Add MLLama (#33703)
* current changes * nit * Add cross_attenttion_mask to processor * multi-image fixed * Add cross_attenttion_mask to processor * cross attn works in all cases * WIP refactoring function for image processor * WIP refactoring image processor functions * Refactor preprocess to use global loops instead of list nested list comps * Docstrings * Add channels unification * fix dtype issues * Update docsrings and format * Consistent max_image_tiles * current script * updates * Add convert to rgb * Add image processor tests * updates! * update * god damn it I am dumb sometimes * Precompute aspect ratios * now this works, full match * fix 😉 * nits * style * fix model and conversion * nit * nit * kinda works * hack for sdpa non-contiguous bias * nits here and there * latest c hanges * merge? * run forward * Add aspect_ratio_mask * vision attention mask * update script and config variable names * nit * nits * be able to load * style * nits * there * nits * make forward run * small update * enable generation multi-turn * nit * nit * Clean up a bit for errors and typos * A bit more constant fixes * 90B keys and shapes match * Fix for 11B model * Fixup, remove debug part * Docs * Make max_aspect_ratio_id to be minimal * Update image processing code to match new implementation * Adjust conversion for final checkpoint state * Change dim in repeat_interleave (accordig to meta code) * tmp fix for num_tiles * Fix for conversion (gate<->up, q/k_proj rope permute) * nits * codestyle * Vision encoder fixes * pass cross attn mask further * Refactor aspect ratio mask * Disable text-only generation * Fix cross attention layers order, remove q/k norm rotation for cross atention layers * Refactor gated position embeddings * fix bugs but needs test with new weights * rope scaling should be llama3 * Fix rope scaling name * Remove debug for linear layer * fix copies * Make mask prepare private func * Remove linear patch embed * Make precomputed embeddings as nn.Embedding module * MllamaPrecomputedAspectRatioEmbedding with config init * Remove unused self.output_dim * nit, intermediate layers * Rename ln and pos_embed * vision_chunk_size -> image_size * return_intermediate -> intermediate_layers_indices * vision_input_dim -> hidden_size * Fix copied from statements * fix most tests * Fix more copied from * layer_id->layer_idx * Comment * Fix tests for processor * Copied from for _prepare_4d_causal_attention_mask_with_cache_position * Style fix * Add MllamaForCausalLM * WIP fixing tests * Remove duplicated layers * Remove dummy file * Fix style * Fix consistency * Fix some TODOs * fix language_model instantiation, add docstring * Move docstring, remove todos for precomputed embeds (we cannot init them properly) * Add initial docstrings * Fix * fix some tests * lets skip these * nits, remove print, style * Add one more copied from * Improve test message * Make validate func private * Fix dummy objects * Refactor `data_format` a bit + add comment * typos/nits Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com> * fix dummy objects and imports * Add chat template config json * remove num_kv_heads from vision attention * fix * move some commits and add more tests * fix test * Remove `update_key_name` from modeling utils * remove num-kv-heads again * some prelimiary docs * Update chat template + tests * nit, conversion script max_num_tiles from params * Fix warning for text-only generation * Update conversion script for instruct models * Update chat template in converstion + test * add tests for CausalLM model * model_max_length, avoid null chat_template * Refactor conversion script * Fix forward * Fix integration tests * Refactor vision config + docs * Fix default * Refactor text config * Doc fixes * Remove unused args, fix docs example * Squashed commit of the following: commit b51ce5a2efffbecdefbf6fc92ee87372ec9d8830 Author: qubvel <qubvel@gmail.com> Date: Wed Sep 18 13:39:15 2024 +0000 Move model + add output hidden states and output attentions * Fix num_channels * Add mllama text and mllama vision models * Fixing repo consistency * Style fix * Fixing repo consistency * Fixing unused config params * Fix failed tests after refactoring * hidden_activation -> hidden_act for text mlp * Remove from_pretrained from sub-configs * Apply suggestions from code review Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/models/mllama/convert_mllama_weights_to_hf.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Reuse lambda in conversion script * Remove run.py * Update docs/source/en/model_doc/mllama.md Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/models/mllama/processing_mllama.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Remove unused LlamaTokenizerFast * Fix logging * Refactor gating * Remove cycle for collecting intermediate states * Refactor text-only check, add integration test for text-only * Revert from pretrained to configs * Fix example * Add auto `bos_token` adding in processor * Fix tips * Update src/transformers/models/auto/tokenization_auto.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Enable supports_gradient_checkpointing model flag * add eager/sdpa options * don't skip attn tests and bring back GC skips (did i really remove those?) * Fix signature, but get error with None gradient * Fix output attention tests * Disable GC back * Change no split modules * Fix dropout * Style * Add Mllama to sdpa list * Add post init for vision model * Refine config for MllamaForCausalLMModelTest and skipped tests for CausalLM model * if skipped, say it, don't pass * Clean vision tester config * Doc for args * Update tests/models/mllama/test_modeling_mllama.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Add cross_attention_mask to test * typehint * Remove todo * Enable gradient checkpointing * Docstring * Style * Fixing and skipping some tests for new cache * Mark flaky test * Skip `test_sdpa_can_compile_dynamic` test * Fixing some offload tests * Add direct GenerationMixin inheritance * Remove unused code * Add initializer_range to vision config * update the test to make sure we show if split * fix gc? * Fix repo consistency * Undo modeling utils debug changes * Fix link * mllama -> Mllama * [mllama] -> [Mllama] * Enable compile test for CausalLM model (text-only) * Fix TextModel prefix * Update doc * Docs for forward, type hints, and vision model prefix * make sure to reset * fix init * small script refactor and styling * nit * updates! * some nits * Interpolate embeddings for 560 size and update integration tests * nit * does not suppor static cache! * update * fix * nit2 * this? * Fix conversion * Style * 4x memory improvement with image cache AFAIK * Token decorator for tests * Skip failing tests * update processor errors * fix split issues * style * weird * style * fix failing tests * update * nit fixing the whisper tests * fix path * update --------- Co-authored-by: raushan <raushan@huggingface.co> Co-authored-by: pavel <ubuntu@ip-10-90-0-11.ec2.internal> Co-authored-by: qubvel <qubvel@gmail.com> Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com> Co-authored-by: ydshieh <ydshieh@users.noreply.github.com> Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
