[Compile] Only test compiling model forward pass (#35658)
* rename test to only compile forward! * style emu
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@@ -2042,16 +2042,10 @@ class GenerationTesterMixin:
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with self.assertRaises(ValueError):
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model.generate(**generation_kwargs, **inputs_dict)
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@parameterized.expand(
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[
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("forward_only", False), # TODO (@joao): a few models failing. After fixed, this should not be "@slow"
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("end_to_end", True), # TODO (@joao): end-to-end compilation is broken with torch 2.5+, explore and fix
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]
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)
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@pytest.mark.generate
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@require_torch_gpu
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@slow
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def test_generate_compile(self, _, end_to_end):
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def test_generate_compile_model_forward(self):
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"""
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Tests that `.generate` is compatible with torch.compile without graph breaks, keeping the same results. Tests
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end-to-end compilation and forward pass compilation only.
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@@ -2061,14 +2055,7 @@ class GenerationTesterMixin:
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if not model_class._supports_static_cache:
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self.skipTest("This model doesn't support static cache")
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# TODO (joao) -- fix and enable me :)
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if end_to_end and any(model_name in model_class.__name__.lower() for model_name in ["whisper"]):
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self.skipTest("whisper model end-to-end generate compile not yet supported")
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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# TODO (joao) -- fix and enable me :)
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if end_to_end and config.is_encoder_decoder:
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self.skipTest("Encoder-decoder model end-to-end generate compile not yet supported")
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model = model_class(config).to(torch_device)
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model.eval() # otherwise `self.training` is `True` -- this flag is used at attn mask creation time
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@@ -2084,10 +2071,8 @@ class GenerationTesterMixin:
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"max_new_tokens": 10,
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"return_dict_in_generate": True,
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"output_scores": True,
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"cache_implementation": "static",
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}
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# end-to-end works best with dynamic cache, forward compilation works best with static cache
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if not end_to_end:
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generation_kwargs["cache_implementation"] = "static"
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# get eager + dynamic cache results for future comparison
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dynamic_outputs = []
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@@ -2098,10 +2083,8 @@ class GenerationTesterMixin:
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generation_config = copy.deepcopy(model.generation_config)
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generation_config.update(**generation_kwargs)
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torch.compiler.reset()
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if end_to_end:
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model.generate = torch.compile(model.generate, fullgraph=True, mode="reduce-overhead")
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else:
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model.forward = torch.compile(model.forward, fullgraph=True, mode="reduce-overhead")
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model.forward = torch.compile(model.forward, fullgraph=True, mode="reduce-overhead")
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compiled_outputs = []
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for model_inputs in input_ids_sets:
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