Several fixes for Gemma3n (#39135)
* remove the skips * fix the epsilon to a small value (does not make sense otherwise) * safeguard * overload test_eager_matches_sdpa * Update test_modeling_common.py * skip appropriate tests * correct no_split_layer * fix all devices issue * fix backward * fix
This commit is contained in:
Cyril Vallez
@@ -156,6 +156,334 @@ TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION = [
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] + [("fp32_pad_left_output_attentions", "fp32", "left", True, True, False)]
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def _test_eager_matches_sdpa_inference(
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self,
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name,
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torch_dtype,
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padding_side,
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use_attention_mask,
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output_attentions,
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enable_kernels,
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atols=None,
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rtols=None,
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):
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"""
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This test is written as a regular function to be able to overload it easily with different tolerances.
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Otherwise, `paramterezie.expand` prevents it as it removes the original function from the namespace.
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"""
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# TODO: we shouldn't need to do this skip, i.e. the test would be composable from the model tester. CLIP-like
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# models have a custom mixin, which we detect to skip this test.
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if any(".CLIPModelTesterMixin" in str(base) for base in self.__class__.__bases__):
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self.skipTest(reason="CLIP-like models have a different `test_eager_matches_sdpa_inference`")
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if not self.has_attentions:
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self.skipTest(reason="Model architecture does not support attentions")
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if not self.all_model_classes[0]._supports_sdpa:
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self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
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# convert shorthand name to torch.dtype
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if torch_dtype == "fp16":
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torch_dtype = torch.float16
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elif torch_dtype == "bf16":
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torch_dtype = torch.bfloat16
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elif torch_dtype == "fp32":
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torch_dtype = torch.float32
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if not is_torch_fp16_available_on_device(torch_device) and torch_dtype == torch.float16:
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self.skipTest(f"float16 not supported on {torch_device} (on the specific device currently used)")
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if not is_torch_bf16_available_on_device(torch_device) and torch_dtype == torch.bfloat16:
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self.skipTest(
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f"bfloat16 not supported on {torch_device} (on the specific device currently used, e.g. Nvidia T4 GPU)"
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)
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# Dictionary of tolerances for eager <> sdpa tests. Key = (device, sdpa_kernels_enabled, dtype)
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if atols is None:
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atols = {
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("cpu", False, torch.float32): 1e-6,
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("cpu", False, torch.float16): 5e-3,
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("cpu", False, torch.bfloat16): 1e-2,
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("cpu", True, torch.float32): 1e-6,
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("cpu", True, torch.float16): 5e-3,
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("cpu", True, torch.bfloat16): 1e-2,
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("cuda", False, torch.float32): 1e-6,
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("cuda", False, torch.bfloat16): 1e-2,
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("cuda", False, torch.float16): 5e-3,
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("cuda", True, torch.float32): 1e-6,
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("cuda", True, torch.bfloat16): 1e-2,
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("cuda", True, torch.float16): 5e-3,
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}
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if rtols is None:
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rtols = {
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("cpu", False, torch.float32): 1e-4,
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("cpu", False, torch.float16): 5e-3,
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("cpu", False, torch.bfloat16): 1e-2,
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("cpu", True, torch.float32): 1e-4,
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("cpu", True, torch.float16): 5e-3,
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("cpu", True, torch.bfloat16): 1e-2,
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("cuda", False, torch.float32): 1e-4,
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("cuda", False, torch.bfloat16): 1e-2,
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("cuda", False, torch.float16): 5e-3,
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("cuda", True, torch.float32): 1e-4,
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("cuda", True, torch.bfloat16): 3e-2, # (different from others)
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("cuda", True, torch.float16): 5e-3,
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}
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set_model_tester_for_less_flaky_test(self)
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for model_class in self.all_model_classes:
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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set_config_for_less_flaky_test(config)
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model = model_class(config)
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# TODO: standardize the interfaces for musicgen models, see other todo in this test
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if model.__class__.__name__ == "MusicgenMelodyForConditionalGeneration":
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is_encoder_decoder = True
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else:
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is_encoder_decoder = model.config.is_encoder_decoder
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model_from_pretrained_kwargs = {
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"pretrained_model_name_or_path": tmpdirname,
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"torch_dtype": torch_dtype,
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}
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if hasattr(config, "use_mask_token") or "use_mask_token" in inspect.signature(model.__init__).parameters:
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model_from_pretrained_kwargs["use_mask_token"] = True
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# TODO: remove this try/except, models should have a shared API
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try:
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model_sdpa = model_class.from_pretrained(**model_from_pretrained_kwargs, attn_implementation="sdpa")
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except ValueError:
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model_sdpa = model_class.from_pretrained(**model_from_pretrained_kwargs)
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model_sdpa = model_sdpa.eval().to(torch_device, dtype=torch_dtype)
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model_eager = model_class.from_pretrained(**model_from_pretrained_kwargs, attn_implementation="eager")
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model_eager = model_eager.eval().to(torch_device, dtype=torch_dtype)
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set_model_for_less_flaky_test(model_eager)
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set_model_for_less_flaky_test(model_sdpa)
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can_output_attn = "output_attentions" in inspect.signature(model_sdpa.forward).parameters
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if not (self.has_attentions and can_output_attn) and output_attentions:
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self.skipTest(reason="Model does not support output_attentions")
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# TODO: if we can also check with `batch_size=1` without being flaky?
