[Model] Nemotron Parse 1.1 Support (#30864)
Signed-off-by: amitz-nv <203509407+amitz-nv@users.noreply.github.com> Signed-off-by: Michael Goin <mgoin64@gmail.com> Co-authored-by: Michael Goin <mgoin64@gmail.com>
This commit is contained in:
amitz-nv
@@ -1220,7 +1220,7 @@ class NemotronH_Nano_VL_V2(
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n = pixel_values.shape[0]
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vit_embeds_list = []
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for i in range(0, n, micro_batch_size):
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vit_embeds = self.vision_model(pixel_values[i : i + micro_batch_size])
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_, vit_embeds = self.vision_model(pixel_values[i : i + micro_batch_size])
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vit_embeds = vit_embeds.to(dtype=torch.bfloat16)
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h = w = int(vit_embeds.shape[1] ** 0.5)
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vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
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@@ -1695,12 +1695,7 @@ class NemotronH_Nano_VL_V2(
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patch_size=patch_size,
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norm_mean=hf_config.norm_mean,
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norm_std=hf_config.norm_std,
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reg_tokens=(
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hf_config_vision.args.get("register_multiple")
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if hasattr(hf_config_vision, "args")
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and isinstance(hf_config_vision.args, dict)
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else None
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),
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**hf_config_vision.args,
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)
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return RadioModel(config=radio_config)
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958
vllm/model_executor/models/nemotron_parse.py
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958
vllm/model_executor/models/nemotron_parse.py
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@@ -0,0 +1,958 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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#
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# Adapted from https://github.com/amalad/vllm/blob/nemotron_parse/vllm/model_executor/models/nemotron_parse.py
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# that's based on https://huggingface.co/nvidia/NVIDIA-Nemotron-Parse-v1.1/blob/main/hf_nemotron_parse_modeling.py
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#
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# Bart classes based on old vLLM codebase:
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# https://github.com/vllm-project/vllm/blob/v0.10.2/vllm/model_executor/models/bart.py
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import math
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from collections.abc import Iterable, Mapping, Sequence
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from typing import Annotated, Literal
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import cv2
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import numpy as np
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import torch
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import torch.nn as nn
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from einops import rearrange
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from PIL import Image
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from timm.data.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
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from torchvision import transforms as T
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from transformers import (
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BartConfig,
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BatchFeature,
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PretrainedConfig,
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TensorType,
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)
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from vllm.attention.backends.abstract import AttentionType
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from vllm.config import CacheConfig, VllmConfig
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from vllm.config.lora import LoRAConfig
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from vllm.config.multimodal import BaseDummyOptions
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from vllm.logger import init_logger
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.linear import ColumnParallelLinear, RowParallelLinear
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.models.interfaces import (
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MultiModalEmbeddings,
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SupportsMultiModal,
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)
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from vllm.model_executor.models.radio import RadioModel
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from vllm.model_executor.models.whisper import WhisperAttention, WhisperCrossAttention
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (
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MultiModalDataDict,
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MultiModalFieldConfig,
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MultiModalKwargsItems,
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)
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from vllm.multimodal.parse import MultiModalDataItems
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from vllm.multimodal.processing import (
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BaseProcessingInfo,
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EncDecMultiModalProcessor,
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PromptReplacement,
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PromptUpdate,
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)
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from vllm.multimodal.profiling import BaseDummyInputsBuilder
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from vllm.transformers_utils.configs.radio import RadioConfig
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from vllm.transformers_utils.tokenizer import AnyTokenizer
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from vllm.utils.tensor_schema import TensorSchema, TensorShape
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logger = init_logger(__name__)
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DEFAULT_FINAL_IMAGE_SIZE = (2048, 1648)
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class BartScaledWordEmbedding(VocabParallelEmbedding):
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"""
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This module overrides VocabParallelEmbedding's
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forward by multiplying with embeddings scale.
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"""
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def __init__(
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self, num_embeddings: int, embedding_dim: int, embed_scale: float = 1.0
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):
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super().__init__(num_embeddings, embedding_dim)
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self.embed_scale = embed_scale
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def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
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return super().forward(input_ids) * self.embed_scale
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class BartParallelLMHead(ParallelLMHead):
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"""
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This module overrides ParallelLMHead's
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forward by dividing by embeddings scale,
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yielding effectively the inverse of
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BartScaledWordEmbedding
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"""
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def __init__(
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self, num_embeddings: int, embedding_dim: int, embed_scale: float = 1.0
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):
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super().__init__(num_embeddings, embedding_dim)
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self.embed_scale = embed_scale
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def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
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return super().forward(input_ids) / self.embed_scale
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class BartDecoderLayer(nn.Module):
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def __init__(
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self,
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config: BartConfig,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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):
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super().__init__()
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self.embed_dim = config.d_model
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self.self_attn = WhisperAttention(
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embed_dim=self.embed_dim,
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num_heads=config.decoder_attention_heads,
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attn_type=AttentionType.DECODER,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.self_attn",
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)
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self.activation_fn = get_act_fn(config.activation_function)
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self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
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"""
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afeldman-nm: personally I would call this "cross-attention",
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however I left the name as "encoder_attn" to maintain consistency
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with the name of the pretrained weights.
