model: Support Hybrid Mamba2 NemotronHForCausalLM (nvidia/NVIDIA-Nemotron-Nano-9B-v2) (#10909)
Signed-off-by: Netanel Haber <nhaber@nvidia.com>
This commit is contained in:
Netanel Haber
@@ -0,0 +1,514 @@
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# Copyright 2023-2025 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/nemotron_h.py
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"""Inference-only NemotronH model."""
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from collections.abc import Iterable
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from typing import Optional, Union
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import torch
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from torch import nn
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from sglang.srt.configs import NemotronHConfig
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from sglang.srt.configs.nemotron_h import ATTENTION, MAMBA, MLP
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from sglang.srt.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from sglang.srt.layers.activation import ReLU2
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from sglang.srt.layers.attention.hybrid_linear_attn_backend import (
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HybridLinearAttnBackend,
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Mamba2AttnBackend,
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)
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from sglang.srt.layers.attention.mamba.mamba import MambaMixer2
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from sglang.srt.layers.layernorm import RMSNorm
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from sglang.srt.layers.linear import (
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ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.quantization import QuantizationConfig
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE,
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
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from sglang.srt.model_loader.weight_utils import (
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default_weight_loader,
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maybe_remap_kv_scale_name,
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)
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from sglang.srt.utils import add_prefix, make_layers_non_pp
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from sglang.utils import logger
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class NemotronHMLP(nn.Module):
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def __init__(
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self,
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config: NemotronHConfig,
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layer_idx: int,
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quant_config: Optional[QuantizationConfig] = None,
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bias: bool = False,
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prefix: str = "",
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) -> None:
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super().__init__()
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hybrid_override_pattern = config.hybrid_override_pattern
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mlp_index = hybrid_override_pattern[: layer_idx + 1].count("-") - 1
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if isinstance(config.intermediate_size, list):
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if len(config.intermediate_size) == 1:
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intermediate_size = config.intermediate_size[0]
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else:
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intermediate_size = config.intermediate_size[mlp_index]
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else:
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intermediate_size = config.intermediate_size
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self.up_proj = ColumnParallelLinear(
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input_size=config.hidden_size,
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output_size=intermediate_size,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.up_proj",
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)
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self.down_proj = RowParallelLinear(
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input_size=intermediate_size,
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output_size=config.hidden_size,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.down_proj",
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)
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self.act_fn = ReLU2()
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def forward(self, x: torch.Tensor):
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x, _ = self.up_proj(x)
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x = self.act_fn(x)
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x, _ = self.down_proj(x)
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return x
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class NemotronHMLPDecoderLayer(nn.Module):
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def __init__(
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self,
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config: NemotronHConfig,
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layer_idx: int,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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self.mixer = NemotronHMLP(
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config,
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quant_config=quant_config,
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bias=config.mlp_bias,
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prefix=f"{prefix}.mixer",
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layer_idx=layer_idx,
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)
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def forward(
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self,
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*,
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hidden_states: torch.Tensor,
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residual: Optional[torch.Tensor],
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forward_batch: ForwardBatch,
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) -> tuple[torch.Tensor, torch.Tensor]:
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if residual is None:
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residual = hidden_states
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hidden_states = self.norm(hidden_states)
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else:
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hidden_states, residual = self.norm(hidden_states, residual)
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hidden_states = self.mixer.forward(hidden_states)
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return hidden_states, residual
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class NemotronHMambaDecoderLayer(nn.Module):
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def __init__(
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self,
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config: NemotronHConfig,
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layer_idx: int,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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self.layer_id = layer_idx
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self.mixer = MambaMixer2(
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cache_params=config.mamba2_cache_params,
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hidden_size=config.hidden_size,
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use_conv_bias=config.use_conv_bias,
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use_bias=config.use_bias,
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n_groups=config.mamba_n_groups,
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rms_norm_eps=config.rms_norm_eps,
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activation=config.mamba_hidden_act,
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quant_config=quant_config,
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)
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def forward(
