* Fix check of unecessary packages (issue #37626)
* Reformat using ruff
* And a condition to avoind the risk of matching a random object in `import_utils`
* Reformat
* copy the last changes from broken PR
* small format
* some fixes and refactoring after review
* format
* add config attr for loss
* some fixes and refactoring
* fix copies
* fix style
* add test for d-fine resnet
* fix decoder layer prop
* fix dummies
* format init
* remove extra print
* refactor modeling, move resnet into separate folder
* fix resnet config
* change resnet on hgnet_v2, add clamp into decoder
* fix init
* fix config doc
* fix init
* fix dummies
* fix config docs
* fix hgnet_v2 config typo
* format modular
* add image classification for hgnet, some refactoring
* format tests
* fix dummies
* fix init
* fix style
* fix init for hgnet v2
* fix index.md, add init rnage for hgnet
* fix conversion
* add missing attr to encoder
* add loss for d-fine, add additional output for rt-detr decoder
* tests and docs fixes
* fix rt_detr v2 conversion
* some fixes for loos and decoder output
* some fixes for loss
* small fix for converted modeling
* add n model config, some todo comments for modular
* convert script adjustments and fixes, small refact
* remove extra output for rt_detr
* make some outputs optionsl, fix conversion
* some posr merge fixes
* small fix
* last field fix
* fix not split for hgnet_v2
* disable parallelism test for hgnet_v2 image classification
* skip multi gpu for d-fine
* adjust after merge init
* remove extra comment
* fix repo name references
* small fixes for tests
* Fix checkpoint path
* Fix consistency
* Fixing docs
---------
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* added fast image processor for VitMatte including updated and new tests, fixed a bug in the slow image processor that processed images incorrectly for input format ChannelDimension.FIRST in which case the trimaps were not added in the correct dimension, this bug was also reflected in the tests through incorretly shaped trimaps being passed
* final edits for fast vitmatte image processor and tests
* final edits for fast vitmatte image processor and tests
---------
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
* added the configuartion for sam_hq
* added the modeelling for sam_hq
* added the sam hq mask decoder with hq features
* added the code for the samhq
* added the code for the samhq
* added the code for the samhq
* Delete src/transformers/models/sam_hq/modelling_sam_hq.py
* added the code for the samhq
* added the code for the samhq
* added the chnages for the modeelling
* added the code for sam hq for image processing
* added code for the sam hq model
* added the required changes
* added the changes
* added the key mappings for the sam hq
* adding the working code of samhq
* added the required files
* adding the pt object
* added the push to hub account
* added the args for the sam maks decoder
* added the args for the sam hq vision config
* aded the some more documentation
* removed the unecessary spaces
* all required chnages
* removed the image processor
* added the required file
* added the changes for the checkcopies
* added the code for modular file
* added the changes for the __init file
* added the code for the interm embeds
* added the code for sam hq
* added the changes for modular file
* added the test file
* added the changes required
* added the changes required
* added the code for the
* added the cl errors
* added the changes
* added the required changes
* added the some code
* added the code for the removing image processor
* added the test dimensins
* added the code for the removing extra used variables
* added the code for modeluar file hf_mlp for a better name
* removed abbrevaation in core functionality
* removed abbrevaation in core functionality
* .contiguous() method is often used to ensure that the tensor is stored in a contiguous block of memory
* added the code which is after make fixup
* added some test for the intermediate embeddings test
* added the code for the torch support in sam hq
* added the code for the updated modular file
* added the changes for documentations as mentioned
* removed the heading
* add the changes for the code
* first mentioned issue resolved
* added the changes code to processor
* added the easy loading to init file
* added the changes to code
* added the code to changes
* added the code to work
* added the code for sam hq
* added the code for sam hq
* added the code for the point pad value
* added the small test for the image embeddings and intermediate embedding
* added the code
* added the code
* added the code for the tests
* added the code
* added ythe code for the processor file
* added the code
* added the code
* added the code
* added the code
* added the code
* added the code for tests and some checks
* added some code
* added the code
* added the code
* added some code
* added some code
* added the changes for required
* added the code
* added the code
* added the code
* added the code
* added the code
* added the code
* added the code
* added the code
* added the code
* added the code
* added some changes
* added some changes
* removed spaces and quality checks
* added some code
* added some code
* added some code
* added code quality checks
