mirror of
https://github.com/huggingface/transformers.git
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0c78ef6cd3
* llavas * add mroe models * fix `compile_forward` test for all models * fix copies * make style * also doesn't support cache class * fix some tests * not copied from * ci green? * fix tests * fix copies * fix tests * check with `numel` and remove `item` * fix copies * fix copies * Update src/transformers/models/cohere2/modeling_cohere2.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * opt remove cross attn * gemma2 * fixup * fixup * fix newly added test * maybe fixed? * green please? --------- Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
558 lines
22 KiB
Python
558 lines
22 KiB
Python
# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. 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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"""Testing suite for the PyTorch Llava-NeXT model."""
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import unittest
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import numpy as np
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import requests
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from huggingface_hub import hf_hub_download
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from parameterized import parameterized
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from transformers import (
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AutoProcessor,
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LlavaOnevisionConfig,
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LlavaOnevisionForConditionalGeneration,
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is_torch_available,
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is_vision_available,
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)
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from transformers.testing_utils import (
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cleanup,
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require_bitsandbytes,
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require_torch,
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slow,
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torch_device,
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)
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from ...generation.test_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import (
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ModelTesterMixin,
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_config_zero_init,
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floats_tensor,
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ids_tensor,
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)
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if is_torch_available():
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import torch
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if is_vision_available():
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from PIL import Image
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class LlavaOnevisionVisionText2TextModelTester:
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def __init__(
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self,
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parent,
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ignore_index=-100,
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image_token_index=1,
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projector_hidden_act="gelu",
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seq_length=7,
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vision_feature_select_strategy="full",
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vision_feature_layer=-1,
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text_config={
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"model_type": "qwen2",
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"seq_length": 7,
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"is_training": True,
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"use_input_mask": True,
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"use_token_type_ids": False,
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"use_labels": True,
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"vocab_size": 99,
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"hidden_size": 32,
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"num_hidden_layers": 2,
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"num_attention_heads": 4,
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"num_key_value_heads": 4,
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"intermediate_size": 37,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"attention_probs_dropout_prob": 0.1,
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"max_position_embeddings": 580,
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"type_vocab_size": 16,
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"type_sequence_label_size": 2,
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"initializer_range": 0.02,
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"num_labels": 3,
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"num_choices": 4,
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"pad_token_id": 0,
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},
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is_training=True,
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vision_config={
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"image_size": 16,
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"patch_size": 8,
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"num_channels": 3,
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"is_training": True,
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"hidden_size": 32,
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"projection_dim": 32,
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"num_hidden_layers": 2,
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"num_attention_heads": 4,
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"intermediate_size": 37,
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"dropout": 0.1,
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"attention_dropout": 0.1,
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"initializer_range": 0.02,
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},
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):
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self.parent = parent
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self.ignore_index = ignore_index
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self.image_token_index = image_token_index
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self.projector_hidden_act = projector_hidden_act
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self.vision_feature_select_strategy = vision_feature_select_strategy
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self.vision_feature_layer = vision_feature_layer
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self.text_config = text_config
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self.vision_config = vision_config
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self.pad_token_id = text_config["pad_token_id"]
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self.num_image_tokens = 10
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self.seq_length = seq_length + self.num_image_tokens
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self.num_hidden_layers = text_config["num_hidden_layers"]
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self.vocab_size = text_config["vocab_size"]
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self.hidden_size = text_config["hidden_size"]
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self.num_attention_heads = text_config["num_attention_heads"]
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self.is_training = is_training
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self.batch_size = 3
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self.num_channels = 3
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self.image_size = 30
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self.image_grid_pinpoints = [[16, 16]]
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def get_config(self):
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return LlavaOnevisionConfig(
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text_config=self.text_config,
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vision_config=self.vision_config,
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ignore_index=self.ignore_index,
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image_token_index=self.image_token_index,
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projector_hidden_act=self.projector_hidden_act,
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vision_feature_select_strategy=self.vision_feature_select_strategy,
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vision_feature_layer=self.vision_feature_layer,
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image_grid_pinpoints=self.image_grid_pinpoints,
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)
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor(
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[
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self.batch_size,
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3,
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self.vision_config["num_channels"],
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self.vision_config["image_size"],