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"""
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self.encoder_attn = WhisperCrossAttention(
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self.embed_dim,
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config.decoder_attention_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.encoder_attn",
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)
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self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
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ffn_hidden_size = self.embed_dim
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ffn_intermediate_size = config.encoder_ffn_dim
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ffn_has_bias = True
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self.fc1 = ColumnParallelLinear(
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ffn_hidden_size,
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ffn_intermediate_size,
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bias=ffn_has_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.fc1",
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)
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self.fc2 = RowParallelLinear(
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ffn_intermediate_size,
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ffn_hidden_size,
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bias=ffn_has_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.fc2",
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)
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self.final_layer_norm = nn.LayerNorm(self.embed_dim)
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def forward(
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self,
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decoder_hidden_states: torch.Tensor,
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encoder_hidden_states: torch.Tensor | None = None,
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) -> torch.Tensor:
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r"""
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Args:
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decoder_hidden_states: torch.Tensor of *decoder* input embeddings.
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encoder_hidden_states: torch.Tensor of *encoder* input embeddings.
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Returns:
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Decoder layer output torch.Tensor
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"""
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residual = decoder_hidden_states
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# Self Attention
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hidden_states = self.self_attn(hidden_states=decoder_hidden_states)
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hidden_states = residual + hidden_states
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hidden_states = self.self_attn_layer_norm(hidden_states)
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# Cross-Attention Block
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residual = hidden_states
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hidden_states = self.encoder_attn(
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hidden_states=hidden_states,
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encoder_hidden_states=encoder_hidden_states,
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)
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hidden_states = residual + hidden_states
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hidden_states = self.encoder_attn_layer_norm(hidden_states)
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# Fully Connected
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residual = hidden_states
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fc1_out, _ = self.fc1(hidden_states)
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hidden_states = self.activation_fn(fc1_out)
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hidden_states, _ = self.fc2(hidden_states)
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hidden_states = residual + hidden_states
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hidden_states = self.final_layer_norm(hidden_states)
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return hidden_states
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class MBartDecoderLayer(BartDecoderLayer):
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def forward(
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self,
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decoder_hidden_states: torch.Tensor,
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encoder_hidden_states: torch.Tensor | None = None,
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) -> torch.Tensor:
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residual = decoder_hidden_states
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hidden_states = self.self_attn_layer_norm(decoder_hidden_states)
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# Self Attention
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hidden_states = self.self_attn(hidden_states=hidden_states)
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hidden_states = residual + hidden_states
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# Cross-Attention Block
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residual = hidden_states
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hidden_states = self.encoder_attn_layer_norm(hidden_states)
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hidden_states = self.encoder_attn(
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hidden_states=hidden_states,
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encoder_hidden_states=encoder_hidden_states,
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)
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hidden_states = residual + hidden_states
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# Fully Connected
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residual = hidden_states
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hidden_states = self.final_layer_norm(hidden_states)
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fc1_out, _ = self.fc1(hidden_states)
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hidden_states = self.activation_fn(fc1_out)
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hidden_states, _ = self.fc2(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states
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class MBartDecoderNoPos(nn.Module):
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"""
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Transformer decoder consisting of *config.decoder_layers* layers.
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Each layer is a [`BartDecoderLayer`]
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Args:
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config: BartConfig
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embed_tokens (nn.Embedding): output embedding
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"""
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def __init__(
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self,
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config: BartConfig,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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lora_config: LoRAConfig | None = None,
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embed_tokens: nn.Embedding | None = None,
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prefix: str = "",
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):
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super().__init__()
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self.cache_config = cache_config
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self.quant_config = quant_config
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self.lora_config = lora_config
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embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
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self.embed_tokens = BartScaledWordEmbedding(
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config.vocab_size, config.d_model, embed_scale=embed_scale
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)
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if embed_tokens is not None:
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self.embed_tokens.weight = embed_tokens.weight
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self.layers = nn.ModuleList(
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[
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MBartDecoderLayer(
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config,
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cache_config,
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quant_config,
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prefix=f"{prefix}.layers.{layer_idx}",
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)
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for layer_idx in range(config.decoder_layers)
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]
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)
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self.layernorm_embedding = nn.LayerNorm(config.d_model)
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self.layer_norm = nn.LayerNorm(config.d_model)
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def forward(
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self,
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decoder_input_ids: torch.Tensor,
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*,
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encoder_hidden_states: torch.Tensor | None,
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inputs_embeds: torch.Tensor | None = None,
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**kwargs,
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) -> torch.Tensor:
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r"""
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Args:
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decoder_input_ids: Indices of *decoder* input sequence tokens in the
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vocabulary. Padding will be ignored by default should you provide it.
