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3e3357fd77
* init * add readme * update readme * no use make * update readme * update fix code * fix editorconfig-checker * no change convert py * use clip_image_u8_free
816 lines
32 KiB
Python
816 lines
32 KiB
Python
# coding=utf-8
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# Copyright 2024 Google AI and The HuggingFace Team. 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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""" PyTorch Siglip model. """
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# Copied from HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit and add tgt_sizes
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import os
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import math
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import warnings
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import numpy as np
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn.init import _calculate_fan_in_and_fan_out
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from transformers.activations import ACT2FN
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from transformers.modeling_utils import PreTrainedModel
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import (
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logging,
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)
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class SiglipVisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`SiglipVisionModel`]. It is used to instantiate a
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Siglip vision encoder according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the vision encoder of the Siglip
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[google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_channels (`int`, *optional*, defaults to 3):
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Number of channels in the input images.
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image_size (`int`, *optional*, defaults to 224):
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The size (resolution) of each image.
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patch_size (`int`, *optional*, defaults to 16):
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The size (resolution) of each patch.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
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layer_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the layer normalization layers.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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Example:
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```python
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>>> from transformers import SiglipVisionConfig, SiglipVisionModel
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>>> # Initializing a SiglipVisionConfig with google/siglip-base-patch16-224 style configuration
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>>> configuration = SiglipVisionConfig()
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>>> # Initializing a SiglipVisionModel (with random weights) from the google/siglip-base-patch16-224 style configuration
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>>> model = SiglipVisionModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "siglip_vision_model"
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def __init__(
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self,
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hidden_size=768,
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intermediate_size=3072,
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num_hidden_layers=12,
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num_attention_heads=12,
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num_channels=3,
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image_size=224,
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patch_size=16,
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hidden_act="gelu_pytorch_tanh",
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layer_norm_eps=1e-6,
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attention_dropout=0.0,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_channels = num_channels
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self.patch_size = patch_size
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self.image_size = image_size
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self.attention_dropout = attention_dropout
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self.layer_norm_eps = layer_norm_eps
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self.hidden_act = hidden_act
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_CHECKPOINT_FOR_DOC = "google/siglip-base-patch16-224"
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SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [
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"google/siglip-base-patch16-224",
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# See all SigLIP models at https://huggingface.co/models?filter=siglip
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]
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# Copied from transformers.models.llama.modeling_llama._get_unpad_data
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def _get_unpad_data(attention_mask):
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seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
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indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
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max_seqlen_in_batch = seqlens_in_batch.max().item()
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cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
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return (
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indices,
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cu_seqlens,
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max_seqlen_in_batch,
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)
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def _trunc_normal_(tensor, mean, std, a, b):
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# Cut & paste from PyTorch official master until it's in a few official releases - RW
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# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
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def norm_cdf(x):
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# Computes standard normal cumulative distribution function
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return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
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if (mean < a - 2 * std) or (mean > b + 2 * std):
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warnings.warn(
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"mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
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"The distribution of values may be incorrect.",
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stacklevel=2,
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)
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# Values are generated by using a truncated uniform distribution and
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# then using the inverse CDF for the normal distribution.
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# Get upper and lower cdf values
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l = norm_cdf((a - mean) / std)
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u = norm_cdf((b - mean) / std)
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# Uniformly fill tensor with values from [l, u], then translate to
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# [2l-1, 2u-1].
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tensor.uniform_(2 * l - 1, 2 * u - 1)
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# Use inverse cdf transform for normal distribution to get truncated
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# standard normal
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if tensor.dtype in [torch.float16, torch.bfloat16]:
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# The `erfinv_` op is not (yet?) defined in float16+cpu, bfloat16+gpu
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og_dtype = tensor.dtype
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tensor = tensor.to(torch.float32)
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tensor.erfinv_()
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tensor = tensor.to(og_dtype)
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else:
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tensor.erfinv_()
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# Transform to proper mean, std
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tensor.mul_(std * math.sqrt(2.0))
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tensor.add_(mean)
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# Clamp to ensure it's in the proper range
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if tensor.dtype == torch.float16:
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# The `clamp_` op is not (yet?) defined in float16+cpu
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tensor = tensor.to(torch.float32)
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tensor.clamp_(min=a, max=b)
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tensor = tensor.to(torch.float16)
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else:
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tensor.clamp_(min=a, max=b)
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def trunc_normal_tf_(
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tensor: torch.Tensor, mean: float = 0.0, std: float = 1.0, a: float = -2.0, b: float = 2.0
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):
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"""Fills the input Tensor with values drawn from a truncated
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normal distribution. The values are effectively drawn from the
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normal distribution :math:`\\mathcal{N}(\text{mean}, \text{std}^2)`
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with values outside :math:`[a, b]` redrawn until they are within
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the bounds. The method used for generating the random values works
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best when :math:`a \\leq \text{mean} \\leq b`.