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self,
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*,
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hidden_states: torch.Tensor,
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residual: Optional[torch.Tensor],
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forward_batch: ForwardBatch,
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) -> tuple[torch.Tensor, torch.Tensor]:
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if residual is None:
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residual = hidden_states
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hidden_states = self.norm(hidden_states)
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else:
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hidden_states, residual = self.norm(hidden_states, residual)
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output = torch.empty_like(hidden_states)
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attn_backend = forward_batch.attn_backend
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assert isinstance(attn_backend, HybridLinearAttnBackend)
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assert isinstance(attn_backend.linear_attn_backend, Mamba2AttnBackend)
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attn_backend.linear_attn_backend.forward(
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mixer=self.mixer,
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layer_id=self.layer_id,
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hidden_states=hidden_states,
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output=output,
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use_triton_causal_conv=True, # TODO: investigate need of `use_triton_causal_conv`
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)
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return output, residual
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class NemotronHAttention(nn.Module):
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def __init__(
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self,
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config: NemotronHConfig,
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layer_idx: int,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = config.num_attention_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = config.num_key_value_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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if hasattr(config, "head_dim") and config.head_dim is not None:
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self.head_dim = config.head_dim
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else:
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self.head_dim = config.hidden_size // self.total_num_heads
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.qkv_proj = QKVParallelLinear(
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config.hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv_proj",
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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config.hidden_size,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.o_proj",
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)
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self.attn = RadixAttention(
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self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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layer_id=layer_idx,
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quant_config=quant_config,
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prefix=add_prefix("attn", prefix),
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)
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def forward(
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self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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attn_output = self.attn.forward(q, k, v, forward_batch)
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output, _ = self.o_proj(attn_output)
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return output
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class NemotronHAttentionDecoderLayer(nn.Module):
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def __init__(
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self,
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config: NemotronHConfig,
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layer_idx: int,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.mixer = NemotronHAttention(
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config,
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layer_idx,
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quant_config,
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prefix=f"{prefix}.mixer",
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)
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def forward(
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self,
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*,
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hidden_states: torch.Tensor,
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residual: Optional[torch.Tensor],
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forward_batch: ForwardBatch,
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) -> tuple[torch.Tensor, torch.Tensor]:
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if residual is None:
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residual = hidden_states
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hidden_states = self.norm(hidden_states)
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else:
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hidden_states, residual = self.norm(hidden_states, residual)
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hidden_states = self.mixer.forward(
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hidden_states=hidden_states, forward_batch=forward_batch
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)
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return hidden_states, residual
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Layers = (
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NemotronHAttentionDecoderLayer
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| NemotronHMLPDecoderLayer
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| NemotronHMambaDecoderLayer
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)
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ALL_DECODER_LAYER_TYPES: dict[str, type[Layers]] = {
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ATTENTION: NemotronHAttentionDecoderLayer,
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MLP: NemotronHMLPDecoderLayer,
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MAMBA: NemotronHMambaDecoderLayer,
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}
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class NemotronHModel(nn.Module):
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def __init__(
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self,
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*,
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config: NemotronHConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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lora_config = None
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self.config = config
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lora_vocab = (
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(lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1))
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if lora_config
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else 0
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)
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self.vocab_size = config.vocab_size + lora_vocab
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self.org_vocab_size = config.vocab_size
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self.embed_tokens = VocabParallelEmbedding(
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self.vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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)
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def get_layer(idx: int, prefix: str):
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layer_class = ALL_DECODER_LAYER_TYPES[config.hybrid_override_pattern[idx]]
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return layer_class(config, idx, quant_config=quant_config, prefix=prefix)