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encoder_hidden_states: Tensor of encoder output embeddings
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Returns:
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Decoder output torch.Tensor
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"""
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(decoder_input_ids)
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hidden_states = self.layernorm_embedding(inputs_embeds)
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# decoder layers
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for decoder_layer in self.layers:
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hidden_states = decoder_layer(
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decoder_hidden_states=hidden_states,
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encoder_hidden_states=encoder_hidden_states,
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)
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hidden_states = self.layer_norm(hidden_states)
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return hidden_states
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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(".self_attn.qkv_proj", ".self_attn.q_proj", "q"),
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(".self_attn.qkv_proj", ".self_attn.k_proj", "k"),
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(".self_attn.qkv_proj", ".self_attn.v_proj", "v"),
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(".encoder_attn.kv_proj", ".encoder_attn.k_proj", "k"),
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(".encoder_attn.kv_proj", ".encoder_attn.v_proj", "v"),
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]
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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if name.startswith("embed_positions"):
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continue
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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class NemotronParsePixelInputs(TensorSchema):
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"""
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Dimensions:
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- b: Batch size
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- c: Number of channels (3)
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- h: Height
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- w: Width
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"""
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type: Literal["pixel_values"]
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data: Annotated[torch.Tensor, TensorShape("b", 3, "h", "w")]
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class NemotronParseImageProcessor:
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"""
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NemotronParse Image Processor
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"""
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def __init__(
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self,
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final_size: tuple = DEFAULT_FINAL_IMAGE_SIZE,
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**kwargs,
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):
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# Ensure final_size is properly formatted
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if isinstance(final_size, (list, tuple)) and len(final_size) >= 2:
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self.final_size = (int(final_size[0]), int(final_size[1]))
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elif isinstance(final_size, (int, float)):
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self.final_size = (int(final_size), int(final_size))
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else:
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self.final_size = DEFAULT_FINAL_IMAGE_SIZE # Default fallback
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self.norm_mean = torch.Tensor(OPENAI_CLIP_MEAN).reshape(1, 3, 1, 1)
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self.norm_std = torch.Tensor(OPENAI_CLIP_STD).reshape(1, 3, 1, 1)
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# Create transforms
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self._create_transforms()
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def _create_transforms(self):
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"""Create transform objects."""
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try:
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import albumentations as A
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except ImportError as err:
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raise ImportError(
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"The package `albumentations` is required to use "
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"NemotronParse model. Please install it with `pip install "
|
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"albumentations`."
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) from err
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# Ensure final_size is a tuple of integers
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if isinstance(self.final_size, (list, tuple)):
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self.target_height, self.target_width = (
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int(self.final_size[0]),
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int(self.final_size[1]),
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)
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else:
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self.target_height = self.target_width = int(self.final_size)
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self.transform = A.Compose(
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[
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A.PadIfNeeded(
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min_height=self.target_height,
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min_width=self.target_width,
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border_mode=cv2.BORDER_CONSTANT,
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fill=[255, 255, 255],
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p=1.0,
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),
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]
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)
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self.torch_transform = T.Compose(
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[
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T.ToTensor(),
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]
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)
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def _resize_with_aspect_ratio(self, image: np.ndarray) -> np.ndarray:
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"""Resize image maintaining aspect ratio (exact replica of original
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LongestMaxSizeHW)."""