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self.vision_config["image_size"],
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]
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)
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config = self.get_config()
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return config, pixel_values
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, pixel_values = config_and_inputs
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input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 2) + 2
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attention_mask = torch.ones(input_ids.shape, dtype=torch.long).to(torch_device)
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input_ids[input_ids == config.image_token_index] = self.pad_token_id
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input_ids[:, : self.num_image_tokens] = config.image_token_index
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labels = torch.zeros((self.batch_size, self.seq_length), dtype=torch.long, device=torch_device)
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labels[:, : self.num_image_tokens] == self.ignore_index
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inputs_dict = {
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"pixel_values": pixel_values,
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"image_sizes": torch.tensor([[45, 45]] * self.batch_size),
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"labels": labels,
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}
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return config, inputs_dict
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def create_and_check_llava_onevision_model_fp16_forward(
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self, config, input_ids, pixel_values, attention_mask, image_sizes
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):
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model = LlavaOnevisionForConditionalGeneration(config=config)
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model.to(torch_device)
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model.half()
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model.eval()
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logits = model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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image_sizes=image_sizes,
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pixel_values=pixel_values.to(torch.bfloat16),
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return_dict=True,
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)["logits"]
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self.parent.assertFalse(torch.isnan(logits).any().item())
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def create_and_check_llava_onevision_model_fp16_autocast_forward(
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self, config, input_ids, pixel_values, attention_mask, image_sizes
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):
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config.torch_dtype = torch.float16
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model = LlavaOnevisionForConditionalGeneration(config=config)
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model.to(torch_device)
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model.eval()
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with torch.autocast(device_type="cuda", dtype=torch.float16):
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logits = model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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image_sizes=image_sizes,
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pixel_values=pixel_values.to(torch.bfloat16),
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return_dict=True,
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)["logits"]
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self.parent.assertFalse(torch.isnan(logits).any().item())
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@require_torch
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class LlavaOnevisionForConditionalGenerationModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
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"""
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Model tester for `LlavaOnevisionForConditionalGeneration`.
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"""
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all_model_classes = (LlavaOnevisionForConditionalGeneration,) if is_torch_available() else ()
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pipeline_model_mapping = (
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{"image-text-to-text": LlavaOnevisionForConditionalGeneration} if is_torch_available() else {}
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)
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test_pruning = False
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test_head_masking = False
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_is_composite = True
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def setUp(self):
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self.model_tester = LlavaOnevisionVisionText2TextModelTester(self)
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common_properties = ["image_token_index", "video_token_index", "vision_feature_layer"]
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self.config_tester = ConfigTester(
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self, config_class=LlavaOnevisionConfig, has_text_modality=False, common_properties=common_properties
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)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_initialization(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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configs_no_init = _config_zero_init(config)
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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for name, param in model.named_parameters():
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# LLaVa Onevision has SigLIP backbone which init weights differently from CLIP
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if "image_newline" in name or "vision_tower" in name:
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continue
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elif param.requires_grad:
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self.assertIn(
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((param.data.mean() * 1e9).round() / 1e9).item(),
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[0.0, 1.0],
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msg=f"Parameter {name} of model {model_class} seems not properly initialized",
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)
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# overwrite inputs_embeds tests because we need to delete "pixel values" for LVLMs
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def test_inputs_embeds(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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inputs = self._prepare_for_class(inputs_dict, model_class)
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input_ids = inputs["input_ids"]
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del inputs["input_ids"]
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del inputs["pixel_values"]
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wte = model.get_input_embeddings()
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inputs["inputs_embeds"] = wte(input_ids)
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with torch.no_grad():
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model(**inputs)
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# overwrite inputs_embeds tests because we need to delete "pixel values" for LVLMs
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# while some other models require pixel_values to be present
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def test_inputs_embeds_matches_input_ids(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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inputs = self._prepare_for_class(inputs_dict, model_class)
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input_ids = inputs["input_ids"]
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del inputs["input_ids"]
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del inputs["pixel_values"]
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inputs_embeds = model.get_input_embeddings()(input_ids)
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with torch.no_grad():
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out_ids = model(input_ids=input_ids, **inputs)[0]
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out_embeds = model(inputs_embeds=inputs_embeds, **inputs)[0]
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torch.testing.assert_close(out_embeds, out_ids)
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@parameterized.expand(
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[
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(-1,),
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([-1],),
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([-1, -2],),
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],
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)
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def test_vision_feature_layers(self, vision_feature_layer):
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"""
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Test that we can use either one vision feature layer, or a list of
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vision feature layers.
