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| # Copyright 2024 The HuggingFace Team. | |
| # | |
| # 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. | |
| import inspect | |
| import unittest | |
| import numpy as np | |
| import torch | |
| from transformers import AutoTokenizer, GlmConfig, GlmForCausalLM | |
| from diffusers import AutoencoderKL, CogView4Pipeline, CogView4Transformer2DModel, FlowMatchEulerDiscreteScheduler | |
| from diffusers.utils.testing_utils import enable_full_determinism, torch_device | |
| from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS | |
| from ..test_pipelines_common import PipelineTesterMixin, to_np | |
| enable_full_determinism() | |
| class CogView4PipelineFastTests(PipelineTesterMixin, unittest.TestCase): | |
| pipeline_class = CogView4Pipeline | |
| params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"} | |
| batch_params = TEXT_TO_IMAGE_BATCH_PARAMS | |
| image_params = TEXT_TO_IMAGE_IMAGE_PARAMS | |
| image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS | |
| required_optional_params = frozenset( | |
| [ | |
| "num_inference_steps", | |
| "generator", | |
| "latents", | |
| "return_dict", | |
| "callback_on_step_end", | |
| "callback_on_step_end_tensor_inputs", | |
| ] | |
| ) | |
| supports_dduf = False | |
| test_xformers_attention = False | |
| test_layerwise_casting = True | |
| def get_dummy_components(self): | |
| torch.manual_seed(0) | |
| transformer = CogView4Transformer2DModel( | |
| patch_size=2, | |
| in_channels=4, | |
| num_layers=2, | |
| attention_head_dim=4, | |
| num_attention_heads=4, | |
| out_channels=4, | |
| text_embed_dim=32, | |
| time_embed_dim=8, | |
| condition_dim=4, | |
| ) | |
| torch.manual_seed(0) | |
| vae = AutoencoderKL( | |
| block_out_channels=[32, 64], | |
| in_channels=3, | |
| out_channels=3, | |
| down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], | |
| up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], | |
| latent_channels=4, | |
| sample_size=128, | |
| ) | |
| torch.manual_seed(0) | |
| scheduler = FlowMatchEulerDiscreteScheduler( | |
| base_shift=0.25, | |
| max_shift=0.75, | |
| base_image_seq_len=256, | |
| use_dynamic_shifting=True, | |
| time_shift_type="linear", | |
| ) | |
| torch.manual_seed(0) | |
| text_encoder_config = GlmConfig( | |
| hidden_size=32, intermediate_size=8, num_hidden_layers=2, num_attention_heads=4, head_dim=8 | |
| ) | |
| text_encoder = GlmForCausalLM(text_encoder_config) | |
| # TODO(aryan): change this to THUDM/CogView4 once released | |
| tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4-9b-chat", trust_remote_code=True) | |
| components = { | |
| "transformer": transformer, | |
| "vae": vae, | |
| "scheduler": scheduler, | |
| "text_encoder": text_encoder, | |
| "tokenizer": tokenizer, | |
| } | |
| return components | |
| def get_dummy_inputs(self, device, seed=0): | |
| if str(device).startswith("mps"): | |
| generator = torch.manual_seed(seed) | |
| else: | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| inputs = { | |
| "prompt": "dance monkey", | |
| "negative_prompt": "", | |
| "generator": generator, | |
| "num_inference_steps": 2, | |
| "guidance_scale": 6.0, | |
| "height": 16, | |
| "width": 16, | |
| "max_sequence_length": 16, | |
| "output_type": "pt", | |
| } | |
| return inputs | |
| def test_inference(self): | |
| device = "cpu" | |
| components = self.get_dummy_components() | |
| pipe = self.pipeline_class(**components) | |
| pipe.to(device) | |
| pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_dummy_inputs(device) | |
| image = pipe(**inputs)[0] | |
| generated_image = image[0] | |
| self.assertEqual(generated_image.shape, (3, 16, 16)) | |
| expected_image = torch.randn(3, 16, 16) | |
| max_diff = np.abs(generated_image - expected_image).max() | |
| self.assertLessEqual(max_diff, 1e10) | |
| def test_callback_inputs(self): | |
| sig = inspect.signature(self.pipeline_class.__call__) | |
| has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters | |
| has_callback_step_end = "callback_on_step_end" in sig.parameters | |
| if not (has_callback_tensor_inputs and has_callback_step_end): | |
| return | |
| components = self.get_dummy_components() | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| self.assertTrue( | |
| hasattr(pipe, "_callback_tensor_inputs"), | |
| f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs", | |
| ) | |
| def callback_inputs_subset(pipe, i, t, callback_kwargs): | |
| # iterate over callback args | |
| for tensor_name, tensor_value in callback_kwargs.items(): | |
| # check that we're only passing in allowed tensor inputs | |
| assert tensor_name in pipe._callback_tensor_inputs | |
| return callback_kwargs | |
| def callback_inputs_all(pipe, i, t, callback_kwargs): | |
| for tensor_name in pipe._callback_tensor_inputs: | |
| assert tensor_name in callback_kwargs | |
| # iterate over callback args | |
| for tensor_name, tensor_value in callback_kwargs.items(): | |
| # check that we're only passing in allowed tensor inputs | |
| assert tensor_name in pipe._callback_tensor_inputs | |
| return callback_kwargs | |
| inputs = self.get_dummy_inputs(torch_device) | |
| # Test passing in a subset | |
| inputs["callback_on_step_end"] = callback_inputs_subset | |
| inputs["callback_on_step_end_tensor_inputs"] = ["latents"] | |
| output = pipe(**inputs)[0] | |
| # Test passing in a everything | |
| inputs["callback_on_step_end"] = callback_inputs_all | |
| inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs | |
| output = pipe(**inputs)[0] | |
| def callback_inputs_change_tensor(pipe, i, t, callback_kwargs): | |
| is_last = i == (pipe.num_timesteps - 1) | |
| if is_last: | |
| callback_kwargs["latents"] = torch.zeros_like(callback_kwargs["latents"]) | |
| return callback_kwargs | |
| inputs["callback_on_step_end"] = callback_inputs_change_tensor | |
| inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs | |
| output = pipe(**inputs)[0] | |
| assert output.abs().sum() < 1e10 | |
| def test_inference_batch_single_identical(self): | |
| self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-3) | |
| def test_attention_slicing_forward_pass( | |
| self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3 | |
| ): | |
| if not self.test_attention_slicing: | |
| return | |
| components = self.get_dummy_components() | |
| pipe = self.pipeline_class(**components) | |
| for component in pipe.components.values(): | |
| if hasattr(component, "set_default_attn_processor"): | |
| component.set_default_attn_processor() | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| generator_device = "cpu" | |
| inputs = self.get_dummy_inputs(generator_device) | |
| output_without_slicing = pipe(**inputs)[0] | |
| pipe.enable_attention_slicing(slice_size=1) | |
| inputs = self.get_dummy_inputs(generator_device) | |
| output_with_slicing1 = pipe(**inputs)[0] | |
| pipe.enable_attention_slicing(slice_size=2) | |
| inputs = self.get_dummy_inputs(generator_device) | |
| output_with_slicing2 = pipe(**inputs)[0] | |
| if test_max_difference: | |
| max_diff1 = np.abs(to_np(output_with_slicing1) - to_np(output_without_slicing)).max() | |
| max_diff2 = np.abs(to_np(output_with_slicing2) - to_np(output_without_slicing)).max() | |
| self.assertLess( | |
| max(max_diff1, max_diff2), | |
| expected_max_diff, | |
| "Attention slicing should not affect the inference results", | |
| ) | |