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NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
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bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
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and the result is subsquently scaled and shifted by the mean and std args.
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Args:
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tensor: an n-dimensional `torch.Tensor`
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mean: the mean of the normal distribution
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std: the standard deviation of the normal distribution
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a: the minimum cutoff value
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b: the maximum cutoff value
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"""
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with torch.no_grad():
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_trunc_normal_(tensor, 0, 1.0, a, b)
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tensor.mul_(std).add_(mean)
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def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"):
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fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
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denom = fan_in
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if mode == "fan_in":
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denom = fan_in
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elif mode == "fan_out":
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denom = fan_out
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elif mode == "fan_avg":
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denom = (fan_in + fan_out) / 2
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variance = scale / denom
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if distribution == "truncated_normal":
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# constant is stddev of standard normal truncated to (-2, 2)
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trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
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elif distribution == "normal":
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with torch.no_grad():
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tensor.normal_(std=math.sqrt(variance))
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elif distribution == "uniform":
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bound = math.sqrt(3 * variance)
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with torch.no_grad():
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tensor.uniform_(-bound, bound)
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else:
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raise ValueError(f"invalid distribution {distribution}")
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def lecun_normal_(tensor):
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variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal")
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def default_flax_embed_init(tensor):
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variance_scaling_(tensor, mode="fan_in", distribution="normal")
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class SiglipVisionEmbeddings(nn.Module):
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def __init__(self, config: SiglipVisionConfig):
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super().__init__()
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self.config = config
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self.embed_dim = config.hidden_size
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self.image_size = config.image_size
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self.patch_size = config.patch_size
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self.patch_embedding = nn.Conv2d(
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in_channels=config.num_channels,
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out_channels=self.embed_dim,
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kernel_size=self.patch_size,
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stride=self.patch_size,
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padding="valid",
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)
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self.num_patches_per_side = self.image_size // self.patch_size
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self.num_patches = self.num_patches_per_side**2
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self.num_positions = self.num_patches
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self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
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class SiglipAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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# Copied from transformers.models.clip.modeling_clip.CLIPAttention.__init__
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.embed_dim = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = self.embed_dim // self.num_heads
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if self.head_dim * self.num_heads != self.embed_dim:
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raise ValueError(
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f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
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f" {self.num_heads})."
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)
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self.scale = self.head_dim**-0.5
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self.dropout = config.attention_dropout
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self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
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self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
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self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
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self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
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# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Siglip
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class SiglipMLP(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.activation_fn = ACT2FN[config.hidden_act]
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self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
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self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
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# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->Siglip
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class SiglipEncoderLayer(nn.Module):
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def __init__(self, config: SiglipVisionConfig):
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super().__init__()
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self.embed_dim = config.hidden_size
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self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
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self.self_attn = (
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SiglipAttention(config)
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)
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self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
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self.mlp = SiglipMLP(config)
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self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
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class SiglipPreTrainedModel(PreTrainedModel):
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"""
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An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
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models.
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"""
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config_class = SiglipVisionConfig
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base_model_prefix = "siglip"
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supports_gradient_checkpointing = True
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def _init_weights(self, module):
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"""Initialize the weights"""
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if isinstance(module, SiglipVisionEmbeddings):
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width = self.config.hidden_size
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nn.init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width))
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elif isinstance(module, nn.Embedding):
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default_flax_embed_init(module.weight)
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elif isinstance(module, SiglipAttention):
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nn.init.normal_(module.q_proj.weight)
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nn.init.normal_(module.k_proj.weight)
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nn.init.normal_(module.v_proj.weight)
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nn.init.normal_(module.out_proj.weight)
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nn.init.zeros_(module.q_proj.bias)
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nn.init.zeros_(module.k_proj.bias)
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nn.init.zeros_(module.v_proj.bias)
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nn.init.zeros_(module.out_proj.bias)
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elif isinstance(module, SiglipMLP):
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nn.init.normal_(module.fc1.weight)
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nn.init.normal_(module.fc2.weight)
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nn.init.normal_(module.fc1.bias, std=1e-6)
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nn.init.normal_(module.fc2.bias, std=1e-6)
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elif isinstance(module, (nn.Linear, nn.Conv2d)):
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lecun_normal_(module.weight)
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if module.bias is not None:
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nn.init.zeros_(module.bias)
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elif isinstance(module, nn.LayerNorm):
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module.bias.data.zero_()
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module.weight.data.fill_(1.0)
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SIGLIP_START_DOCSTRING = r"""
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This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
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etc.)