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for batch_size in [7]:
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# musicgen decoder models; TODO: find better abstraction
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if (
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model.__class__.__name__.startswith("Musicgen")
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and hasattr(self.model_tester, "num_codebooks")
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and not hasattr(model_eager, "text_encoder")
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):
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input_data_batch_size = batch_size * self.model_tester.num_codebooks
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else:
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input_data_batch_size = batch_size
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processed_inputs = {}
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processed_inputs[model.main_input_name] = inputs_dict[model.main_input_name]
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for key in getattr(self, "additional_model_inputs", []):
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# Some models don't have all `additional_model_inputs`, especially when we
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# craft cases to test model in different settings
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if key in inputs_dict:
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processed_inputs[key] = inputs_dict[key]
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for key, value in processed_inputs.items():
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if torch.is_floating_point(value):
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value = value.to(torch_dtype)
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# extend value to have at least `input_data_batch_size` elements
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if value.shape[0] < input_data_batch_size:
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size = (input_data_batch_size - value.shape[0], *value.shape[1:])
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if torch.is_floating_point(value):
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extension = torch.rand(size=size, dtype=value.dtype, device=torch_device)
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else:
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extension = torch.randint(high=5, size=size, dtype=value.dtype, device=torch_device)
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value = torch.cat((value, extension), dim=0).to(torch_device)
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processed_inputs[key] = value[:input_data_batch_size]
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if not use_attention_mask:
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dummy_attention_mask = None
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else:
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dummy_attention_mask = inputs_dict.get("attention_mask", None)
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if dummy_attention_mask is None:
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if is_encoder_decoder:
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seqlen = inputs_dict.get("decoder_input_ids", processed_inputs[model.main_input_name]).shape[
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-1
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]
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else:
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seqlen = processed_inputs[model.main_input_name].shape[-1]
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dummy_attention_mask = torch.ones(batch_size, seqlen).to(torch.int64).to(torch_device)
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# extend dummy_attention_mask to have at least `batch_size` elements
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if dummy_attention_mask.shape[0] < batch_size:
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size = (batch_size - dummy_attention_mask.shape[0], *dummy_attention_mask.shape[1:])
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extension = torch.ones(size=size, dtype=dummy_attention_mask.dtype, device=torch_device)
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dummy_attention_mask = torch.cat((dummy_attention_mask, extension), dim=0)
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dummy_attention_mask = dummy_attention_mask[:batch_size].to(torch_device)
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dummy_attention_mask[:] = 1
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if padding_side == "left":
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dummy_attention_mask[-1, :2] = 0
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dummy_attention_mask[-1, 2:] = 1
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elif padding_side == "right":
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dummy_attention_mask[-1, -2:] = 0
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dummy_attention_mask[-1, :-2] = 1
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if is_encoder_decoder:
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# musicgen encoder-decoder models; TODO: find better abstraction
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if model.__class__.__name__.startswith("Musicgen") and hasattr(self.model_tester, "num_codebooks"):
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input_data_batch_size = batch_size * self.model_tester.num_codebooks
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else:
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input_data_batch_size = batch_size
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decoder_input_ids = inputs_dict.get("decoder_input_ids", processed_inputs[model.main_input_name])
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decoder_input_ids = decoder_input_ids[:input_data_batch_size]
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if decoder_input_ids.shape[0] != input_data_batch_size:
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extension = torch.ones(
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input_data_batch_size - decoder_input_ids.shape[0],
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*decoder_input_ids.shape[1:],
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dtype=decoder_input_ids.dtype,
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device=torch_device,
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)
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decoder_input_ids = torch.cat((decoder_input_ids, extension), dim=0)
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decoder_input_ids = decoder_input_ids.to(torch_device)
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# TODO: never an `attention_mask` arg here?
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processed_inputs.update(
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{
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"decoder_input_ids": decoder_input_ids,
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"decoder_attention_mask": dummy_attention_mask,
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"output_hidden_states": True,
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}
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)
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else:
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processed_inputs.update(
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{
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"output_hidden_states": True,
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}
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)
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# Otherwise fails for e.g. WhisperEncoderModel
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if "attention_mask" in inspect.signature(model_eager.forward).parameters:
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processed_inputs["attention_mask"] = dummy_attention_mask
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if self.has_attentions and "output_attentions" in inspect.signature(model_sdpa.forward).parameters:
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processed_inputs["output_attentions"] = output_attentions
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if "bool_masked_pos" in inspect.signature(model_eager.forward).parameters:
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dummy_mask = torch.ones((self.model_tester.num_masks,))
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# In case of additional token (like class) we define a custom `mask_length`
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if hasattr(self.model_tester, "mask_length"):
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mask_length = self.model_tester.mask_length - dummy_mask.size(0)
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else:
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mask_length = self.model_tester.seq_length - dummy_mask.size(0)
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dummy_mask = torch.cat([dummy_mask, torch.zeros(mask_length)])
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dummy_bool_masked_pos = dummy_mask.expand(batch_size, -1).bool()
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processed_inputs["bool_masked_pos"] = dummy_bool_masked_pos.to(torch_device)
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if "noise" in inspect.signature(model_eager.forward).parameters:
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np.random.seed(2)
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num_patches = int((self.model_tester.image_size // self.model_tester.patch_size) ** 2)
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noise = np.random.uniform(size=(batch_size, num_patches))
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processed_inputs["noise"] = torch.from_numpy(noise)
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# TODO: test gradients as well (& for FA2 as well!)