* added the checks for quality checks
* addded some code which fixes test_inference_mask_generation_no_point
* added code for the test_inference_mask_generation_one_point_one_bb
* added code for the test_inference_mask_generation_one_point_one_bb_zero
* added code for the test_inference_mask_generation_one_box
* added some code in modelling for testing
* added some code which sort maks with high score
* added some code
* added some code
* added some code for the move KEYS_TO_MODIFY_MAPPING
* added some code for the unsqueeze removal
* added some code for the unsqueeze removal
* added some code
* added some code
* add some code
* added some code
* added some code
* added some testign values changed
* added changes to code in sam hq for readbility purpose
* added pre commit checks
* added the fix samvisionmodel for compatibilty
* added the changes made on sam by cyyever
* fixed the tests for samhq
* added some the code
* added some code related to init file issue during merge conflicts
* remobved the merge conflicts
* added changes mentioned by aruther and mobap
* added changes mentioned by aruther and mobap
* solving quality checks
* added the changes for input clearly
* added the changes
* added changes in mask generation file rgearding model inputs and sam hq quargs in processor file
* added changes in processor file
* added the Setup -> setupclass conversion
* added the code mentioned for processor
* added changes for the code
* added some code
* added some code
* added some code
---------
Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
Two PEFT tests are actually failing:
tests/peft_integration/test_peft_integration.py::PeftIntegrationTester::test_delete_adapter
tests/peft_integration/test_peft_integration.py::PeftIntegrationTester::test_peft_pipeline_no_warning
This must have been going on for some time but was apparently never
noticed. The cause is that the tests themselves are faulty, the PEFT
integration is correct in these cases.
test_delete_adapter
The first faulty test was introduced by #34650. AFAICT, it should never
have passed in the first place, the PEFT integration logic was not
changed in the meantime. At this point, the logs for the PR CI are gone,
so I'm not sure if the test passed back then or not.
test_peft_pipeline_no_warning
This test was introduced in #36783 and should also never have passed, as
the self.assertNoLogs context manager only returns None, thus the assert
should never have worked (mea culpa for suggesting this code snippet).
Here too, the CI logs are deleted by now, so I can't check if the test
already failed back then.
* update siglip2 model card
* Update docs/source/en/model_doc/siglip2.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/siglip2.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/siglip2.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/siglip2.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/siglip2.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/siglip2.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* address comments
* separate naflex and fixres variant
* Update docs/source/en/model_doc/siglip2.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/siglip2.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/siglip2.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Fix wrong position_ids shape in doc
Supported by ClvpDecoder.forward, line 1212--1215:
src/transformers/models/clvp/modeling_clvp.py:
1212 if inputs_embeds is None:
1213 inputs_embeds = self.input_embeds_layer(input_ids)
1214 position_embeds = self.position_embeds_layer(position_ids)
1215 inputs_embeds = inputs_embeds + position_embeds
* Fix possibly wrong input_ids shape in doc
Since 'input_ids_length' was mentioned immediately after the shape `(batch_size, sequence_length)`, it doesn't make sense to me for `input_ids` to have such shape---IMO it ought to have shape `(batch_size, input_ids_length)` instead.
* Fix possibly wrong inputs_embeds shape in doc
Supported by CTRLModel.forward, line 448--449:
src/transformers/models/ctrl/modeling_ctrl.py:
448 if inputs_embeds is None:
449 inputs_embeds = self.w(input_ids)
This commit is introduced due to commit 6f36b56497828642b65f54ea26aa4064186de57a.
* Fix possibly wrong token_type_ids shape in doc
Supported by CTRLModel.forward, line 441--460:
src/transformers/models/ctrl/modeling_ctrl.py:
441 if token_type_ids is not None:
442 token_type_ids = token_type_ids.view(-1, input_shape[-1])
443 token_type_embeds = self.w(token_type_ids)
444 token_type_embeds *= np.sqrt(self.d_model_size)
445 else:
446 token_type_embeds = 0
447
448 if inputs_embeds is None:
449 inputs_embeds = self.w(input_ids)
450 # inputs_embeds = embedded.unsqueeze(0) if len(input_ids.shape)<2 else embedded
451 seq_len = input_shape[-1]
452 mask = torch.triu(torch.ones(seq_len + past_length, seq_len + past_length), 1).to(device)
453
454 inputs_embeds *= np.sqrt(self.d_model_size)
455
456 # `self.pos_encoding` won't be sent to the correct device along the model, so we do it manually.
457 self.pos_encoding = self.pos_encoding.to(device)
458 pos_embeds = self.pos_encoding[position_ids, :]
459
460 hidden_states = inputs_embeds + pos_embeds + token_type_embeds
This commit is introduced due to commit 6f36b56497828642b65f54ea26aa4064186de57a.