This commit is contained in:
Arthur
@@ -490,7 +490,7 @@ class GenerationTesterMixin:
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config, input_ids, attention_mask, inputs_dict = self._get_input_ids_and_config()
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if not hasattr(config, "use_cache"):
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self.skipTest(reason="This model doesn't support caching")
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self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
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if any(model_name in model_class.__name__.lower() for model_name in ["rwkv"]):
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self.skipTest(reason="Won't fix: model with non-standard dictionary output shapes")
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@@ -631,7 +631,7 @@ class GenerationTesterMixin:
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config, input_ids, attention_mask, inputs_dict = self._get_input_ids_and_config()
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if not hasattr(config, "use_cache"):
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self.skipTest(reason="This model doesn't support caching")
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self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
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if any(model_name in model_class.__name__.lower() for model_name in ["rwkv"]):
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self.skipTest(reason="Won't fix: model with non-standard dictionary output shapes")
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@@ -983,7 +983,7 @@ class GenerationTesterMixin:
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# NOTE: contrastive search only works with cache on at the moment.
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if not hasattr(config, "use_cache"):
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self.skipTest(reason="This model doesn't support caching")
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self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
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config.is_decoder = True
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# test old generation output for backwards compatibility
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@@ -1014,7 +1014,7 @@ class GenerationTesterMixin:
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# NOTE: contrastive search only works with cache on at the moment.
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if not hasattr(config, "use_cache"):
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self.skipTest(reason="This model doesn't support caching")
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self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
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config.is_decoder = True
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model = model_class(config).to(torch_device).eval()
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@@ -1054,7 +1054,7 @@ class GenerationTesterMixin:
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# NOTE: contrastive search only works with cache on at the moment.
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if not hasattr(config, "use_cache"):
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self.skipTest(reason="This model doesn't support caching")
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self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
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config.is_decoder = True
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@@ -1085,6 +1085,7 @@ class GenerationTesterMixin:
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self.assertListEqual(low_output.tolist(), high_output.tolist())
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@pytest.mark.generate
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@unittest.skip("Started to break with https://github.com/huggingface/transformers/pull/33703")
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def test_beam_search_low_memory(self):
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# Check that choosing 'low_memory' does not change the model output
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for model_class in self.all_generative_model_classes:
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@@ -1172,7 +1173,7 @@ class GenerationTesterMixin:
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# NOTE: assisted generation only works with cache on at the moment.
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if not hasattr(config, "use_cache"):
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self.skipTest(reason="This model doesn't support caching")
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self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
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config.is_decoder = True
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model = model_class(config).to(torch_device).eval()
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@@ -1249,7 +1250,7 @@ class GenerationTesterMixin:
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# NOTE: assisted generation only works with cache on at the moment.
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if not hasattr(config, "use_cache"):
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self.skipTest(reason="This model doesn't support caching")
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self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
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config.is_decoder = True
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model = model_class(config).to(torch_device).eval()
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@@ -1362,7 +1363,7 @@ class GenerationTesterMixin:
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# NOTE: assisted generation only works with cache on at the moment.