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height, width = image.shape[:2]
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max_size_height = self.target_height
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max_size_width = self.target_width
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# Original LongestMaxSizeHW algorithm from custom_augmentations.py
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aspect_ratio = width / height
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new_height = height
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new_width = width
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|
||||
# If height too big then scale image down
|
||||
if height > max_size_height:
|
||||
new_height = max_size_height
|
||||
new_width = int(new_height * aspect_ratio)
|
||||
|
||||
# If width too big, scale image down further
|
||||
if new_width > max_size_width:
|
||||
new_width = max_size_width
|
||||
new_height = int(new_width / aspect_ratio)
|
||||
|
||||
# Use cv2.INTER_LINEAR like the original
|
||||
return cv2.resize(
|
||||
image, (new_width, new_height), interpolation=cv2.INTER_LINEAR
|
||||
)
|
||||
|
||||
def _pad_to_size(self, image: np.ndarray) -> np.ndarray:
|
||||
"""Pad image to target size with white padding (matches A.PadIfNeeded
|
||||
behavior)."""
|
||||
h, w = image.shape[:2]
|
||||
min_height, min_width = self.target_height, self.target_width
|
||||
|
||||
# Only pad if image is smaller than target (matches A.PadIfNeeded logic)
|
||||
pad_h = max(0, min_height - h)
|
||||
pad_w = max(0, min_width - w)
|
||||
|
||||
if pad_h == 0 and pad_w == 0:
|
||||
return image
|
||||
|
||||
# A.PadIfNeeded pads to bottom-right with constant value
|
||||
if len(image.shape) == 3:
|
||||
# Color image - pad bottom and right with white (255, 255, 255)
|
||||
padded = np.pad(
|
||||
image,
|
||||
((0, pad_h), (0, pad_w), (0, 0)),
|
||||
mode="constant",
|
||||
constant_values=255,
|
||||
)
|
||||
else:
|
||||
# Grayscale image - pad with white (255)
|
||||
padded = np.pad(
|
||||
image, ((0, pad_h), (0, pad_w)), mode="constant", constant_values=255
|
||||
)
|
||||
|
||||
return padded
|
||||
|
||||
def preprocess(
|
||||
self,
|
||||
images: Image.Image | list[Image.Image],
|
||||
**kwargs,
|
||||
) -> dict[str, torch.Tensor]:
|
||||
"""
|
||||
Preprocess an image or batch of images for the NemotronParse model.
|
||||
|
||||
Args:
|
||||
images: Input image(s)
|
||||
"""
|
||||
# Ensure images is a list
|
||||
if not isinstance(images, list):
|
||||
images = [images]
|
||||
|
||||
# Convert PIL images to numpy arrays if needed
|
||||
processed_images = []
|
||||
for image in images:
|
||||
if isinstance(image, Image.Image):
|
||||
image = np.asarray(image)
|
||||
processed_images.append(image)
|
||||
|
||||
# Apply NemotronParse-specific transforms
|
||||
pixel_values = []
|
||||
for image in processed_images:
|
||||
# Manual resize with aspect ratio preservation
|
||||
# (replaces LongestMaxSizeHW)
|
||||
processed_image = self._resize_with_aspect_ratio(image)
|
||||
|
||||
# Apply remaining albumentations transforms if available
|
||||
if self.transform is not None:
|
||||
transformed = self.transform(image=processed_image)
|
||||
processed_image = transformed["image"]
|
||||
else:
|
||||
# Fallback: just pad to target size
|
||||
processed_image = self._pad_to_size(processed_image)
|
||||
|
||||
# Convert to tensor
|
||||
pixel_values_tensor = self.torch_transform(processed_image)
|
||||
|
||||
# Handle grayscale images
|
||||
if pixel_values_tensor.shape[0] == 1:
|
||||
pixel_values_tensor = pixel_values_tensor.expand(3, -1, -1)
|
||||
|
||||
pixel_values.append(pixel_values_tensor)
|
||||
|
||||
# Stack into batch
|
||||
pixel_values = torch.stack(pixel_values)
|
||||
|
||||
# Normalize pixel values
|
||||
