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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,199 @@ | ||
| # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
|
||
| from typing import Union | ||
|
|
||
| import torch | ||
| import torch.nn as nn | ||
| from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer | ||
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| class AbstractEmbModel(nn.Module): | ||
| def __init__(self, enable_lora_finetune=False, target_block=[], target_module=[]): | ||
| super().__init__() | ||
| self._is_trainable = None | ||
| self._ucg_rate = None | ||
| self._input_key = None | ||
|
|
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| self.TARGET_BLOCK = target_block | ||
| self.TARGET_MODULE = target_module | ||
| if enable_lora_finetune: | ||
| self.lora_layers = [] | ||
|
|
||
| @property | ||
| def is_trainable(self) -> bool: | ||
| return self._is_trainable | ||
|
|
||
| @property | ||
| def ucg_rate(self) -> Union[float, torch.Tensor]: | ||
| return self._ucg_rate | ||
|
|
||
| @property | ||
| def input_key(self) -> str: | ||
| return self._input_key | ||
|
|
||
| @is_trainable.setter | ||
| def is_trainable(self, value: bool): | ||
| self._is_trainable = value | ||
|
|
||
| @ucg_rate.setter | ||
| def ucg_rate(self, value: Union[float, torch.Tensor]): | ||
| self._ucg_rate = value | ||
|
|
||
| @input_key.setter | ||
| def input_key(self, value: str): | ||
| self._input_key = value | ||
|
|
||
| @is_trainable.deleter | ||
| def is_trainable(self): | ||
| del self._is_trainable | ||
|
|
||
| @ucg_rate.deleter | ||
| def ucg_rate(self): | ||
| del self._ucg_rate | ||
|
|
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| @input_key.deleter | ||
| def input_key(self): | ||
| del self._input_key | ||
|
|
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| def encode(self, *args, **kwargs): | ||
| raise NotImplementedError | ||
|
|
||
| def _enable_lora(self, lora_model): | ||
| for module_name, module in lora_model.named_modules(): | ||
| if module.__class__.__name__ in self.TARGET_BLOCK: | ||
| tmp = {} | ||
| for sub_name, sub_module in module.named_modules(): | ||
| if sub_module.__class__.__name__ in self.TARGET_MODULE: | ||
| if hasattr(sub_module, "input_size") and hasattr( | ||
| sub_module, "output_size" | ||
| ): # for megatron ParallelLinear | ||
| lora = LoraWrapper(sub_module, sub_module.input_size, sub_module.output_size) | ||
| else: # for nn.Linear | ||
| lora = LoraWrapper(sub_module, sub_module.in_features, sub_module.out_features) | ||
| self.lora_layers.append(lora) | ||
| if sub_name not in tmp.keys(): | ||
| tmp.update({sub_name: lora}) | ||
| else: | ||
| print(f"Duplicate subnames are found in module {module_name}") | ||
| for sub_name, lora_layer in tmp.items(): | ||
| lora_name = f'{sub_name}_lora' | ||
| module.add_module(lora_name, lora_layer) | ||
|
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||
|
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| class FrozenCLIPEmbedder(AbstractEmbModel): | ||
| """Uses the CLIP transformer encoder for text (from Hugging Face)""" | ||
|
|
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| LAYERS = ["last", "pooled", "hidden"] | ||
|
|
||
| def __init__( | ||
| self, | ||
| version="openai/clip-vit-large-patch14", | ||
| device="cuda", | ||
| max_length=77, | ||
| enable_lora_finetune=False, | ||
| layer="last", | ||
| layer_idx=None, | ||
| always_return_pooled=False, | ||
| dtype=torch.float, | ||
| ): | ||
| super().__init__(enable_lora_finetune, target_block=["CLIPAttention", "CLIPMLP"], target_module=["Linear"]) | ||
| self.tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") | ||
| self.transformer = CLIPTextModel.from_pretrained(version, torch_dtype=dtype).to(device) | ||
| self.device = device | ||
| self.max_length = max_length | ||
| self.freeze() | ||
| if enable_lora_finetune: | ||
| self._enable_lora(self.transformer) | ||
| print(f"CLIP transformer encoder add {len(self.lora_layers)} lora layers.") | ||
|
|
||
| self.layer = layer | ||
| self.layer_idx = layer_idx | ||
| self.return_pooled = always_return_pooled | ||
| if layer == "hidden": | ||
| assert layer_idx is not None | ||
| assert 0 <= abs(layer_idx) <= 12 | ||
|
|
||
| def freeze(self): | ||
| self.transformer = self.transformer.eval() | ||
| for param in self.parameters(): | ||
| param.requires_grad = False | ||
|
|
||
| def forward(self, text, max_sequence_length=None): | ||
| batch_encoding = self.tokenizer( | ||
| text, | ||
| truncation=True, | ||
| max_length=max_sequence_length if max_sequence_length else self.max_length, | ||
| return_length=True, | ||
| return_overflowing_tokens=False, | ||
| padding="max_length", | ||
| return_tensors="pt", | ||
| ) | ||
| tokens = batch_encoding["input_ids"].to(self.transformer.device, non_blocking=True) | ||
| outputs = self.transformer(input_ids=tokens, output_hidden_states=(self.layer == "hidden")) | ||
|
|
||
| if self.layer == "last": | ||
| z = outputs.last_hidden_state | ||
| elif self.layer == "pooled": | ||
| z = outputs.pooler_output[:, None, :] | ||
| else: | ||
| z = outputs.hidden_states[self.layer_idx] | ||
|
|
||
| # Pad the seq length to multiple of 8 | ||
| seq_len = (z.shape[1] + 8 - 1) // 8 * 8 | ||
| z = torch.nn.functional.pad(z, (0, 0, 0, seq_len - z.shape[1]), value=0.0) | ||
| if self.return_pooled: | ||
| return z, outputs.pooler_output | ||
| return z | ||
|
|
||
| def encode(self, text): | ||
| return self(text) | ||
|
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||
|
|
||
| class FrozenT5Embedder(AbstractEmbModel): | ||
| def __init__( | ||
| self, | ||
| version="google/t5-v1_1-xxl", | ||
| max_length=512, | ||
| device="cuda", | ||
| dtype=torch.float, | ||
| ): | ||
| super().__init__() | ||
| self.tokenizer = T5Tokenizer.from_pretrained("google/t5-v1_1-xxl", max_length=max_length) | ||
| self.transformer = T5EncoderModel.from_pretrained(version, torch_dtype=dtype).to(device) | ||
| self.max_length = max_length | ||
| self.freeze() | ||
| self.device = device | ||
| self.dtype = dtype | ||
|
|
||
| def freeze(self): | ||
| self.transformer = self.transformer.eval() | ||
| for param in self.parameters(): | ||
| param.requires_grad = False | ||
|
|
||
| def forward(self, text, max_sequence_length=None): | ||
| batch_encoding = self.tokenizer( | ||
| text, | ||
| truncation=True, | ||
| max_length=max_sequence_length if max_sequence_length else self.max_length, | ||
| return_length=False, | ||
| return_overflowing_tokens=False, | ||
| padding="max_length", | ||
| return_tensors="pt", | ||
| ) | ||
|
|
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| tokens = batch_encoding["input_ids"].to(self.transformer.device, non_blocking=True) | ||
| outputs = self.transformer(input_ids=tokens, output_hidden_states=None) | ||
|
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||
| return outputs.last_hidden_state |
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