mirror of https://github.com/hpcaitech/ColossalAI
74 lines
2.7 KiB
Python
74 lines
2.7 KiB
Python
from functools import partial
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import diffusers
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import torch
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import transformers
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from ..registry import ModelAttribute, model_zoo
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BATCH_SIZE = 2
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SEQ_LENGTH = 5
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HEIGHT = 224
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WIDTH = 224
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IN_CHANNELS = 3
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LATENTS_SHAPE = (BATCH_SIZE, IN_CHANNELS, HEIGHT // 7, WIDTH // 7)
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TIME_STEP = 3
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data_vae_fn = lambda: dict(sample=torch.randn(2, 3, 32, 32))
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data_unet_fn = lambda: dict(sample=torch.randn(2, 3, 32, 32), timestep=3)
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identity_output = lambda x: x
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def data_clip_model():
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input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
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attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
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position_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
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pixel_values = torch.zeros((BATCH_SIZE, IN_CHANNELS, HEIGHT, WIDTH), dtype=torch.float32)
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return dict(input_ids=input_ids,
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pixel_values=pixel_values,
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attention_mask=attention_mask,
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position_ids=position_ids)
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def data_clip_text():
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input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
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attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
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return dict(input_ids=input_ids, attention_mask=attention_mask)
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def data_clip_vision():
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pixel_values = torch.zeros((BATCH_SIZE, IN_CHANNELS, HEIGHT, WIDTH), dtype=torch.float32)
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return dict(pixel_values=pixel_values)
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model_zoo.register(name='diffusers_auto_encoder_kl',
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model_fn=diffusers.AutoencoderKL,
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data_gen_fn=data_vae_fn,
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output_transform_fn=identity_output)
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model_zoo.register(name='diffusers_vq_model',
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model_fn=diffusers.VQModel,
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data_gen_fn=data_vae_fn,
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output_transform_fn=identity_output)
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model_zoo.register(name='diffusers_clip_model',
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model_fn=partial(transformers.CLIPModel, config=transformers.CLIPConfig()),
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data_gen_fn=data_clip_model,
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output_transform_fn=identity_output)
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model_zoo.register(name='diffusers_clip_text_model',
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model_fn=partial(transformers.CLIPTextModel, config=transformers.CLIPTextConfig()),
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data_gen_fn=data_clip_text,
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output_transform_fn=identity_output)
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model_zoo.register(name='diffusers_clip_vision_model',
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model_fn=partial(transformers.CLIPVisionModel, config=transformers.CLIPVisionConfig()),
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data_gen_fn=data_clip_vision,
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output_transform_fn=identity_output)
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model_zoo.register(name='diffusers_unet2d_model',
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model_fn=diffusers.UNet2DModel,
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data_gen_fn=data_unet_fn,
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output_transform_fn=identity_output)
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