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"""
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config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.vision_feature_layer = vision_feature_layer
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num_feature_layers = 1 if isinstance(vision_feature_layer, int) else len(vision_feature_layer)
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hidden_size = config.vision_config.hidden_size
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expected_features = hidden_size * num_feature_layers
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for model_class in self.all_model_classes:
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model = model_class(config).to(torch_device)
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# We should have the right number of input features,
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# and should be able to run a forward pass without exploding
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assert model.multi_modal_projector.linear_1.in_features == expected_features
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model(**input_dict)
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, SiglipVisionModel does not support standalone training"
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)
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, SiglipVisionModel does not support standalone training"
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)
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, SiglipVisionModel does not support standalone training"
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)
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def test_training_gradient_checkpointing_use_reentrant_false(self):
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pass
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@unittest.skip("FlashAttention only support fp16 and bf16 data type")
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def test_flash_attn_2_fp32_ln(self):
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pass
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@unittest.skip(
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"VLMs need lots of steps to prepare images/mask correctly to get pad-free inputs. Can be tested as part of LLM test"
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)
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def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self):
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pass
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@unittest.skip("LLaVA OneVision has dynamic control flow in unpadding")
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def test_generate_compile_model_forward(self):
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pass
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@require_torch
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class LlavaOnevisionForConditionalGenerationIntegrationTest(unittest.TestCase):
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def setUp(self):
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self.processor = AutoProcessor.from_pretrained(
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"llava-hf/llava-onevision-qwen2-0.5b-ov-hf", padding_side="left"
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)
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image_file = hf_hub_download(
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repo_id="raushan-testing-hf/images_test", filename="llava_v1_5_radar.jpg", repo_type="dataset"
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)
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video_file = hf_hub_download(
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repo_id="raushan-testing-hf/videos-test", filename="video_demo.npy", repo_type="dataset"
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)
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self.image = Image.open(image_file)
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self.video = np.load(video_file)
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self.prompt_image = "user\n<image>\nWhat do you see in this image?<|im_end|>\n<|im_start|>assistant\n"
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self.prompt_video = "user\n<video>\nWhat do you see in this video?<|im_end|>\n<|im_start|>assistant\n"
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def tearDown(self):
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cleanup(torch_device, gc_collect=True)
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@slow
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@require_bitsandbytes
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def test_small_model_integration_test(self):
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model = LlavaOnevisionForConditionalGeneration.from_pretrained(
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"llava-hf/llava-onevision-qwen2-0.5b-ov-hf", torch_dtype="float16", device_map=torch_device
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)
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inputs = self.processor(images=self.image, text=self.prompt_image, return_tensors="pt").to(
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torch_device, torch.float16
