_dtype, ssm_dtype, mamba_layers = (
self.hybrid_gdn_params
)
mamba_layers_len = len(mamba_layers)
return (
int(np.prod(conv_state_shape)) * conv_dtype.itemsize
+ int(np.prod(temporal_state_shape)) * ssm_dtype.itemsize
) * mamba_layers_len
@property
def full_attention_layer_ids(self):
# For Falcon-H1, we do have attention on all layers
return range(self.num_hidden_layers)
@property
def linear_layer_ids(self):
# For Falcon-H1, we do have mamba on all layers
return range(self.num_hidden_layers)
@property
def hybrid_gdn_params(self):
world_size = get_tensor_model_parallel_world_size()
n_groups = self.mamba_n_groups
if self.mamba_n_groups % world_size != 0:
# - for TP we shard conv_dim by sharding on n_groups,
# - but if n_groups cannot divide tp_size, we need to
# extend some extra groups
extra_groups = MambaStateShapeCalculator.extra_groups_for_head_shards(
self.mamba_n_groups, world_size
)
n_groups += extra_groups
conv_dim = self.mamba_d_ssm + 2 * n_groups * self.mamba_d_state
conv_state_shape = (
divide(conv_dim, world_size),
self.mamba_d_conv - 1,
)
# we TP-ize on the heads dimension
temporal_state_shape = (
self.mamba_d_state,
self.mamba_d_head,
divide(self.mamba_n_heads, world_size),
)
conv_dtype = torch.bfloat16
dtype_map = {
"float32": torch.float32,
"bfloat16": torch.bfloat16,
}
ssm_dtype = dtype_map[os.environ["SGLANG_MAMBA_SSM_DTYPE"]]
mamba_layers = self.linear_layer_ids
return (
conv_state_shape,
temporal_state_shape,
conv_dtype,
ssm_dtype,
mamba_layers,
)