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This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
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Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
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and behavior.
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Parameters:
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config ([`SiglipVisionConfig`]): Model configuration class with all the parameters of the model.
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Initializing with a config file does not load the weights associated with the model, only the
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configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
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"""
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SIGLIP_VISION_INPUTS_DOCSTRING = r"""
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Args:
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pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
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Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
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[`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
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output_attentions (`bool`, *optional*):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
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tensors for more detail.
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output_hidden_states (`bool`, *optional*):
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Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
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more detail.
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return_dict (`bool`, *optional*):
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Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
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"""
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# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->Siglip
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class SiglipEncoder(nn.Module):
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"""
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Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
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[`SiglipEncoderLayer`].
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Args:
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config: SiglipConfig
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"""
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def __init__(self, config: SiglipVisionConfig):
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super().__init__()
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self.config = config
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self.layers = nn.ModuleList([SiglipEncoderLayer(config) for _ in range(config.num_hidden_layers)])
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self.gradient_checkpointing = False
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class SiglipVisionTransformer(SiglipPreTrainedModel):
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config_class = SiglipVisionConfig
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main_input_name = "pixel_values"
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_supports_flash_attn_2 = True
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def __init__(self, config: SiglipVisionConfig):
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super().__init__(config)
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self.config = config
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embed_dim = config.hidden_size
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self.embeddings = SiglipVisionEmbeddings(config)
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self.encoder = SiglipEncoder(config)
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self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
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self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
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# Initialize weights and apply final processing
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self.post_init()
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def get_input_embeddings(self) -> nn.Module:
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return self.embeddings.patch_embedding
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import argparse
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import json
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import re
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import numpy as np
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from gguf import *
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from transformers.models.idefics2.modeling_idefics2 import Idefics2VisionTransformer, Idefics2VisionConfig
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TEXT = "clip.text"
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VISION = "clip.vision"
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def add_key_str(raw_key: str, arch: str) -> str:
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return raw_key.format(arch=arch)
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def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_minicpmv: bool) -> bool:
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if name in (
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"logit_scale",
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"text_model.embeddings.position_ids",
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"vision_model.embeddings.position_ids",
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):
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return True
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if has_minicpmv and name in ["visual_projection.weight"]:
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return True
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if name.startswith("v") and not has_vision:
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return True
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if name.startswith("t") and not has_text:
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return True
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return False
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def get_tensor_name(name: str) -> str:
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if "projection" in name:
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return name
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if "mm_projector" in name:
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name = name.replace("model.mm_projector", "mm")
|
|
name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1)
|
|
name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1)
|
|
return name
|
|
|
|
return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln")
|
|
|
|
|
|
def bytes_to_unicode():
|
|
"""
|
|
Returns list of utf-8 byte and a corresponding list of unicode strings.
|
|
The reversible bpe codes work on unicode strings.
|
|
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
|
|
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
|
|
This is a significant percentage of your normal, say, 32K bpe vocab.
|
|
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
|
|
And avoids mapping to whitespace/control characters the bpe code barfs on.
|
|
"""
|
|
bs = (
|
|
list(range(ord("!"), ord("~") + 1))
|
|
+ list(range(ord("¡"), ord("¬") + 1))
|
|
+ list(range(ord("®"), ord("ÿ") + 1))
|
|
)
|
|
cs = bs[:]
|
|
n = 0
|
|
for b in range(2**8):
|
|
if b not in bs:
|
|
bs.append(b)
|
|
cs.append(2**8 + n)
|
|
n += 1
|
|
cs = [chr(n) for n in cs]
|
|
return dict(zip(bs, cs))
|
|
|
|
|
|
ap = argparse.ArgumentParser()
|
|
ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True)
|
|
ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16")
|
|
ap.add_argument("--text-only", action="store_true", required=False,
|
|
help="Save a text-only model. It can't be used to encode images")
|
|
ap.add_argument("--vision-only", action="store_true", required=False,
|
|
help="Save a vision-only model. It can't be used to encode texts")
|
|
ap.add_argument("--clip-model-is-vision", action="store_true", required=False,
|
|
help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
|
|
ap.add_argument("--clip-model-is-openclip", action="store_true", required=False,
|
|
help="The clip model is from openclip (for ViT-SO400M type))")
|
|
ap.add_argument("--minicpmv-projector", help="Path to minicpmv.projector file. If specified, save an image encoder for MiniCPM-V models.")
