Add Qwen 3.5 and Nemotron 3 (nemotron_h) model support

dam/merge.py

@@ -24,6 +24,12 @@ def fix_config(save_path, num_models, non_linearity, merge_embedding_layers, mer
     elif data['model_type'] == "llama":
         data['model_type'] = "mergedllama"
         data['architectures'][0] = 'MergedLlamaForCausalLM'
+    elif data['model_type'] == "qwen3_5_text" or data['model_type'] == "qwen3_5":
+        data['model_type'] = "mergedqwen3_5"
+        data['architectures'][0] = 'MergedQwen3_5ForCausalLM'
+    elif data['model_type'] == "nemotron_h":
+        data['model_type'] = "mergednemotron_h"
+        data['architectures'][0] = 'MergedNemotronHForCausalLM'
 
     data['num_merged_models'] = num_models
     data['non_linearity'] = non_linearity

dam/model_preparation.py

@@ -5,14 +5,22 @@
 from modeling.mistral.modeling import MergedMistralForCausalLM
 from modeling.llama3.config import MergedLlamaConfig
 from modeling.llama3.modeling import MergedLlamaForCausalLM
+from modeling.qwen3_5.config import MergedQwen3_5Config
+from modeling.qwen3_5.modeling import MergedQwen3_5ForCausalLM
+from modeling.nemotron.config import MergedNemotronHConfig
+from modeling.nemotron.modeling import MergedNemotronHForCausalLM
 from glom import glom, Assign
 from modeling.dam import DAMLinearLayer
 from utils import find_linear_layers, find_embedding_layers
 
 AutoConfig.register("mergedmistral", MergedMistralConfig)
 AutoConfig.register("mergedllama", MergedLlamaConfig)
+AutoConfig.register("mergedqwen3_5", MergedQwen3_5Config)
+AutoConfig.register("mergednemotron_h", MergedNemotronHConfig)
 AutoModelForCausalLM.register(MergedMistralConfig, MergedMistralForCausalLM)
 AutoModelForCausalLM.register(MergedLlamaConfig, MergedLlamaForCausalLM)
+AutoModelForCausalLM.register(MergedQwen3_5Config, MergedQwen3_5ForCausalLM)
+AutoModelForCausalLM.register(MergedNemotronHConfig, MergedNemotronHForCausalLM)
 
 def print_trainable_parameters(model):
     """