normalized_values = (pixel_values - self.norm_mean) / self.norm_std
|
||||
return {"pixel_values": normalized_values}
|
||||
|
||||
def __call__(
|
||||
self, images: Image.Image | list[Image.Image], **kwargs
|
||||
) -> dict[str, torch.Tensor]:
|
||||
return self.preprocess(images, **kwargs)
|
||||
|
||||
|
||||
class NemotronParseProcessor:
|
||||
"""
|
||||
NemotronParse Processor
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
tokenizer: AnyTokenizer,
|
||||
**kwargs,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.config = config
|
||||
self.tokenizer = tokenizer
|
||||
|
||||
self.image_processor = NemotronParseImageProcessor(final_size=config.image_size)
|
||||
|
||||
def _make_batch_input(self, input_item=None):
|
||||
if input_item is None:
|
||||
input_item = []
|
||||
if not isinstance(input_item, list):
|
||||
input_item = [input_item]
|
||||
return input_item
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
text: str | None = None,
|
||||
images: Image.Image | list[Image.Image] | None = None,
|
||||
return_tensors: str | TensorType | None = None,
|
||||
**kwargs,
|
||||
) -> BatchFeature:
|
||||
text, images = [self._make_batch_input(x) for x in (text, images)]
|
||||
image_inputs = {} if len(images) == 0 else self.image_processor(images)
|
||||
|
||||
text_inputs = self.tokenizer(text, add_special_tokens=False, **kwargs)
|
||||
combined_outputs = BatchFeature(
|
||||
data={**text_inputs, **image_inputs},
|
||||
tensor_type=return_tensors,
|
||||
)
|
||||
return combined_outputs
|
||||
|
||||
|
||||
class NemotronParseProcessingInfo(BaseProcessingInfo):
|
||||
def get_hf_config(self):
|
||||
return self.ctx.get_hf_config()
|
||||
|
||||
def get_hf_processor(self, **kwargs) -> NemotronParseProcessor:
|
||||
return self.ctx.init_processor(
|
||||
NemotronParseProcessor,
|
||||
config=self.get_hf_config(),
|
||||
tokenizer=self.get_tokenizer(),
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
def get_supported_mm_limits(self) -> Mapping[str, int | None]:
|
||||
return {"image": 1}
|
||||
|
||||
def get_num_image_tokens(self) -> int:
|
||||
config = self.get_hf_config()
|
||||
final_size = config.image_size
|
||||
patch_size = config.encoder.patch_size
|
||||
|
||||
return (final_size[0] // patch_size) * ((final_size[1] // patch_size) // 4) + 1
|
||||
|
||||
def get_mm_max_tokens_per_item(
|
||||
self,
|
||||
seq_len: int,
|
||||
mm_counts: Mapping[str, int],
|
||||
) -> Mapping[str, int] | None:
|
||||
image_tokens = self.get_num_image_tokens()
|
||||
return {"image": image_tokens}
|
||||
|
||||
|
||||
class NemotronParseDummyInputsBuilder(
|
||||
BaseDummyInputsBuilder[NemotronParseProcessingInfo]
|
||||
):
|
||||
def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
|
||||
return ""
|
||||
|
||||
def get_dummy_mm_data(
|
||||
self,
|
||||
seq_len: int,
|
||||
mm_counts: Mapping[str, int],
|
||||
mm_options: Mapping[str, BaseDummyOptions] | None = None,
|
||||
) -> MultiModalDataDict:
|
||||
num_images = mm_counts.get("image", 0)
|
||||
|
||||
target_width, target_height = self.info.get_hf_config().image_size
|
||||
|
||||
return {
|
||||
"image": self._get_dummy_images(
|
||||
width=target_width, height=target_height, num_images=num_images
|
||||
)
|
||||
}
|
||||
|
||||
|
||||
class NemotronParseMultiModalProcessor(
|
||||
EncDecMultiModalProcessor[NemotronParseProcessingInfo]
|
||||
):
|
||||
def create_encoder_prompt(
|
||||
self,
|
||||
prompt: str | list[int],
|
||||
mm_data: MultiModalDataDict,
|
||||
) -> str | list[int]:
|
||||
return [0]
|
||||
|
||||
@property
|
||||
def pad_dummy_encoder_prompt(self) -> bool:
|
||||
return True
|
||||
|
||||
def _call_hf_processor(
|
||||
self,
|
||||
prompt: str,
|
||||
mm_data: Mapping[str, object],
|
||||
mm_kwargs: Mapping[str, object],
|
||||
tok_kwargs: Mapping[str, object],
|
||||
) -> BatchFeature:
|
||||