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if not hasattr(config, "use_cache"):
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self.skipTest(reason="This model doesn't support caching")
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self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
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config.is_decoder = True
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model = model_class(config).to(torch_device).eval()
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@@ -1549,7 +1550,7 @@ class GenerationTesterMixin:
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# If it doesn't support cache, pass the test
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if not hasattr(config, "use_cache"):
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self.skipTest(reason="This model doesn't support caching")
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self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
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model = model_class(config).to(torch_device)
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if "use_cache" not in inputs:
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@@ -1745,7 +1746,7 @@ class GenerationTesterMixin:
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config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
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if not hasattr(config, "use_cache"):
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self.skipTest(reason="This model doesn't support caching")
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self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
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# Let's make it always:
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# 1. use cache (for obvious reasons)
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@@ -1845,12 +1846,13 @@ class GenerationTesterMixin:
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input_ids, attention_mask=attention_mask, **generation_kwargs, **inputs_dict
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)
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set_seed(seed)
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num_hidden_layers = config.get_text_config().num_hidden_layers
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if config.is_encoder_decoder:
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cache_cls = EncoderDecoderCache
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past_key_values = cache_cls(DynamicCache(), DynamicCache())
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past_key_values = cache_cls(DynamicCache(num_hidden_layers), DynamicCache(num_hidden_layers))
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else:
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cache_cls = DynamicCache
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past_key_values = cache_cls()
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past_key_values = cache_cls(num_hidden_layers)
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new_results = model.generate(
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input_ids,
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attention_mask=attention_mask,
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@@ -1870,23 +1872,27 @@ class GenerationTesterMixin:
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new_cache_converted = new_results.past_key_values.to_legacy_cache()
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for layer_idx in range(len(legacy_cache)):
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for kv_idx in range(len(legacy_cache[layer_idx])):
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self.assertTrue(
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torch.allclose(
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legacy_cache[layer_idx][kv_idx],
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new_cache_converted[layer_idx][kv_idx],
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# TODO: @raushan, please look into this for new cache format
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if legacy_cache[layer_idx][kv_idx] != []:
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self.assertTrue(
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torch.allclose(
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legacy_cache[layer_idx][kv_idx],
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new_cache_converted[layer_idx][kv_idx],
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)
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)
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)
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new_cache = new_results.past_key_values
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legacy_cache_converted = cache_cls.from_legacy_cache(legacy_results.past_key_values)
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for layer_idx in range(len(new_cache)):
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for kv_idx in range(len(new_cache[layer_idx])):
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self.assertTrue(
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torch.allclose(
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new_cache[layer_idx][kv_idx],
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legacy_cache_converted[layer_idx][kv_idx],
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# TODO: @raushan, please look into this for new cache format
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if new_cache[layer_idx][kv_idx] != []:
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self.assertTrue(
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torch.allclose(
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new_cache[layer_idx][kv_idx],
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legacy_cache_converted[layer_idx][kv_idx],
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)
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)
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)
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@pytest.mark.generate
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def test_generate_with_static_cache(self):
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@@ -1960,8 +1966,12 @@ class GenerationTesterMixin:
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# passing past key values of different type should raise Error
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with self.assertRaises(ValueError):
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num_hidden_layers = config.get_text_config().num_hidden_layers
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model.generate(
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input_ids, attention_mask=attention_mask, past_key_valyes=DynamicCache(), **generation_kwargs
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input_ids,
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attention_mask=attention_mask,
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past_key_valyes=DynamicCache(num_hidden_layers),
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**generation_kwargs,
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)
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# setting incorrect cache_config args should raise an Error, i.e. nbits=60 does not make sense
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@@ -2004,6 +2014,12 @@ class GenerationTesterMixin:
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"max_new_tokens": 10,
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}
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max_cache_len = input_ids.shape[1] + generation_kwargs["max_new_tokens"]
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config = config.get_text_config()
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past_key_values = StaticCache(
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config, batch_size=half_batch_size, max_cache_len=max_cache_len, device=torch_device
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)
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for model_inputs in input_ids_sets:
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# eager dynamic cache
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output_dynamic = model.generate(model_inputs, **generation_kwargs)
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@@ -2013,7 +2029,9 @@ class GenerationTesterMixin:
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compiled_generate = torch.compile(model.generate, fullgraph=True, mode="reduce-overhead")
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generation_config = copy.deepcopy(model.generation_config)
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generation_config.update(**generation_kwargs)
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output_compiled = compiled_generate(model_inputs, generation_config=generation_config)
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output_compiled = compiled_generate(
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model_inputs, generation_config=generation_config, past_key_values=past_key_values
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)
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self.assertListEqual(output_dynamic.tolist(), output_compiled.tolist())
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@pytest.mark.generate
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