mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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267 lines
9.9 KiB
Python
267 lines
9.9 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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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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import re
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from typing import Dict, Optional, Union
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import numpy as np
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import paddle
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import paddle.nn as nn
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from fastdeploy.config import FDConfig
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from fastdeploy.model_executor.forward_meta import ForwardMeta
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from fastdeploy.model_executor.graph_optimization.decorator import (
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support_graph_optimization,
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)
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from fastdeploy.model_executor.layers.attention.attention import Attention
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from fastdeploy.model_executor.layers.embeddings import VocabParallelEmbedding
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from fastdeploy.model_executor.layers.lm_head import ParallelLMHead
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from fastdeploy.model_executor.layers.normalization import RMSNorm
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from fastdeploy.model_executor.models.ernie4_5_moe import Ernie4_5_DecoderLayer
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from fastdeploy.model_executor.models.model_base import (
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ModelCategory,
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ModelForCasualLM,
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ModelRegistry,
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)
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from fastdeploy.model_executor.utils import (
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default_weight_loader,
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process_weights_after_loading,
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)
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from fastdeploy.platforms import current_platform
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from .projector import Projector
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from .siglip import SiglipVisionModel
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@support_graph_optimization
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class PaddleOCRVLModel(nn.Layer):
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def __init__(
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self,
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fd_config: FDConfig = None,
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):
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super().__init__()
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self.config = fd_config.model_config
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self.num_layers = fd_config.model_config.num_hidden_layers
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fd_config.model_config.pretrained_config.prefix_name = "model"
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self._dtype = fd_config.model_config.torch_dtype
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self.embed_tokens = VocabParallelEmbedding(
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fd_config=fd_config,
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num_embeddings=fd_config.model_config.vocab_size,
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embedding_dim=fd_config.model_config.hidden_size,
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params_dtype=self._dtype,
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prefix=(f"{fd_config.model_config.pretrained_config.prefix_name}.embed_tokens"),
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)
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self.layers = nn.LayerList(
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[
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Ernie4_5_DecoderLayer(
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fd_config=fd_config,
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prefix=f"{fd_config.model_config.pretrained_config.prefix_name}.layers.{i}",
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)
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for i in range(self.num_layers)
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]
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)
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for i, layer in enumerate(self.layers):
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layer.self_attn.attn = Attention(
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fd_config=fd_config,
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layer_id=i,
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prefix=f"{fd_config.model_config.pretrained_config.prefix_name}.layers.{i}.self_attn",
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use_neox_rotary_style=True,
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)
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self.norm = RMSNorm(
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fd_config,
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hidden_size=fd_config.model_config.hidden_size,
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eps=fd_config.model_config.rms_norm_eps,
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prefix=f"{fd_config.model_config.pretrained_config.prefix_name}.norm",
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)
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def get_input_embeddings(
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self, ids_remove_padding: paddle.Tensor, forward_meta: ForwardMeta = None
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) -> paddle.Tensor:
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return self.embed_tokens(ids_remove_padding=ids_remove_padding, forward_meta=forward_meta)
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def forward(
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self,
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input_embeddings: paddle.Tensor,
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forward_meta: ForwardMeta,
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):
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hidden_states = input_embeddings
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if current_platform.is_iluvatar() and forward_meta.attn_backend.mixed:
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hidden_states = forward_meta.attn_backend.transpose(hidden_states)
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residual = None
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for i in range(self.num_layers):
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hidden_states, residual = self.layers[i](forward_meta, hidden_states, residual)
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out = self.norm(hidden_states, residual)[0]
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if current_platform.is_iluvatar() and forward_meta.attn_backend.mixed:
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out = forward_meta.attn_backend.reverse_transpose(out)
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return out
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@ModelRegistry.register_model_class(
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architecture="PaddleOCRVLForConditionalGeneration",
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module_name="paddleocr_vl.paddleocr_vl",
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category=ModelCategory.MULTIMODAL,
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primary_use=ModelCategory.MULTIMODAL,
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)
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class PaddleOCRVLForConditionalGeneration(ModelForCasualLM):
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def __init__(self, fd_config):
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super().__init__(fd_config)
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config = fd_config.model_config
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self.config = config
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self.mlp_AR = Projector(config, config.vision_config, prefix="mlp_AR")
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self.visual = SiglipVisionModel(config.vision_config, prefix="visual")
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self.model = PaddleOCRVLModel(fd_config)
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self.vocab_size = config.vocab_size
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self.lm_head = ParallelLMHead(
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fd_config=fd_config,
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embedding_dim=fd_config.model_config.hidden_size,
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num_embeddings=fd_config.model_config.vocab_size,
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prefix="lm_head",
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)
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# Persistent buffers for CUDA graphs.