if mm_data:
|
||||
processed_outputs = super()._call_hf_processor(
|
||||
prompt, mm_data, mm_kwargs, tok_kwargs
|
||||
)
|
||||
else:
|
||||
hf_processor = self.info.get_hf_processor()
|
||||
tokenizer = hf_processor.tokenizer
|
||||
processed_outputs = tokenizer(
|
||||
prompt, add_special_tokens=False, return_tensors="pt"
|
||||
)
|
||||
return processed_outputs
|
||||
|
||||
def _get_mm_fields_config(
|
||||
self,
|
||||
hf_inputs: BatchFeature,
|
||||
hf_processor_mm_kwargs: Mapping[str, object],
|
||||
) -> Mapping[str, MultiModalFieldConfig]:
|
||||
return dict(pixel_values=MultiModalFieldConfig.batched("image"))
|
||||
|
||||
def _get_prompt_updates(
|
||||
self,
|
||||
mm_items: MultiModalDataItems,
|
||||
hf_processor_mm_kwargs: Mapping[str, object],
|
||||
out_mm_kwargs: MultiModalKwargsItems,
|
||||
) -> Sequence[PromptUpdate]:
|
||||
num_image_tokens = self.info.get_num_image_tokens()
|
||||
|
||||
return [
|
||||
PromptReplacement(
|
||||
modality="image",
|
||||
target=[0],
|
||||
replacement=[0] * num_image_tokens,
|
||||
)
|
||||
]
|
||||
|
||||
|
||||
class RadioWithNeck(nn.Module):
|
||||
"""Vision encoder using RADIO model with custom neck."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
quant_config: QuantizationConfig | None = None,
|
||||
prefix: str = "",
|
||||
):
|
||||
super().__init__()
|
||||
self.config = config.encoder
|
||||
|
||||
self.model_encoder = self.get_vit_model_from_radio_config(
|
||||
config, quant_config=quant_config
|
||||
)
|
||||
|
||||
# Neck components
|
||||
last_hidden_state = 1024
|
||||
self.conv1 = nn.Conv1d(1280, last_hidden_state, 1)
|
||||
self.layer_norm1 = nn.LayerNorm(
|
||||
last_hidden_state, eps=1e-06, elementwise_affine=True
|
||||
)
|
||||
self.conv2 = nn.Conv2d(
|
||||
last_hidden_state,
|
||||
last_hidden_state,
|
||||
kernel_size=(1, 4),
|
||||
stride=(1, 4),
|
||||
padding=0,
|
||||
bias=False,
|
||||
)
|
||||
self.layer_norm2 = nn.LayerNorm(
|
||||
last_hidden_state, eps=1e-06, elementwise_affine=True
|
||||
)
|
||||
self.sum_proj = ColumnParallelLinear(
|
||||
3840,
|
||||
last_hidden_state,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.sum_proj",
|
||||
)
|
||||
self.layer_norm3 = nn.LayerNorm(
|
||||
last_hidden_state, eps=1e-06, elementwise_affine=True
|
||||
)
|
||||
|
||||
def get_vit_model_from_radio_config(
|
||||
self,
|
||||
hf_config: PretrainedConfig,
|
||||
quant_config: QuantizationConfig | None = None,
|
||||
) -> RadioModel:
|
||||
hf_config_vision = hf_config.encoder
|
||||
model_name = hf_config_vision.args.get("model")
|
||||
if model_name is None:
|
||||
raise ValueError(f"Unsupported vit model type: {model_name}")
|
||||
|
||||
radio_config = RadioConfig(
|
||||
model_name=model_name,
|
||||
image_size=hf_config.image_size,
|
||||
**hf_config_vision.args,
|
||||
)
|
||||
|
||||
return RadioModel(config=radio_config, quant_config=quant_config)
|
||||
|
||||
def forward(self, pixel_values: torch.Tensor, **kwargs) -> torch.Tensor:
|
||||
summary, feature = self.model_encoder(pixel_values)
|
||||
|
||||
output = self.conv1(feature.permute(0, 2, 1)).permute(0, 2, 1)
|
||||
output = self.layer_norm1(output)
|
||||
|
||||
patch_size = self.config.patch_size
|
||||
output = rearrange(
|
||||
output,
|
||||
"b (h w) d -> b d h w",
|
||||
h=pixel_values.shape[-2] // patch_size,
|
||||
w=pixel_values.shape[-1] // patch_size,
|
||||
)
|
||||
|
||||
output = self.conv2(output)
|
||||
output = rearrange(output, "b d h w -> b (h w) d")
|
||||
output = self.layer_norm2(output)
|
||||
summary = self.layer_norm3(self.sum_proj(summary)[0])
|
||||
output = torch.cat((output, summary.unsqueeze(1)), dim=1)
|
||||
|
||||
return output
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
|
||||
model_encoder_weights = []
|
||||
adaptor_dict = {
|
||||
name: param
|
||||
for name, param in dict(self.named_parameters()).items()