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)
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self.assertTrue(inputs.input_ids.shape[1] == 6567) # should expand num-image-tokens times
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self.assertTrue(inputs.pixel_values.shape == torch.Size([1, 10, 3, 384, 384]))
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self.assertTrue(inputs.image_sizes.tolist() == [[899, 1024]])
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# verify single forward pass
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inputs = inputs.to(torch_device)
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# verify generation
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output = model.generate(**inputs, max_new_tokens=100)
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EXPECTED_DECODED_TEXT = 'user\n\nWhat do you see in this image?\nassistant\nThe image is a radar chart that compares the performance of different models in a specific task, likely related to natural language processing or machine learning. The chart is divided into several axes, each representing a different model or method. The models are color-coded and labeled with their respective names. The axes are labeled with terms such as "VQA," "GQA," "MQA," "VQAv2," "MM-Vet," "LLaVA-Bench," "LLaVA-1' # fmt: skip
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self.assertEqual(
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self.processor.decode(output[0], skip_special_tokens=True),
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EXPECTED_DECODED_TEXT,
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)
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@slow
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@require_bitsandbytes
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def test_small_model_integration_test_batch(self):
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model = LlavaOnevisionForConditionalGeneration.from_pretrained(
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"llava-hf/llava-onevision-qwen2-0.5b-ov-hf", torch_dtype="float16", device_map=torch_device
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)
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inputs = self.processor(
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text=[self.prompt_image, self.prompt_video],
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images=self.image,
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videos=self.video,
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return_tensors="pt",
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padding=True,
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).to(torch_device, torch.float16)
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output = model.generate(**inputs, max_new_tokens=20)
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EXPECTED_DECODED_TEXT = ['user\n\nWhat do you see in this image?\nassistant\nThe image is a radar chart that compares the performance of different models in a specific task, likely related', 'user\n\nWhat do you see in this video?\nassistant\nA child wearing a light blue sleeveless top and pink pants is seen sitting on a bed, eng'] # fmt: skip
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self.assertEqual(
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self.processor.batch_decode(output, skip_special_tokens=True),
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EXPECTED_DECODED_TEXT,
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)
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@slow
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@require_bitsandbytes
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def test_small_model_integration_test_video(self):
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# related to (#29835)
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model = LlavaOnevisionForConditionalGeneration.from_pretrained(
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"llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
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torch_dtype="float16",
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device_map=torch_device,
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)
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inputs = self.processor(text=self.prompt_video, videos=self.video, return_tensors="pt").to(
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torch_device, torch.float16
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)
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# verify generation
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output = model.generate(**inputs, max_new_tokens=40)
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EXPECTED_DECODED_TEXT = 'user\n\nWhat do you see in this video?\nassistant\nA child wearing a light blue sleeveless top and pink pants is seen sitting on a bed, engrossed in reading a book.' # fmt: skip
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self.assertEqual(
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self.processor.decode(output[0], skip_special_tokens=True),
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EXPECTED_DECODED_TEXT,
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)
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@slow