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if fd_config.graph_opt_config.use_cudagraph:
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self._decoder_input_embeddings = paddle.zeros(
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[fd_config.graph_opt_config.max_capture_size, fd_config.model_config.hidden_size],
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dtype=fd_config.model_config.dtype,
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)
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@paddle.no_grad()
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def load_weights(self, weights_iterator) -> None:
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"""
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Load model parameters from a given weights_iterator object.
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Args:
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weights_iterator (Iterator): An iterator yielding (name, weight) pairs.
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"""
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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("up_gate_proj", "gate_proj", "gate"),
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("up_gate_proj", "up_proj", "up"),
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("embed_tokens.embeddings", "embed_tokens", None),
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("lm_head.linear", "lm_head", None),
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]
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params_dict = dict(self.named_parameters())
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process_weights_after_loading_fn = process_weights_after_loading(dict(self.named_sublayers()), self.fd_config)
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for loaded_weight_name, loaded_weight in weights_iterator:
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loaded_weight_name = (
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self.process_weights_before_loading_fn(loaded_weight_name)
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if getattr(self, "process_weights_before_loading_fn", None)
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else loaded_weight_name
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)
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if loaded_weight_name is None:
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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 loaded_weight_name:
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continue
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model_param_name = loaded_weight_name.replace(weight_name, param_name)
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if model_param_name not in params_dict:
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continue
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param = params_dict[model_param_name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader(self.fd_config))
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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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model_param_name = loaded_weight_name
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if model_param_name not in params_dict:
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continue
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param = params_dict[model_param_name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader(self.fd_config))
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weight_loader(param, loaded_weight)
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model_sublayer_name = re.sub(r"\.(weight)$", "", model_param_name)
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process_weights_after_loading_fn(model_sublayer_name, param)
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@paddle.no_grad()
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def set_state_dict(self, state_dict: Dict[str, Union[np.ndarray, paddle.Tensor]]):
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"""
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Load model parameters from a given state dictionary.
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Args:
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state_dict (dict[str, np.ndarray | paddle.Tensor]):
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A dictionary containing model parameters, where keys are parameter names
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and values are NumPy arrays or PaddlePaddle tensors.
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"""
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self.model.load_state_dict(state_dict)
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self.visual.load_state_dict(state_dict)
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self.projector.load_state_dict(state_dict)
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self.lm_head.load_state_dict(state_dict)
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@property
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def projector(self):
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return self.mlp_AR
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@classmethod
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def name(self):
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return "PaddleOCRVLForConditionalGeneration"
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def compute_logits(self, hidden_states: paddle.Tensor):
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logits = self.lm_head(hidden_states)
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logits = paddle.cast(logits, paddle.float32)
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logits[:, self.vocab_size :] = -float("inf")
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return logits
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def get_input_embeddings(
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self,
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ids_remove_padding: paddle.Tensor,
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image_features: Optional[paddle.Tensor] = None,
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forward_meta=None,
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) -> paddle.Tensor:
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input_embeddings = self.model.get_input_embeddings(
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ids_remove_padding=ids_remove_padding, forward_meta=forward_meta
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)
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image_mask = ids_remove_padding == self.model.config.image_token_id
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image_token_num = image_mask.sum()
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if image_token_num > 0:
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input_embeddings[image_mask] = image_features.cast(self._dtype)
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return input_embeddings
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def forward(
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self,
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ids_remove_padding: paddle.Tensor,
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image_features: Optional[paddle.Tensor],
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forward_meta: ForwardMeta,
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):
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input_embeddings = self.get_input_embeddings(
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ids_remove_padding=ids_remove_padding, image_features=image_features, forward_meta=forward_meta
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)
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if forward_meta.step_use_cudagraph:
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self._decoder_input_embeddings.copy_(input_embeddings, False)
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input_embeddings = self._decoder_input_embeddings
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hidden_states = self.model(
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input_embeddings=input_embeddings,
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forward_meta=forward_meta,
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)
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return hidden_states
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