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self.layers = make_layers_non_pp(
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len(config.hybrid_override_pattern), get_layer, prefix=f"{prefix}.layers"
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)
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self.norm_f = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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forward_batch: ForwardBatch,
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pp_proxy_tensors: Optional[PPProxyTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, PPProxyTensors]:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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residual = None
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else:
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assert pp_proxy_tensors is not None
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hidden_states = pp_proxy_tensors["hidden_states"]
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residual = pp_proxy_tensors["residual"]
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residual = None
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for layer in self.layers:
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if not isinstance(layer, Layers):
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raise ValueError(f"Unknown layer type: {type(layer)}")
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hidden_states, residual = layer.forward(
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hidden_states=hidden_states,
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residual=residual,
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forward_batch=forward_batch,
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)
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if not get_pp_group().is_last_rank:
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return PPProxyTensors(
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{"hidden_states": hidden_states, "residual": residual}
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)
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hidden_states, _ = self.norm_f(hidden_states, residual)
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return hidden_states
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class NemotronHForCausalLM(nn.Module):
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remap_prefix = {"backbone": "model"}
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remap_substr = {"A_log": "A", "embeddings": "embed_tokens"}
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# LoRA specific attributes
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embedding_modules = {
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"embed_tokens": "input_embeddings",
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"lm_head": "output_embeddings",
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}
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embedding_padding_modules = ["lm_head"]
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def __init__(
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self,
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*,
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config: NemotronHConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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lora_config = None
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self.config = config
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self.model = self._init_model(
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config=config, quant_config=quant_config, prefix=prefix
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)
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if self.config.tie_word_embeddings:
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self.lm_head = self.model.embed_tokens
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else:
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self.unpadded_vocab_size = config.vocab_size
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if lora_config:
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self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
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self.lm_head = ParallelLMHead(
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self.unpadded_vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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padding_size=(
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DEFAULT_VOCAB_PADDING_SIZE
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# We need bigger padding if using lora for kernel
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# compatibility
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if not lora_config
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else lora_config.lora_vocab_padding_size
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),
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quant_config=quant_config,
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prefix=add_prefix("lm_head", prefix),
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)
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self.logits_processor = LogitsProcessor(config)
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def _init_model(
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self,
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config: NemotronHConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
|
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):
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return NemotronHModel(config=config, quant_config=quant_config, prefix=prefix)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.get_input_embeddings(input_ids)
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@torch.no_grad()
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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forward_batch: ForwardBatch,
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input_embeds: Optional[torch.Tensor] = None,
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pp_proxy_tensors: Optional[PPProxyTensors] = None,
|
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):
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hidden_states = self.model.forward(
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input_ids, positions, forward_batch, pp_proxy_tensors, input_embeds
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)
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return self.logits_processor(
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input_ids, hidden_states, self.lm_head, forward_batch
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)
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def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
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return self.mamba_cache.copy_inputs_before_cuda_graphs(input_buffers, **kwargs)
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def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
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return self.mamba_cache.get_seqlen_agnostic_capture_inputs(batch_size)
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> None:
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
|
||||
("qkv_proj", "q_proj", "q"),
|
||||
("qkv_proj", "k_proj", "k"),
|
||||
("qkv_proj", "v_proj", "v"),
|
||||
]
|
||||
|
||||
updated_weights = []
|
||||
for name, loaded_weight in weights:
|
||||
for prefix, new_key in self.remap_prefix.items():
|
||||
if name.startswith(prefix):
|
||||
name = name.replace(prefix, new_key)
|
||||
for substr, new_key in self.remap_substr.items():
|
||||
if substr in name:
|
||||
name = name.replace(substr, new_key)
|
||||
updated_weights.append((name, loaded_weight))
|
||||
params_dict = dict(self.named_parameters())
|
||||
|
||||
for name, loaded_weight in updated_weights:
|
||||
if "scale" in name:
|
||||
name = maybe_remap_kv_scale_name(name, params_dict)
|
||||
if name is None:
|
||||
continue
|
||||
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
if name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
if name in params_dict.keys():
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(
|
||||
param, "weight_loader", default_weight_loader
|
||||
)
|
||||
weight_loader(param, loaded_weight)
|
||||
else:
|
||||
logger.warning(f"Parameter {name} not found in params_dict")
|
||||
|
||||
|
||||
EntryClass = [NemotronHForCausalLM]
|
||||
Reference in New Issue
Block a user