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@require_bitsandbytes
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def test_small_model_integration_test_multi_image(self):
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# related to (#29835)
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model = LlavaOnevisionForConditionalGeneration.from_pretrained(
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"llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
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torch_dtype="float16",
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device_map=torch_device,
|
|
)
|
|
|
|
url = "https://www.ilankelman.org/stopsigns/australia.jpg"
|
|
image = Image.open(requests.get(url, stream=True).raw)
|
|
prompt = (
|
|
"user\n<image><image>\nWhat is the difference between these images?<|im_end|>\n<|im_start|>assistant\n"
|
|
)
|
|
inputs = self.processor(text=prompt, images=[self.image, image], return_tensors="pt").to(
|
|
torch_device, torch.float16
|
|
)
|
|
|
|
# verify generation
|
|
output = model.generate(**inputs, max_new_tokens=40)
|
|
EXPECTED_DECODED_TEXT = "user\n\nWhat is the difference between these images?\nassistant\nThe images you've provided appear to be related to a graphical representation of a radar chart, which is a type of data visualization used to show the distribution of a particular variable across a geographic area. The" # fmt: skip
|
|
|
|
self.assertEqual(
|
|
self.processor.decode(output[0], skip_special_tokens=True),
|
|
EXPECTED_DECODED_TEXT,
|
|
)
|
|
|
|
@slow
|
|
@require_bitsandbytes
|
|
def test_small_model_integration_test_multi_video(self):
|
|
# related to (#29835)
|
|
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
|
|
"llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
|
|
torch_dtype="float16",
|
|
device_map=torch_device,
|
|
)
|
|
|
|
prompt = "user\n<video><video>\nAre these videos identical?<|im_end|>\n<|im_start|>assistant\n"
|
|
inputs = self.processor(text=prompt, videos=[self.video, self.video], return_tensors="pt").to(
|
|
torch_device, torch.float16
|
|
)
|
|
|
|
# verify generation
|
|
output = model.generate(**inputs, max_new_tokens=40)
|
|
EXPECTED_DECODED_TEXT = "user\n\nAre these videos identical?\nassistant\nNo, the video is not identical; it shows slight variations in the child's actions and the background." # fmt: skip
|
|
|
|
self.assertEqual(
|
|
self.processor.decode(output[0], skip_special_tokens=True),
|
|
EXPECTED_DECODED_TEXT,
|
|
)
|
|
|
|
@slow
|
|
@require_bitsandbytes
|
|
def test_small_model_integration_test_batch_different_resolutions(self):
|
|
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
|
|
"llava-hf/llava-onevision-qwen2-0.5b-ov-hf", torch_dtype="float16", device_map=torch_device
|
|
)
|
|
|
|
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
lowres_url = "https://4.img-dpreview.com/files/p/TS560x560~forums/56876524/03975b28741443319e9a94615e35667e"
|
|
cats_image = Image.open(requests.get(url, stream=True).raw)
|
|
lowres_img = Image.open(requests.get(lowres_url, stream=True).raw)
|
|
|
|
inputs = self.processor(
|
|
text=[self.prompt_image, self.prompt_image],
|
|
images=[lowres_img, cats_image],
|
|
return_tensors="pt",
|
|
padding=True,
|
|
).to(torch_device, torch.float16)
|
|
|
|
# verify generation
|
|
output = model.generate(**inputs, max_new_tokens=50)
|
|
EXPECTED_DECODED_TEXT = ['user\n\nWhat do you see in this image?\nassistant\nThe image shows a scene from a wildlife camera, likely a security camera, capturing a moment in a natural setting. It features two deer, one larger and one smaller, grazing on the grass. The environment is foggy, suggesting early morning or late', 'user\n\nWhat do you see in this image?\nassistant\nIn the tranquil setting of this image, two cats are enjoying a peaceful nap on a vibrant pink blanket. The cat on the left, with its gray and black striped fur, is lying on its side, its head comfortably resting on the blanket. Its'] # fmt: skip
|
|
self.assertEqual(
|
|
self.processor.batch_decode(output, skip_special_tokens=True),
|
|
EXPECTED_DECODED_TEXT,
|
|
)
|
|
|
|
@slow
|
|
@require_bitsandbytes
|
|
def test_small_model_integration_test_batch_matches_single(self):
|
|
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
|
|
"llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
|
|
torch_dtype="float16",
|
|
device_map=torch_device,
|
|
)
|
|
|
|
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
lowres_url = "https://4.img-dpreview.com/files/p/TS560x560~forums/56876524/03975b28741443319e9a94615e35667e"
|
|
cats_image = Image.open(requests.get(url, stream=True).raw)
|
|
lowres_img = Image.open(requests.get(lowres_url, stream=True).raw)
|
|
|
|
inputs_batched = self.processor(
|
|
text=[self.prompt_image, self.prompt_image],
|
|
images=[lowres_img, cats_image],
|
|
return_tensors="pt",
|
|
padding=True,
|
|
).to(torch_device, torch.float16)
|
|
|
|
inputs_single = self.processor(
|
|
text=self.prompt_image, images=lowres_img, return_tensors="pt", padding=True
|
|
).to(torch_device, torch.float16)
|
|
|
|
# verify generation
|
|
output_batched = model.generate(**inputs_batched, max_new_tokens=50)
|
|
output_single = model.generate(**inputs_single, max_new_tokens=50)
|
|
|
|
self.assertEqual(
|
|
self.processor.decode(output_batched[0], skip_special_tokens=True),
|
|
self.processor.decode(output_single[0], skip_special_tokens=True),
|
|
)
|