|
|
ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2"], default="mlp")
|
|
ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
|
|
# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
|
|
# Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5
|
|
default_image_mean = [0.48145466, 0.4578275, 0.40821073]
|
|
default_image_std = [0.26862954, 0.26130258, 0.27577711]
|
|
ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
|
|
ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
|
|
ap.add_argument('--minicpmv_version', type=int, help='minicpmv_version: MiniCPM-V-2 use 1; MiniCPM-V-2.5 use 2; MiniCPM-V-2.6 use 3; MiniCPM-o-2.6 use 4', default=2)
|
|
|
|
# with proper
|
|
args = ap.parse_args()
|
|
|
|
|
|
if args.text_only and args.vision_only:
|
|
print("--text-only and --image-only arguments cannot be specified at the same time.")
|
|
exit(1)
|
|
|
|
if args.use_f32:
|
|
print("WARNING: Weights for the convolution op is always saved in f16, as the convolution op in GGML does not support 32-bit kernel weights yet.")
|
|
|
|
# output in the same directory as the model if output_dir is None
|
|
dir_model = args.model_dir
|
|
|
|
if args.clip_model_is_vision or not os.path.exists(dir_model + "/vocab.json") or args.clip_model_is_openclip:
|
|
vocab = None
|
|
tokens = None
|
|
else:
|
|
with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
|
|
vocab = json.load(f)
|
|
tokens = [key for key in vocab]
|
|
|
|
# possible data types
|
|
# ftype == 0 -> float32
|
|
# ftype == 1 -> float16
|
|
#
|
|
# map from ftype to string
|
|
ftype_str = ["f32", "f16"]
|
|
|
|
ftype = 1
|
|
if args.use_f32:
|
|
ftype = 0
|
|
|
|
# if args.clip_model_is_vision or args.clip_model_is_openclip:
|
|
# model = CLIPVisionModel.from_pretrained(dir_model)
|
|
# processor = None
|
|
# else:
|
|
# model = CLIPModel.from_pretrained(dir_model)
|
|
# processor = CLIPProcessor.from_pretrained(dir_model)
|
|
|
|
minicpmv_version = args.minicpmv_version
|
|
emb_dim = 4096
|
|
block_count = 26
|
|
if minicpmv_version == 1:
|
|
emb_dim = 2304
|
|
block_count = 26
|
|
elif minicpmv_version == 2:
|
|
emb_dim = 4096
|
|
block_count = 27
|
|
elif minicpmv_version == 3:
|
|
emb_dim = 3584
|
|
block_count = 27
|
|
elif minicpmv_version == 4:
|
|
emb_dim = 3584
|
|
block_count = 27
|
|
|
|
default_vision_config = {
|
|
"hidden_size": 1152,
|
|
"image_size": 980,
|
|
"intermediate_size": 4304,
|
|
"model_type": "idefics2",
|
|
"num_attention_heads": 16,
|
|
"num_hidden_layers": 27,
|
|
"patch_size": 14,
|
|
}
|
|
|
|
vision_config = Idefics2VisionConfig(**default_vision_config)
|
|
model = Idefics2VisionTransformer(vision_config)
|
|
if minicpmv_version == 3:
|
|
vision_config = SiglipVisionConfig(**default_vision_config)
|
|
model = SiglipVisionTransformer(vision_config)
|
|
elif minicpmv_version == 4:
|
|
vision_config = SiglipVisionConfig(**default_vision_config)
|
|
model = SiglipVisionTransformer(vision_config)
|
|
|
|
processor = None
|
|
# if model.attn_pool is not None:
|
|
# model.attn_pool = torch.nn.Identity()
|
|
|
|