* Fix possibly wrong position_ids shape in doc
Supported by CTRLModel.forward, line 448--460:
src/transformers/models/ctrl/modeling_ctrl.py:
448 if inputs_embeds is None:
449 inputs_embeds = self.w(input_ids)
450 # inputs_embeds = embedded.unsqueeze(0) if len(input_ids.shape)<2 else embedded
451 seq_len = input_shape[-1]
452 mask = torch.triu(torch.ones(seq_len + past_length, seq_len + past_length), 1).to(device)
453
454 inputs_embeds *= np.sqrt(self.d_model_size)
455
456 # `self.pos_encoding` won't be sent to the correct device along the model, so we do it manually.
457 self.pos_encoding = self.pos_encoding.to(device)
458 pos_embeds = self.pos_encoding[position_ids, :]
459
460 hidden_states = inputs_embeds + pos_embeds + token_type_embeds
This commit is introduced due to commit 6f36b56497828642b65f54ea26aa4064186de57a.
* Fix wrong token_type_ids shape in doc
Supported by TFCTRLMainLayer.call, line 376--394:
src/transformers/models/ctrl/modeling_tf_ctrl.py:
376 if token_type_ids is not None:
377 token_type_ids = tf.reshape(token_type_ids, [-1, shape_list(token_type_ids)[-1]])
378 token_type_embeds = self.w(token_type_ids)
379 token_type_embeds *= tf.math.sqrt(tf.cast(self.d_model_size, dtype=token_type_embeds.dtype))
380 else:
381 token_type_embeds = tf.constant(0.0)
382 position_ids = tf.reshape(position_ids, [-1, shape_list(position_ids)[-1]])
383
384 if inputs_embeds is None:
385 check_embeddings_within_bounds(input_ids, self.w.input_dim)
386 inputs_embeds = self.w(input_ids)
387 seq_len = input_shape[-1]
388 mask = 1 - tf.linalg.band_part(tf.ones((seq_len, seq_len)), -1, 0)
389
390 inputs_embeds *= tf.math.sqrt(tf.cast(self.d_model_size, inputs_embeds.dtype))
391
392 pos_embeds = tf.gather(self.pos_encoding, position_ids)
393 pos_embeds = tf.cast(pos_embeds, dtype=token_type_embeds.dtype)
394 hidden_states = inputs_embeds + pos_embeds + token_type_embeds
* Fix wrong position_ids shape in doc
Supported by TFCTRLMainLayer.call, line 384--394:
src/transformers/models/ctrl/modeling_tf_ctrl.py:
384 if inputs_embeds is None:
385 check_embeddings_within_bounds(input_ids, self.w.input_dim)
386 inputs_embeds = self.w(input_ids)
387 seq_len = input_shape[-1]
388 mask = 1 - tf.linalg.band_part(tf.ones((seq_len, seq_len)), -1, 0)
389
390 inputs_embeds *= tf.math.sqrt(tf.cast(self.d_model_size, inputs_embeds.dtype))
391
392 pos_embeds = tf.gather(self.pos_encoding, position_ids)
393 pos_embeds = tf.cast(pos_embeds, dtype=token_type_embeds.dtype)
394 hidden_states = inputs_embeds + pos_embeds + token_type_embeds
* Fix wrong inputs_embeds shape in doc
Supported by TFCTRLMainLayer.call, line 384--394:
src/transformers/models/ctrl/modeling_tf_ctrl.py:
384 if inputs_embeds is None:
385 check_embeddings_within_bounds(input_ids, self.w.input_dim)
386 inputs_embeds = self.w(input_ids)
387 seq_len = input_shape[-1]
388 mask = 1 - tf.linalg.band_part(tf.ones((seq_len, seq_len)), -1, 0)
389
390 inputs_embeds *= tf.math.sqrt(tf.cast(self.d_model_size, inputs_embeds.dtype))
391
392 pos_embeds = tf.gather(self.pos_encoding, position_ids)
393 pos_embeds = tf.cast(pos_embeds, dtype=token_type_embeds.dtype)
394 hidden_states = inputs_embeds + pos_embeds + token_type_embeds
* Fix wrong inputs_embeds shape in doc
Supported by ClvpDecoder.forward, line 1212--1213:
src/transformers/models/clvp/modeling_clvp.py:
1212 if inputs_embeds is None:
1213 inputs_embeds = self.input_embeds_layer(input_ids)
* Fix wrong position_ids shape in doc
Supported by FlaxGemmaPreTrainedModel.__call__, line 502--508:
src/transformers/models/gemma/modeling_flax_gemma.py:
502 batch_size, sequence_length = input_ids.shape
503
504 if position_ids is None:
505 if past_key_values is not None:
506 raise ValueError("Make sure to provide `position_ids` when passing `past_key_values`.")