|
||||
if not name.startswith("model_encoder")
|
||||
}
|
||||
for name, w in weights:
|
||||
if name.startswith("model_encoder"):
|
||||
model_encoder_weights.append((".".join(name.split(".")[1:]), w))
|
||||
else:
|
||||
param = adaptor_dict[name]
|
||||
with torch.no_grad():
|
||||
default_weight_loader(param, w)
|
||||
|
||||
self.model_encoder.load_weights(model_encoder_weights)
|
||||
|
||||
|
||||
@MULTIMODAL_REGISTRY.register_processor(
|
||||
NemotronParseMultiModalProcessor,
|
||||
info=NemotronParseProcessingInfo,
|
||||
dummy_inputs=NemotronParseDummyInputsBuilder,
|
||||
)
|
||||
class NemotronParseForConditionalGeneration(nn.Module, SupportsMultiModal):
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
super().__init__()
|
||||
config = vllm_config.model_config.hf_config
|
||||
|
||||
self.config = config
|
||||
self.vision_config = config.encoder
|
||||
cache_config = vllm_config.cache_config
|
||||
quant_config = vllm_config.quant_config
|
||||
|
||||
self.encoder = RadioWithNeck(
|
||||
config=config, quant_config=quant_config, prefix=f"{prefix}.encoder"
|
||||
)
|
||||
|
||||
self.decoder = MBartDecoderNoPos(
|
||||
config.decoder,
|
||||
cache_config=cache_config,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.decoder",
|
||||
)
|
||||
|
||||
self.vocab_size = config.decoder.vocab_size
|
||||
self.lm_head = ParallelLMHead(
|
||||
config.decoder.vocab_size, config.decoder.d_model, quant_config=quant_config
|
||||
)
|
||||
self.logits_processor = LogitsProcessor(
|
||||
self.vocab_size, config.decoder.vocab_size
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_placeholder_str(cls, modality: str, i: int) -> str | None:
|
||||
if modality.startswith("image"):
|
||||
return None
|
||||
|
||||
raise ValueError("Only image modality is supported")
|
||||
|
||||
def _parse_and_validate_image_input(
|
||||
self, **kwargs: object
|
||||
) -> NemotronParsePixelInputs | None:
|
||||
pixel_values = kwargs.pop("pixel_values", None)
|
||||
image_embeds = kwargs.pop("image_embeds", None)
|
||||
|
||||
if pixel_values is None and image_embeds is None:
|
||||
return None
|
||||
|
||||
if pixel_values is not None and image_embeds is not None:
|
||||
raise ValueError("Both pixel values and image embeds are provided.")
|
||||
|
||||
if pixel_values is not None:
|
||||
h, w = self.config.image_size
|
||||
return NemotronParsePixelInputs(
|
||||
type="pixel_values",
|
||||
data=pixel_values,
|
||||
resolve_bindings={
|
||||
"h": h,
|
||||
"w": w,
|
||||
},
|
||||
)
|
||||
|
||||
if image_embeds is not None:
|
||||
raise NotImplementedError
|
||||
|
||||
raise AssertionError("This line should be unreachable.")
|
||||
|
||||
def _process_image_input(
|
||||
self, image_input: NemotronParsePixelInputs
|
||||
) -> torch.Tensor:
|
||||
assert image_input["type"] == "pixel_values"
|
||||
pixel_values = image_input["data"]
|
||||
dtype = next(self.encoder.parameters()).dtype
|
||||
pixel_values = pixel_values.to(dtype)
|
||||
return self.encoder(pixel_values)
|
||||
|
||||
def get_language_model(self) -> torch.nn.Module:
|
||||
return self.decoder
|
||||
|
||||
def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings | None:
|
||||
image_input = self._parse_and_validate_image_input(**kwargs)
|
||||
if image_input is None:
|
||||
return None
|
||||
vision_embeddings = self._process_image_input(image_input)
|
||||
return vision_embeddings
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
encoder_outputs: list[torch.Tensor] | None = None,
|
||||
**kwargs,
|
||||
) -> torch.Tensor:
|
||||
r"""
|
||||
Args:
|
||||
input_ids: torch.Tensor of *decoder* input token ids.
|
||||
positions: torch.Tensor of *decoder* position indices.
|
||||
encoder_outputs: List of encoder output tensors (vision embeddings).
|
||||
During profiling, this may be None or empty.
