Upload NemotronFlashForCausalLM

config.json

@@ -110,7 +110,7 @@
   "mamba_proj_bias": false,
   "max_position_embeddings": 29000,
   "mlp_hidden_act": "silu",
-  "model_type": "jamba",
+  "model_type": "nemotron_flash",
   "new_seq_length": 2048,
   "num_attention_heads": 24,
   "num_experts": 1,

configuration_nemotron_flash.py

@@ -1,18 +1,7 @@
 # coding=utf-8
-# Copyright 2024 AI21 Labs Ltd. 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.
-""" Nemotron Flash model configuration"""
+# Copyright 2025 NVIDIA Corporation. All rights reserved.
+
+""" Nemotron-Flash model configuration"""
 import math
 
 from transformers.configuration_utils import PretrainedConfig
@@ -23,99 +12,7 @@ logger = logging.get_logger(__name__)
 
 
 class NemotronFlashConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`JambaModel`]. It is used to instantiate a
-    Jamba model according to the specified arguments, defining the model architecture. Instantiating a configuration
-    with the defaults will yield a similar configuration to that of the jamba-small architecture.
-
-    [ai21labs/jamba-small](https://huggingface.co/ai21labs/Jamba-v0.1)
-
-    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 65536):
-            Vocabulary size of the Jamba model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`JambaModel`]
-        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 checkout [this
-            paper](https://arxiv.org/pdf/2305.13245.pdf). 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-06):
-            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`.
-        calc_logits_for_entire_prompt (`bool`, *optional*, defaults to `False`):
-            Whether or not to calculate logits for entire prompt during generation. If `False`, only the logits of the
-            last prompt token will be calculated, which are the only logits needed for generation. For long sequences,
-            the logits for the entire sequence may use a lot of memory so setting `calc_logits_for_entire_prompt=False`
-            will reduce memory footprint significantly.
-            Note: some generation features may not be available if this is set to `False`.
-        output_router_logits (`bool`, *optional*, defaults to `False`):
-            Whether or not the router logits should be returned by the model. Enabling this will also
-            allow the model to output the auxiliary loss. See [here]() for more details
-        router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
-            The aux loss factor for the total loss.
-        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.
-        sliding_window (`int`, *optional*):
-            Sliding window attention window size. If not specified, will default to `None`.
-        n_ctx (`int`, *optional*, defaults to 262144):
-            This value doesn't have any real effect. The maximum sequence length that this model is intended to be
-            used with. It can be used with longer sequences, but performance may degrade.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-        num_experts_per_tok (`int`, *optional*, defaults to 2):
-            The number of experts to root per-token, can be also interpreted as the `top-p` routing
-            parameter
-        num_experts (`int`, *optional*, defaults to 16):
-            Number of experts per Sparse MLP layer.
-        use_mamba_kernels (`bool`, *optional*, defaults to `True`):
-            Flag indicating whether or not to use the fast mamba kernels. These are available only if `mamba-ssm` and
-            `causal-conv1d` are installed, and the mamba modules are running on a CUDA device. Raises ValueError if
-            `True` and kernels are not available
-        mamba_d_state (`int`, *optional*, defaults to 16):
-            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_dt_rank (`Union[int,str]`, *optional*, defaults to `"auto"`):
-            Rank of the the mamba discretization projection matrix. `"auto"` means that it will default to `math.ceil(self.hidden_size / 16)`
-        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_inner_layernorms (`bool`, *optional*, defaults to `True`):
-            Flag indicating whether or not to apply layernorms to internal mamba activations
-
-    """
-
-    model_type = "jamba"
+    model_type = "nemotron_flash"
     keys_to_ignore_at_inference = ["past_key_values"]
 
     def __init__(
@@ -151,23 +48,14 @@ class NemotronFlashConfig(PretrainedConfig):
             mamba_conv_bias=True,
             mamba_proj_bias=False,
             mamba_inner_layernorms=True,
-
             hybrid_decoder_layer='mamba',
-
             global_attn_idx=None,
-
             attn_implementation_new='flash_attention_2',
-
             mamba2_headdim=64,
-
             rope_type=None,
-            
             layer_types=None,
-            
             ffn_expand_ratio=None,
-            
             d_conv=4,
-
             **kwargs,
     ):
         self.vocab_size = vocab_size
@@ -181,7 +69,6 @@ class NemotronFlashConfig(PretrainedConfig):
         self.orig_max_position_embeddings = orig_max_position_embeddings
         self.attention_dropout = attention_dropout
 
-        # for backward compatibility
         if num_key_value_heads is None:
             num_key_value_heads = num_attention_heads
 
@@ -207,7 +94,6 @@ class NemotronFlashConfig(PretrainedConfig):
         self.mamba_proj_bias = mamba_proj_bias
         self.mamba_inner_layernorms = mamba_inner_layernorms
 
-        # added by Xin 
         self.kq_norm = kwargs.pop("kq_norm", None)
         self.rope = kwargs.pop("rope", False)
         self.rope_theta = kwargs.pop("rope_theta", 10000.0)
@@ -216,7 +102,6 @@ class NemotronFlashConfig(PretrainedConfig):
         self.kq_head_dim = kwargs.pop("kq_head_dim", -1)
         self.v_head_dim = kwargs.pop("v_head_dim", -1)
 
-        #! adhoc change
         self.new_seq_length = 2048
 
         self.hybrid_decoder_layer = hybrid_decoder_layer