# model.blocks = model.blocks[:-1]
|
|
model.load_state_dict(torch.load(os.path.join(dir_model, "minicpmv.clip")))
|
|
|
|
fname_middle = None
|
|
has_text_encoder = True
|
|
has_vision_encoder = True
|
|
has_minicpmv_projector = False
|
|
|
|
if args.text_only:
|
|
fname_middle = "text-"
|
|
has_vision_encoder = False
|
|
elif args.minicpmv_projector is not None:
|
|
fname_middle = "mmproj-"
|
|
has_text_encoder = False
|
|
has_minicpmv_projector = True
|
|
minicpmv_version = 4
|
|
elif args.vision_only:
|
|
fname_middle = "vision-"
|
|
has_text_encoder = False
|
|
else:
|
|
fname_middle = ""
|
|
|
|
output_dir = args.output_dir if args.output_dir is not None else dir_model
|
|
os.makedirs(output_dir, exist_ok=True)
|
|
output_prefix = os.path.basename(output_dir).replace("ggml_", "")
|
|
fname_out = os.path.join(output_dir, f"{fname_middle}model-{ftype_str[ftype]}.gguf")
|
|
fout = GGUFWriter(path=fname_out, arch="clip")
|
|
|
|
fout.add_bool("clip.has_text_encoder", has_text_encoder)
|
|
fout.add_bool("clip.has_vision_encoder", has_vision_encoder)
|
|
fout.add_bool("clip.has_minicpmv_projector", has_minicpmv_projector)
|
|
fout.add_file_type(ftype)
|
|
if args.text_only:
|
|
fout.add_description("text-only CLIP model")
|
|
elif args.vision_only and not has_minicpmv_projector:
|
|
fout.add_description("vision-only CLIP model")
|
|
elif has_minicpmv_projector:
|
|
fout.add_description("image encoder for MiniCPM-V")
|
|
# add projector type
|
|
fout.add_string("clip.projector_type", "resampler")
|
|
fout.add_int32("clip.minicpmv_version", minicpmv_version)
|
|
else:
|
|
fout.add_description("two-tower CLIP model")
|
|
|
|
if has_vision_encoder:
|
|
# vision_model hparams
|
|
fout.add_uint32("clip.vision.image_size", 448)
|
|
fout.add_uint32("clip.vision.patch_size", 14)
|
|
fout.add_uint32(add_key_str(KEY_EMBEDDING_LENGTH, VISION), 1152)
|
|
fout.add_uint32(add_key_str(KEY_FEED_FORWARD_LENGTH, VISION), 4304)
|
|
fout.add_uint32("clip.vision.projection_dim", 0)
|
|
fout.add_uint32(add_key_str(KEY_ATTENTION_HEAD_COUNT, VISION), 16)
|
|
fout.add_float32(add_key_str(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
|
|
fout.add_uint32(add_key_str(KEY_BLOCK_COUNT, VISION), block_count)
|
|
|
|
if processor is not None:
|
|
image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean
|
|
image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std
|
|
else:
|
|
image_mean = args.image_mean if args.image_mean is not None else default_image_mean
|
|
image_std = args.image_std if args.image_std is not None else default_image_std
|
|
fout.add_array("clip.vision.image_mean", image_mean)
|
|
fout.add_array("clip.vision.image_std", image_std)
|
|
|
|
use_gelu = True
|
|
fout.add_bool("clip.use_gelu", use_gelu)
|
|
|
|
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
|
|
"""
|
|
embed_dim: output dimension for each position
|
|
pos: a list of positions to be encoded: size (M,)
|
|
out: (M, D)
|
|
"""
|
|
assert embed_dim % 2 == 0
|
|
omega = np.arange(embed_dim // 2, dtype=np.float32)
|
|
omega /= embed_dim / 2.