507
508 position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
* Fix wrong position_ids shape in doc
Supported by FlaxGPT2PreTrainedModel.__call__, line 482--488:
src/transformers/models/gpt2/modeling_flax_gpt2.py:
482 batch_size, sequence_length = input_ids.shape
483
484 if position_ids is None:
485 if past_key_values is not None:
486 raise ValueError("Make sure to provide `position_ids` when passing `past_key_values`.")
487
488 position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
* Fix wrong position_ids shape in doc
Supported by GPT2Model.forward, line 918--921:
src/transformers/models/gpt2/modeling_gpt2.py:
918 if inputs_embeds is None:
919 inputs_embeds = self.wte(input_ids)
920 position_embeds = self.wpe(position_ids)
921 hidden_states = inputs_embeds + position_embeds.to(inputs_embeds.device)
* Fix wrong inputs_embeds shape in doc
Supported by GPT2Model.forward, line 918--919:
src/transformers/models/gpt2/modeling_gpt2.py:
918 if inputs_embeds is None:
919 inputs_embeds = self.wte(input_ids)
* Fix wrong labels shape in doc
Supported by GPT2LMHeadModel.forward, line 1156--1157:
src/transformers/models/gpt2/modeling_gpt2.py:
1156 Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
1157 `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
* Fix wrong labels shape in doc
Supported by GPT2DoubleHeadsModel.forward, line 1314--1315:
src/transformers/models/gpt2/modeling_gpt2.py:
1314 Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
1315 `labels = input_ids`. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`. All labels set to
* Fix wrong token_type_ids shape in doc
Supported by TFGPT2MainLayer.call, line 486--500:
src/transformers/models/gpt2/modeling_tf_gpt2.py:
486 if inputs_embeds is None:
487 check_embeddings_within_bounds(input_ids, self.config.vocab_size)
488 inputs_embeds = self.wte(input_ids)
489
490 position_embeds = self.wpe(position_ids)
491
492 if token_type_ids is not None:
493 token_type_ids = tf.reshape(token_type_ids, [-1, shape_list(token_type_ids)[-1]])
494 token_type_embeds = self.wte(token_type_ids)
495 else:
496 token_type_embeds = tf.constant(0.0)
497
498 position_embeds = tf.cast(position_embeds, dtype=inputs_embeds.dtype)
499 token_type_embeds = tf.cast(token_type_embeds, dtype=inputs_embeds.dtype)
500 hidden_states = inputs_embeds + position_embeds + token_type_embeds
* Fix wrong position_ids shape in doc
Supported by TFGPT2MainLayer.call, line 486--500:
src/transformers/models/gpt2/modeling_tf_gpt2.py:
486 if inputs_embeds is None:
487 check_embeddings_within_bounds(input_ids, self.config.vocab_size)
488 inputs_embeds = self.wte(input_ids)
489
490 position_embeds = self.wpe(position_ids)
491
492 if token_type_ids is not None:
493 token_type_ids = tf.reshape(token_type_ids, [-1, shape_list(token_type_ids)[-1]])
494 token_type_embeds = self.wte(token_type_ids)
495 else:
496 token_type_embeds = tf.constant(0.0)
497
498 position_embeds = tf.cast(position_embeds, dtype=inputs_embeds.dtype)
499 token_type_embeds = tf.cast(token_type_embeds, dtype=inputs_embeds.dtype)
500 hidden_states = inputs_embeds + position_embeds + token_type_embeds
* Fix wrong inputs_embeds shape in doc
Supported by TFGPT2MainLayer.call, line 486--488:
src/transformers/models/gpt2/modeling_tf_gpt2.py:
486 if inputs_embeds is None:
487 check_embeddings_within_bounds(input_ids, self.config.vocab_size)
488 inputs_embeds = self.wte(input_ids)
* Fix wrong position_ids shape in doc
Supported by GPTBigCodeModel.forward, line 962--965:
src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py:
962 if inputs_embeds is None:
963 inputs_embeds = self.wte(input_ids)
964 position_embeds = self.wpe(position_ids)
965 hidden_states = inputs_embeds + position_embeds.to(inputs_embeds.device)
* Fix wrong inputs_embeds shape in doc
Supported by GPTBigCodeModel.forward, line 962--963:
src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py:
962 if inputs_embeds is None:
963 inputs_embeds = self.wte(input_ids)
* Fix wrong labels shape in doc
Supported by GPTBigCodeForCausalLM.forward, line 1158--1159:
src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py:
1158 Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
1159 `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
* Fix wrong position_ids shape in doc
Supported by FlaxGPTNeoModule.__call__, line 549--552:
src/transformers/models/gpt_neo/modeling_flax_gpt_neo.py:
549 input_embeds = self.wte(input_ids.astype("i4"))
550 position_embeds = self.wpe(position_ids.astype("i4"))
551
552 hidden_states = input_embeds + position_embeds
* Fix wrong position_ids shape in doc
Supported by GPTNeoModel.forward, line 685--720:
src/transformers/models/gpt_neo/modeling_gpt_neo.py:
685 if inputs_embeds is None:
686 inputs_embeds = self.wte(input_ids)
687
688 # kept for BC (non `Cache` `past_key_values` inputs)
689 return_legacy_cache = False
690 if use_cache and not isinstance(past_key_values, Cache):
691 return_legacy_cache = True
692 if past_key_values is None:
693 past_key_values = DynamicCache()
694 else:
695 past_key_values = DynamicCache.from_legacy_cache(past_key_values)
696 logger.warning_once(
697 "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
698 "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
699 "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
700 )
701
702 seq_length = inputs_embeds.shape[1]
703 if cache_position is None:
704 past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
705 cache_position = torch.arange(past_seen_tokens, past_seen_tokens + seq_length, device=inputs_embeds.device)
706
707 if position_ids is None:
708 position_ids = cache_position.unsqueeze(0)
709
710 causal_mask = self._update_causal_mask(
711 attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
712 )
713
714 # Prepare head mask if needed
715 # 1.0 in head_mask indicate we keep the head
716 # attention_probs has shape bsz x num_heads x N x N
717 # head_mask has shape n_layer x batch x num_heads x N x N
718 head_mask = self.get_head_mask(head_mask, self.config.num_layers)
719 position_embeds = self.wpe(position_ids)
720 hidden_states = inputs_embeds + position_embeds
* Fix wrong inputs_embeds shape in doc
Supported by GPTNeoModel.forward, line 685--686:
src/transformers/models/gpt_neo/modeling_gpt_neo.py:
685 if inputs_embeds is None:
686 inputs_embeds = self.wte(input_ids)
* Fix wrong labels shape in doc
Supported by GPTNeoForCausalLM.forward, line 968--969:
src/transformers/models/gpt_neo/modeling_gpt_neo.py:
968 Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
969 `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
* Fix wrong position_ids shape in doc
Supported by FlaxGPTJPreTrainedModel.__call__, line 455--461:
src/transformers/models/gptj/modeling_flax_gptj.py:
455 batch_size, sequence_length = input_ids.shape
456
457 if position_ids is None:
458 if past_key_values is not None:
459 raise ValueError("Make sure to provide `position_ids` when passing `past_key_values`.")
460
461 position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
* Fix wrong token_type_ids shape in doc
Supported by TFGPTJMainLayer.call, line 482--493:
src/transformers/models/gptj/modeling_tf_gptj.py:
482 if inputs_embeds is None:
483 check_embeddings_within_bounds(input_ids, self.wte.vocab_size)
484 inputs_embeds = self.wte(input_ids, mode="embedding")
485
486 if token_type_ids is not None:
487 token_type_ids = tf.reshape(token_type_ids, [-1, shape_list(token_type_ids)[-1]])
488 token_type_embeds = self.wte(token_type_ids, mode="embedding")
489 else:
490 token_type_embeds = tf.constant(0.0)
491
492 token_type_embeds = tf.cast(token_type_embeds, dtype=inputs_embeds.dtype)
493 hidden_states = inputs_embeds + token_type_embeds
* Fix wrong position_ids shape in doc
Supported by TFGPTJMainLayer.call, line 434--449:
src/transformers/models/gptj/modeling_tf_gptj.py:
434 elif input_ids is not None:
435 input_shape = shape_list(input_ids)
436 input_ids = tf.reshape(input_ids, [-1, input_shape[-1]])
437 elif inputs_embeds is not None:
438 input_shape = shape_list(inputs_embeds)[:-1]
439 else:
440 raise ValueError("You have to specify either input_ids or inputs_embeds")
441
442 if past_key_values is None:
443 past_length = 0
444 past_key_values = [None] * len(self.h)
445 else:
446 past_length = shape_list(past_key_values[0][0])[-2]
447
448 if position_ids is None:
449 position_ids = tf.expand_dims(tf.range(past_length, input_shape[-1] + past_length), axis=0)
* Fix wrong inputs_embeds shape in doc
Supported by TFGPTJMainLayer.call, line 482--484:
src/transformers/models/gptj/modeling_tf_gptj.py:
482 if inputs_embeds is None:
483 check_embeddings_within_bounds(input_ids, self.wte.vocab_size)
484 inputs_embeds = self.wte(input_ids, mode="embedding")
* Fix wrong labels shape in doc
Supported by TFGPTJForCausalLM.call, line 812--813:
src/transformers/models/gptj/modeling_tf_gptj.py:
812 Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
813 `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
* Fix possibly wrong input_ids shape in doc
Since 'input_ids_length' was mentioned immediately after the shape `(batch_size, sequence_length)`, it doesn't make sense to me for `input_ids` to have such shape---IMO it ought to have shape `(batch_size, input_ids_length)` instead.