|
||||
Returns:
|
||||
Output torch.Tensor
|
||||
"""
|
||||
inputs_embeds = None
|
||||
if encoder_outputs:
|
||||
inputs_embeds = torch.cat(encoder_outputs, dim=0)
|
||||
hidden_states = self.decoder(
|
||||
decoder_input_ids=input_ids, encoder_hidden_states=inputs_embeds
|
||||
)
|
||||
return hidden_states
|
||||
|
||||
def compute_logits(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
) -> torch.Tensor | None:
|
||||
return self.logits_processor(self.lm_head, hidden_states)
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
|
||||
lm_head_dict = dict(self.lm_head.named_parameters())
|
||||
|
||||
def is_encoder(name: str) -> bool:
|
||||
return name.startswith("encoder")
|
||||
|
||||
def is_decoder(name: str) -> bool:
|
||||
return name.startswith("decoder")
|
||||
|
||||
def is_lm_head(name: str):
|
||||
return name.startswith("lm_head")
|
||||
|
||||
# Separate weights by component
|
||||
encoder_weights = []
|
||||
decoder_weights = []
|
||||
|
||||
for name, w in weights:
|
||||
if is_encoder(name):
|
||||
encoder_weights.append((".".join(name.split(".")[1:]), w))
|
||||
elif is_decoder(name):
|
||||
decoder_weights.append((".".join(name.split(".")[1:]), w))
|
||||
elif is_lm_head(name):
|
||||
trimmed_name = ".".join(name.split(".")[1:])
|
||||
param = lm_head_dict[trimmed_name]
|
||||
with torch.no_grad():
|
||||
default_weight_loader(param, w)
|
||||
else:
|
||||
logger.info("Found unexpected weight: %s", name)
|
||||
|
||||
# Load encoder weights
|
||||
self.encoder.load_weights(encoder_weights)
|
||||
# Load decoder weights
|
||||
self.decoder.load_weights(decoder_weights)
|
||||
@@ -427,15 +427,17 @@ class RadioInternVisionModel(nn.Module):
|
||||
to_2tuple(config.patch_size), config.image_size
|
||||
)
|
||||
max_img_size = int(
|
||||
round(config.max_img_size / config.patch_size) * config.patch_size
|
||||
round(config.cpe_max_size / config.patch_size) * config.patch_size
|
||||
)
|
||||
unique_teachers = set(t["name"] for t in config.teachers)
|
||||
self.patch_generator = ViTPatchGenerator(
|
||||
config.patch_size,
|
||||
config.hidden_size,
|
||||
input_dims=self.img_size,
|
||||
max_input_dims=max_img_size,
|
||||
cls_token=True,
|
||||
register_multiple=config.reg_tokens,
|
||||
num_cls_tokens=len(unique_teachers) if config.cls_token_per_teacher else 1,
|
||||
register_multiple=config.register_multiple,
|
||||
)
|
||||
|
||||
self.encoder = InternVisionEncoder(
|
||||
@@ -489,11 +491,20 @@ class RadioModel(nn.Module):
|
||||
prefix=prefix,
|
||||
)
|
||||
|
||||
summary_idxs = None
|
||||
if config.teachers:
|
||||
summary_idxs = torch.tensor(
|
||||
[i for i, t in enumerate(config.teachers) if t.get("use_summary", True)]
|
||||
)
|
||||
if summary_idxs.numel() > 0:
|
||||
self.register_buffer("summary_idxs", summary_idxs)
|
||||
self.summary_idxs = summary_idxs
|
||||
|
||||
def forward(
|
||||
self,
|
||||
pixel_values: torch.Tensor | None = None,
|
||||
pixel_embeds: torch.Tensor | None = None,
|
||||
) -> torch.FloatTensor:
|
||||
) -> tuple[torch.FloatTensor, torch.FloatTensor]:
|
||||
y = self.model(pixel_values)
|
||||
return self._extract_final(y)
|
||||
|
||||
@@ -546,10 +557,17 @@ class RadioModel(nn.Module):
|
||||
|
||||
return loaded_params
|
||||
|
||||
def _extract_final(self, y: torch.Tensor):
|
||||
def _extract_final(
|
||||
self, y: torch.Tensor
|
||||
) -> tuple[torch.FloatTensor, torch.FloatTensor]:
|
||||
# Remove CLS + REGISTERS tokens
|
||||
patch_gen = getattr(self.model, "patch_generator", None)
|
||||
if patch_gen is not None:
|
||||
all_summary = y[:, : patch_gen.num_cls_tokens]
|
||||
if self.summary_idxs is not None:
|
||||
bb_summary = all_summary[:, self.summary_idxs]
|
||||
else:
|
||||
bb_summary = all_summary
|
||||
all_feat = y[:, patch_gen.num_skip :]
|
||||
|
||||
return all_feat
|
||||
return bb_summary.flatten(1), all_feat
|
||||
|
||||
@@ -428,6 +428,10 @@ _MULTIMODAL_MODELS = {
|
||||
"VoxtralForConditionalGeneration": ("voxtral", "VoxtralForConditionalGeneration"), # noqa: E501
|
||||
"VoxtralStreamingGeneration": ("voxtral_streaming", "VoxtralStreamingGeneration"), # noqa: E501
|
||||
# [Encoder-decoder]
|
||||
"NemotronParseForConditionalGeneration": (
|
||||
"nemotron_parse",
|
||||
"NemotronParseForConditionalGeneration",
|
||||
),
|
||||
"WhisperForConditionalGeneration": ("whisper", "WhisperForConditionalGeneration"), # noqa: E501
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user