mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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114 lines
4.5 KiB
Python
114 lines
4.5 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 math
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from typing import Optional
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import paddle
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import paddle.nn as nn
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from fastdeploy.model_executor.utils import h2d_copy
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class GELUActivation(nn.Layer):
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"""
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Original Implementation of the GELU activation function in Google BERT repo when initially created. For
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information: OpenAI GPT's GELU is slightly different (and gives slightly different results): 0.5 * x * (1 +
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torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) This is now written in C in nn.functional
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Also see the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415
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"""
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def __init__(self, use_gelu_python: bool = False):
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super().__init__()
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if use_gelu_python:
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self.act = self._gelu_python
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else:
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self.act = nn.functional.gelu
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def _gelu_python(self, input):
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return input * 0.5 * (1.0 + paddle.erf(input / math.sqrt(2.0)))
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def forward(self, input):
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return self.act(input)
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class Projector(nn.Layer):
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def __init__(self, text_config, vision_config, prefix=""):
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super().__init__()
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self.prefix_name = prefix
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self.text_config = text_config
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self.vision_config = vision_config
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self.merge_kernel_size = (2, 2)
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self.hidden_size = self.vision_config.hidden_size * self.merge_kernel_size[0] * self.merge_kernel_size[1]
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self.pre_norm = nn.LayerNorm(self.vision_config.hidden_size, epsilon=1e-05)
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self.linear_1 = nn.Linear(self.hidden_size, self.hidden_size)
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self.linear_1.weight.weight_loader = self.weight_loader
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self.act = GELUActivation()
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self.linear_2 = nn.Linear(self.hidden_size, self.text_config.hidden_size)
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self.linear_2.weight.weight_loader = self.weight_loader
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def forward(self, image_features, image_grid_thw):
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m1, m2 = self.merge_kernel_size
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if isinstance(image_features, (list, tuple)):
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processed_features = list()
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for image_feature, image_grid in zip(image_features, image_grid_thw):
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image_feature = self.pre_norm(image_feature) # shape: (T*H*W, D)
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t, h, w = image_grid
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from einops import rearrange
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image_feature = rearrange(
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image_feature,
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"(t h p1 w p2) d -> (t h w) (p1 p2 d)",
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t=int(t),
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h=int(h // m1),
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p1=int(m1),
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w=int(w // m2),
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p2=int(m2),
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)
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hidden_states = self.linear_1(image_feature)
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hidden_states = self.act(hidden_states)
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hidden_states = self.linear_2(hidden_states)
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processed_features.append(hidden_states)
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return processed_features
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dim = image_features.shape[-1]
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image_features = paddle.reshape(image_features, [-1, dim])
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hidden_states = self.pre_norm(image_features)
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hidden_states = paddle.reshape(hidden_states, [-1, self.hidden_size])
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hidden_states = self.linear_1(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states = self.linear_2(hidden_states)
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return hidden_states
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def weight_loader(self, param, loaded_weight, loaded_shard_id: Optional[str] = None):
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loaded_weight = loaded_weight.transpose([1, 0])
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if not param._is_initialized():
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param.initialize()
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assert param.shape == loaded_weight.shape, (
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f" Attempted to load weight ({loaded_weight.shape}) " f"into parameter ({param.shape})"
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)
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# Ensure loaded weight dtype matches model param dtype
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if loaded_weight.dtype != param.dtype:
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if loaded_weight.dtype == paddle.int8 and param.dtype == paddle.float8_e4m3fn:
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loaded_weight = loaded_weight.view(param.dtype)
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else:
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loaded_weight = loaded_weight.cast(param.dtype)
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h2d_copy(param, loaded_weight)
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