|
|
omega = 1. / 10000 ** omega # (D/2,)
|
|
|
|
pos = pos.reshape(-1) # (M,)
|
|
out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
|
|
|
|
emb_sin = np.sin(out) # (M, D/2)
|
|
emb_cos = np.cos(out) # (M, D/2)
|
|
|
|
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
|
|
return emb
|
|
|
|
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
|
|
assert embed_dim % 2 == 0
|
|
|
|
# use half of dimensions to encode grid_h
|
|
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
|
|
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
|
|
|
|
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
|
|
return emb
|
|
|
|
|
|
# https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20
|
|
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
|
|
"""
|
|
grid_size: int of the grid height and width
|
|
return:
|
|
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
|
|
"""
|
|
if isinstance(grid_size, int):
|
|
grid_h_size, grid_w_size = grid_size, grid_size
|
|
else:
|
|
grid_h_size, grid_w_size = grid_size[0], grid_size[1]
|
|
|
|
grid_h = np.arange(grid_h_size, dtype=np.float32)
|
|
grid_w = np.arange(grid_w_size, dtype=np.float32)
|
|
grid = np.meshgrid(grid_w, grid_h) # here w goes first
|
|
grid = np.stack(grid, axis=0)
|
|
|
|
grid = grid.reshape([2, 1, grid_h_size, grid_w_size])
|
|
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
|
|
if cls_token:
|
|
pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
|
|
return pos_embed
|
|
|
|
def _replace_name_resampler(s, v):
|
|
if re.match("resampler.pos_embed", s):
|
|
return {
|
|
s: v,
|
|
re.sub("pos_embed", "pos_embed_k", s): torch.from_numpy(get_2d_sincos_pos_embed(emb_dim, (70, 70))),
|
|
}
|
|
if re.match("resampler.proj", s):
|
|
return {
|
|
re.sub("proj", "pos_embed_k", s): torch.from_numpy(get_2d_sincos_pos_embed(emb_dim, (70, 70))),
|
|
re.sub("proj", "proj.weight", s): v.transpose(-1, -2).contiguous(),
|
|
}
|
|
if re.match("resampler.attn.in_proj_.*", s):
|
|
return {
|
|
re.sub("attn.in_proj_", "attn.q.", s): v.chunk(3, dim=0)[0],
|
|
re.sub("attn.in_proj_", "attn.k.", s): v.chunk(3, dim=0)[1],
|
|
re.sub("attn.in_proj_", "attn.v.", s): v.chunk(3, dim=0)[2],
|
|
}
|
|
return {s: v}
|
|
|
|
if has_minicpmv_projector:
|
|
projector = torch.load(args.minicpmv_projector)
|
|
new_state_dict = {}
|
|
for k, v in projector.items():
|
|
kvs = _replace_name_resampler(k, v)
|
|
for nk, nv in kvs.items():
|
|
new_state_dict[nk] = nv
|
|
projector = new_state_dict
|
|
ftype_cur = 0
|
|
for name, data in projector.items():
|
|
name = get_tensor_name(name)
|
|
data = data.squeeze().numpy()
|
|
|
|
n_dims = len(data.shape)
|
|
if ftype == 1:
|
|
if name[-7:] == ".weight" and n_dims == 2:
|
|
print(" Converting to float16")
|
|
data = data.astype(np.float16)
|
|
ftype_cur = 1
|
|
else:
|
|
print(" Converting to float32")
|
|
data = data.astype(np.float32)
|
|
ftype_cur = 0
|
|
else:
|
|
if data.dtype != np.float32:
|
|
print(" Converting to float32")
|
|
data = data.astype(np.float32)
|
|
ftype_cur = 0
|
|
|
|
fout.add_tensor(name, data)
|
|
print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}")
|
|
|
|
print("Projector tensors added\n")
|
|
|
|
def _replace_name(s, v):
|
|
s = "vision_model." + s
|
|
if re.match("vision_model.embeddings.position_embedding", s):
|
|
v = v.unsqueeze(0)
|
|
return {s: v}
|
|
|
|
return {s: v}
|
|
|
|
state_dict = model.state_dict()
|
|
new_state_dict = {}
|
|
for k, v in state_dict.items():
|
|
kvs = _replace_name(k, v)
|
|
for nk, nv in kvs.items():
|
|
new_state_dict[nk] = nv
|
|
state_dict = new_state_dict
|
|
for name, data in state_dict.items():
|
|
if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_minicpmv_projector):
|
|
# we don't need this
|
|
print(f"skipping parameter: {name}")
|
|
continue
|
|
|
|
name = get_tensor_name(name)
|
|
data = data.squeeze().numpy()
|
|
|
|
n_dims = len(data.shape)
|
|
|
|
# ftype == 0 -> float32, ftype == 1 -> float16
|
|
ftype_cur = 0
|
|
if n_dims == 4:
|
|
print(f"tensor {name} is always saved in f16")
|
|
data = data.astype(np.float16)
|
|
ftype_cur = 1
|
|
elif ftype == 1:
|
|
if name[-7:] == ".weight" and n_dims == 2:
|
|
print(" Converting to float16")
|
|
data = data.astype(np.float16)
|
|
ftype_cur = 1
|
|
else:
|
|
print(" Converting to float32")
|
|
data = data.astype(np.float32)
|
|
ftype_cur = 0
|
|
else:
|
|
if data.dtype != np.float32:
|
|
print(" Converting to float32")
|
|
data = data.astype(np.float32)
|
|
ftype_cur = 0
|
|
|
|
print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}")
|
|
fout.add_tensor(name, data)
|
|
|
|
|
|
fout.write_header_to_file()
|
|
fout.write_kv_data_to_file()
|
|
fout.write_tensors_to_file()
|
|
fout.close()
|
|
|
|
print("Done. Output file: " + fname_out)
|