dam/modeling/nemotron/config.py

@@ -0,0 +1,253 @@
+# Copyright 2024-2025 NVIDIA Corporation 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.
+"""Merged Nemotron-H model configuration for DAM."""
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class MergedNemotronHConfig(PretrainedConfig):
+    r"""
+    Configuration class for the DAM-merged Nemotron-H model.
+
+    This extends the NemotronHConfig with DAM-specific parameters for
+    differentiable adaptive merging. Nemotron-H uses a hybrid architecture
+    with three block types: Mamba-2 SSM, standard attention, and MoE.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 131072):
+            Vocabulary size of the Nemotron model.
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for attention layers.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            Number of key_value heads for GQA.
+        head_dim (`int`, *optional*, defaults to 128):
+            Attention head dimension.
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
+            Maximum sequence length.
+        intermediate_size (`int`, *optional*, defaults to 21504):
+            Dimension of the MLP representations.
+        mlp_hidden_act (`str`, *optional*, defaults to `"relu2"`):
+            Activation function for MLP layers (ReLU squared).
+        mlp_bias (`bool`, *optional*, defaults to `False`):
+            Whether MLP layers use bias.
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether attention projection layers use bias.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            Attention dropout rate.
+        sliding_window (`int`, *optional*):
+            Sliding window attention size, if applicable.
+        layers_block_type (`list[str]`, *optional*):
+            Per-layer block type specification. Each entry is one of
+            "mamba", "attention", or "moe".
+        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
+            Epsilon for layer normalization.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether to return last key/values attentions.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            Base period of the RoPE embeddings.
+        ssm_state_size (`int`, *optional*, defaults to 128):
+            SSM state dimension for Mamba layers.
+        mamba_num_heads (`int`, *optional*, defaults to 128):
+            Number of Mamba heads.
+        mamba_head_dim (`int`, *optional*, defaults to 64):
+            Mamba head dimension.
+        mamba_hidden_act (`str`, *optional*, defaults to `"silu"`):
+            Activation function for Mamba layers.
+        n_groups (`int`, *optional*, defaults to 8):
+            Number of groups in Mamba-2.
+        conv_kernel (`int`, *optional*, defaults to 4):
+            Kernel size for conv1d in Mamba layers.
+        expand (`int`, *optional*, defaults to 2):
+            Expansion factor for Mamba layers.
+        use_conv_bias (`bool`, *optional*, defaults to `True`):
+            Whether conv1d uses bias.
+        chunk_size (`int`, *optional*, defaults to 128):
+            Chunk size for Mamba-2 SSD computation.
+        mamba_proj_bias (`bool`, *optional*, defaults to `False`):
+            Whether Mamba projection layers use bias.
+        n_routed_experts (`int`, *optional*, defaults to 8):
+            Number of routed experts in MoE layers.
+        n_shared_experts (`int`, *optional*, defaults to 1):
+            Number of shared experts in MoE layers.
+        moe_intermediate_size (`int`, *optional*, defaults to 7688):
+            Intermediate size for MoE expert MLPs.
+        moe_shared_expert_intermediate_size (`int`, *optional*, defaults to 7688):
+            Intermediate size for shared expert MLPs.
+        num_experts_per_tok (`int`, *optional*, defaults to 2):
+            Number of experts activated per token.
+        num_merged_models (`int`, *optional*, defaults to 3):
+            The number of models being merged.
+        init_merger_values (`list[float]`, *optional*, defaults to []):
+            Initial values for the merger coefficients.
+        use_tanh (`bool`, *optional*, defaults to False):
+            Whether to apply tanh non-linearity to merger coefficients.
+        dam_embedding_layer (`bool`, *optional*, defaults to True):
+            Whether embedding layers use DAM merging.
+        dam_layernorms (`bool`, *optional*, defaults to True):
+            Whether layer normalization uses DAM merging.
+        uses_base_model (`bool`, *optional*, defaults to True):
+            Whether the base model is included in the merge.
+        is_embedding_coef_trainable (`bool`, *optional*, defaults to False):
+            Whether embedding merger coefficients are trainable.
+        is_norm_coef_trainable (`bool`, *optional*, defaults to False):
+            Whether norm merger coefficients are trainable.
+    """
+
+    model_type = "mergednemotron_h"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=131072,
+        hidden_size=4096,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        head_dim=128,
+        max_position_embeddings=4096,
+        intermediate_size=21504,
+        mlp_hidden_act="relu2",
+        mlp_bias=False,
+        attention_bias=False,
+        attention_dropout=0.0,
+        sliding_window=None,
+        layers_block_type=None,
+        layer_norm_epsilon=1e-5,
+        initializer_range=0.02,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        # Mamba-2 parameters
+        ssm_state_size=128,
+        mamba_num_heads=128,
+        mamba_head_dim=64,
+        mamba_hidden_act="silu",
+        n_groups=8,
+        conv_kernel=4,
+        expand=2,
+        time_step_min=0.001,
+        time_step_max=0.1,
+        time_step_floor=1e-4,
+        use_conv_bias=True,
+        chunk_size=128,
+        mamba_proj_bias=False,
+        use_mamba_kernels=True,
+        residual_in_fp32=False,
+        rescale_prenorm_residual=True,
+        # MoE parameters
+        n_routed_experts=8,
+        n_shared_experts=1,
+        moe_intermediate_size=7688,
+        moe_shared_expert_intermediate_size=7688,
+        moe_latent_size=None,
+        moe_shared_expert_overlap=True,
+        num_experts_per_tok=2,
+        routed_scaling_factor=1.0,
+        norm_topk_prob=True,
+        use_bias=False,
+        # DAM-specific parameters
+        num_merged_models=3,
+        init_merger_values=[],
+        use_tanh=False,
+        model_index=None,
+        dam_embedding_layer=True,
+        dam_layernorms=True,
+        uses_base_model=True,
+        is_embedding_coef_trainable=False,
+        is_norm_coef_trainable=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.head_dim = head_dim
+        self.max_position_embeddings = max_position_embeddings
+        self.intermediate_size = intermediate_size
+        self.mlp_hidden_act = mlp_hidden_act
+        self.mlp_bias = mlp_bias
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.sliding_window = sliding_window
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_range = initializer_range
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+
+        # Mamba-2
+        self.ssm_state_size = ssm_state_size
+        self.mamba_num_heads = mamba_num_heads
+        self.mamba_head_dim = mamba_head_dim
+        self.mamba_hidden_act = mamba_hidden_act
+        self.n_groups = n_groups
+        self.conv_kernel = conv_kernel
+        self.expand = expand
+        self.time_step_min = time_step_min
+        self.time_step_max = time_step_max
+        self.time_step_floor = time_step_floor
+        self.use_conv_bias = use_conv_bias
+        self.chunk_size = chunk_size
+        self.mamba_proj_bias = mamba_proj_bias
+        self.use_mamba_kernels = use_mamba_kernels
+        self.residual_in_fp32 = residual_in_fp32
+        self.rescale_prenorm_residual = rescale_prenorm_residual
+
+        # MoE
+        self.n_routed_experts = n_routed_experts
+        self.n_shared_experts = n_shared_experts
+        self.moe_intermediate_size = moe_intermediate_size
+        self.moe_shared_expert_intermediate_size = moe_shared_expert_intermediate_size
+        self.moe_latent_size = moe_latent_size
+        self.moe_shared_expert_overlap = moe_shared_expert_overlap
+        self.num_experts_per_tok = num_experts_per_tok
+        self.routed_scaling_factor = routed_scaling_factor
+        self.norm_topk_prob = norm_topk_prob
+        self.use_bias = use_bias
+
+        # Layer types: default to alternating mamba/moe/attention/moe pattern
+        if layers_block_type is None:
+            default_pattern = ["mamba", "moe", "attention", "moe"]
+            self.layers_block_type = [
+                default_pattern[i % len(default_pattern)]
+                for i in range(num_hidden_layers)
+            ]
+        else:
+            self.layers_block_type = layers_block_type
+
+        # DAM-specific
+        self.num_merged_models = num_merged_models
+        self.init_merger_values = init_merger_values
+        self.use_tanh = use_tanh
+        self.model_index = model_index
+        self.dam_embedding_layer = dam_embedding_layer
+        self.dam_layernorms = dam_layernorms
+        self.uses_base_model = uses_base_model
+        self.is_embedding_coef_trainable = is_embedding_coef_trainable
+        self.is_norm_coef_trainable = is_norm_coef_trainable
+
+        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,
+        )