F.scaled_dot_product_attention support (#26572)
* add sdpa * wip * cleaning * add ref * yet more cleaning * and more :) * wip llama * working llama * add output_attentions=True support * bigcode sdpa support * fixes * gpt-bigcode support, require torch>=2.1.1 * add falcon support * fix conflicts falcon * style * fix attention_mask definition * remove output_attentions from attnmaskconverter * support whisper without removing any Copied from statement * fix mbart default to eager renaming * fix typo in falcon * fix is_causal in SDPA * check is_flash_attn_2_available in the models init as well in case the model is not initialized through from_pretrained * add warnings when falling back on the manual implementation * precise doc * wip replace _flash_attn_enabled by config.attn_implementation * fix typo * add tests * style * add a copy.deepcopy on the config in from_pretrained, as we do not want to modify it inplace * obey to config.attn_implementation if a config is passed in from_pretrained * fix is_torch_sdpa_available when torch is not installed * remove dead code * Update src/transformers/modeling_attn_mask_utils.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/modeling_attn_mask_utils.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/modeling_attn_mask_utils.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/modeling_attn_mask_utils.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/modeling_attn_mask_utils.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/models/bart/modeling_bart.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * remove duplicate pretraining_tp code * add dropout in llama * precise comment on attn_mask * add fmt: off for _unmask_unattended docstring * precise num_masks comment * nuke pretraining_tp in LlamaSDPAAttention following Arthur's suggestion * cleanup modeling_utils * backward compatibility * fix style as requested * style * improve documentation * test pass * style * add _unmask_unattended tests * skip meaningless tests for idefics * hard_check SDPA requirements when specifically requested * standardize the use if XXX_ATTENTION_CLASSES * fix SDPA bug with mem-efficient backend on CUDA when using fp32 * fix test * rely on SDPA is_causal parameter to handle the causal mask in some cases * fix FALCON_ATTENTION_CLASSES * remove _flash_attn_2_enabled occurences * fix test * add OPT to the list of supported flash models * improve test * properly test on different SDPA backends, on different dtypes & properly handle separately the pad tokens in the test * remove remaining _flash_attn_2_enabled occurence * Update src/transformers/modeling_utils.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/modeling_utils.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/modeling_utils.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/modeling_attn_mask_utils.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update docs/source/en/perf_infer_gpu_one.md Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * remove use_attn_implementation * fix docstring & slight bug * make attn_implementation internal (_attn_implementation) * typos * fix tests * deprecate use_flash_attention_2=True * fix test * add back llama that was removed by mistake * fix tests * remove _flash_attn_2_enabled occurences bis * add check & test that passed attn_implementation is valid * fix falcon torchscript export * fix device of mask in tests * add tip about torch.jit.trace and move bt doc below sdpa * fix parameterized.expand order * move tests from test_modeling_attn_mask_utils to test_modeling_utils as a relevant test class is already there * update sdpaattention class with the new cache * Update src/transformers/configuration_utils.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/models/bark/modeling_bark.py * address review comments * WIP torch.jit.trace fix. left: test both eager & sdpa * add test for torch.jit.trace for both eager/sdpa * fix falcon with torch==2.0 that needs to use sdpa * fix doc * hopefully last fix * fix key_value_length that has no default now in mask converter * is it flacky? * fix speculative decoding bug * tests do pass * fix following #27907 --------- Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
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fxmarty
@@ -387,9 +387,9 @@ class MistralModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMi
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model = model_class.from_pretrained(
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tmpdirname, torch_dtype=torch.float16, use_flash_attention_2=False, low_cpu_mem_usage=True
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).to(torch_device)
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model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, low_cpu_mem_usage=True).to(
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torch_device
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)
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dummy_input = torch.LongTensor([[0, 2, 3, 4], [0, 2, 3, 4]]).to(torch_device)
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dummy_attention_mask = torch.LongTensor([[1, 1, 1, 1], [1, 1, 1, 0]]).to(torch_device)
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@@ -397,7 +397,10 @@ class MistralModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMi
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model.generate(dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False)
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model = model_class.from_pretrained(
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tmpdirname, torch_dtype=torch.float16, use_flash_attention_2=True, low_cpu_mem_usage=True
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tmpdirname,
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torch_dtype=torch.float16,
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attn_implementation="flash_attention_2",
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low_cpu_mem_usage=True,
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).to(torch_device)
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with self.assertRaises(ValueError):
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@@ -437,7 +440,7 @@ class MistralModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMi
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model = model_class.from_pretrained(
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tmpdirname,
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torch_dtype=torch.float16,
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use_flash_attention_2=True,
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attn_implementation="flash_attention_2",
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low_cpu_mem_usage=True,
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).to(torch_device)
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@@ -507,7 +510,7 @@ class MistralIntegrationTest(unittest.TestCase):
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"mistralai/Mistral-7B-v0.1",
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device_map="auto",
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load_in_4bit=True,
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use_flash_attention_2=True,
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attn_implementation="flash_attention_2",
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)
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input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device)
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generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0)
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