* Fix possibly wrong token_type_ids shape in doc
Supported by ImageGPTModel.forward, line 773--780:
src/transformers/models/imagegpt/modeling_imagegpt.py:
773 if inputs_embeds is None:
774 inputs_embeds = self.wte(input_ids)
775 position_embeds = self.wpe(position_ids)
776 hidden_states = inputs_embeds + position_embeds.to(inputs_embeds.device)
777
778 if token_type_ids is not None:
779 token_type_embeds = self.wte(token_type_ids)
780 hidden_states = hidden_states + token_type_embeds
This commit is introduced due to commit 8e594a4143cca79f165b99e4ed4c9f3a90047bf3.
* Fix possibly wrong position_ids shape in doc
Supported by ImageGPTModel.forward, line 773--776:
src/transformers/models/imagegpt/modeling_imagegpt.py:
773 if inputs_embeds is None:
774 inputs_embeds = self.wte(input_ids)
775 position_embeds = self.wpe(position_ids)
776 hidden_states = inputs_embeds + position_embeds.to(inputs_embeds.device)
This commit is introduced due to commit 8e594a4143cca79f165b99e4ed4c9f3a90047bf3.
* Fix possibly wrong inputs_embeds shape in doc
Supported by ImageGPTModel.forward, line 773--774:
src/transformers/models/imagegpt/modeling_imagegpt.py:
773 if inputs_embeds is None:
774 inputs_embeds = self.wte(input_ids)
This commit is introduced due to commit 8e594a4143cca79f165b99e4ed4c9f3a90047bf3.
* Fix possibly wrong labels shape in doc
Supported by ImageGPTForCausalImageModeling.forward, line 923--924:
src/transformers/models/imagegpt/modeling_imagegpt.py:
923 Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
924 `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
This commit is introduced due to commit 8e594a4143cca79f165b99e4ed4c9f3a90047bf3.
* Fix possibly wrong labels shape in doc
Supported by ImageGPTModel.forward, line 665--666:
src/transformers/models/imagegpt/modeling_imagegpt.py:
665 Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
666 `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
This commit is introduced due to commit 8e594a4143cca79f165b99e4ed4c9f3a90047bf3.
* Fix wrong position_ids shape in doc
Supported by FlaxLlamaPreTrainedModel.__call__, line 484--490:
src/transformers/models/llama/modeling_flax_llama.py:
484 batch_size, sequence_length = input_ids.shape
485
486 if position_ids is None:
487 if past_key_values is not None:
488 raise ValueError("Make sure to provide `position_ids` when passing `past_key_values`.")
489
490 position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
* Fix wrong position_ids shape in doc
Supported by FlaxMistralPreTrainedModel.__call__, line 478--484:
src/transformers/models/mistral/modeling_flax_mistral.py:
478 batch_size, sequence_length = input_ids.shape
479
480 if position_ids is None:
481 if past_key_values is not None:
482 raise ValueError("Make sure to provide `position_ids` when passing `past_key_values`.")
483
484 position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
* Fix qwen2_5 get_rope_index tensor device locations
* simpler fix
* edit right file for modular model
* add a test
* try normalizing type to fix non-video
* fix some imports
* add a video forward test with dummy input
* skip compilation on cpu offload
* add test
* better logic
* docstring
* boolean logic
* add disk offload check
* warn users if compilation options are set but compilation doesn happen
* fix test
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