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with torch.no_grad():
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with sdpa_kernel(
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enable_flash=enable_kernels,
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enable_math=True,
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enable_mem_efficient=enable_kernels,
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):
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prepared_inputs = self._prepare_for_class(processed_inputs, model_class)
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prepared_inputs = {
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k: v.to(torch_device) if isinstance(v, torch.Tensor) else v for k, v in prepared_inputs.items()
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}
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outputs_eager = model_eager(**prepared_inputs)
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outputs_sdpa = model_sdpa(**prepared_inputs)
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if "logits_per_text" in outputs_eager:
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key = "logits_per_text"
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elif "vision_hidden_states" in outputs_eager:
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key = "vision_hidden_states"
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elif "audio_values" in outputs_eager:
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key = "audio_values"
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elif "decoder_hidden_states" in outputs_eager:
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key = "decoder_hidden_states"
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elif "logits" in outputs_eager and "Classification" in model_class.__name__:
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key = "logits"
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elif "language_model_outputs" in outputs_eager and "blip" in model_class.__name__.lower():
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outputs_eager = outputs_eager["language_model_outputs"]
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outputs_sdpa = outputs_sdpa["language_model_outputs"]
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key = "hidden_states" if "hidden_states" in outputs_eager else "decoder_hidden_states"
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else:
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key = "hidden_states"
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# TODO: rename logits -> hidden_states
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logits_eager = outputs_eager[key]
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logits_sdpa = outputs_sdpa[key]
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if key in ["vision_hidden_states", "decoder_hidden_states", "hidden_states"]:
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logits_eager = logits_eager[-1]
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logits_sdpa = logits_sdpa[-1]
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if key == "logits_per_text":
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nan_mask = torch.isnan(logits_eager)
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logits_eager[nan_mask] = 0
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logits_sdpa[nan_mask] = 0
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if torch_device in ["cpu", "cuda"]:
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atol = atols[torch_device, enable_kernels, torch_dtype]
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rtol = rtols[torch_device, enable_kernels, torch_dtype]
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elif torch_device == "hpu":
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atol = atols["cuda", enable_kernels, torch_dtype]
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rtol = rtols["cuda", enable_kernels, torch_dtype]
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elif torch_device == "xpu":
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# As of PyTorch 2.5 XPU backend supports only torch.nn.attention.SDPBackend.MATH
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# which is implemented on PyTorch level using aten operators and is
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# device agnostic with respect to implementation of each aten operator.
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atol = atols["cuda", False, torch_dtype]
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rtol = rtols["cuda", False, torch_dtype]
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else:
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atol = 1e-7
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rtol = 1e-4
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# Masked tokens output slightly deviates - we don't mind that.
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if use_attention_mask:
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_logits_sdpa = torch.zeros_like(input=logits_sdpa)
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_logits_eager = torch.zeros_like(input=logits_eager)
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_logits_sdpa[:-1] = logits_sdpa[:-1]
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_logits_eager[:-1] = logits_eager[:-1]
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if padding_side == "left":
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_logits_sdpa[-1:, 2:] = logits_sdpa[-1:, 2:]
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_logits_eager[-1:, 2:] = logits_eager[-1:, 2:]
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elif padding_side == "right":
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_logits_sdpa[-1:, 2:] = logits_sdpa[-1:, :-2]
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_logits_eager[-1:, 2:] = logits_eager[-1:, :-2]
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logits_sdpa = _logits_sdpa
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logits_eager = _logits_eager
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results = [
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torch.allclose(_logits_sdpa, _logits_eager, atol=atol, rtol=rtol)
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for (_logits_sdpa, _logits_eager) in zip(logits_sdpa, logits_eager)
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]
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# If 80% batch elements have matched results, it's fine
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if np.mean(results) < 0.8:
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mean_relative_diff = ((logits_sdpa - logits_eager).abs() / (logits_eager.abs() + 1e-12)).mean()
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raise ValueError(
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f"mean relative difference for {key}: {mean_relative_diff:.3e}, torch atol = {atol}, torch rtol = "
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f"{rtol}"
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)
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def _config_zero_init(config):
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configs_no_init = copy.deepcopy(config)
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for key in configs_no_init.__dict__.keys():
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@@ -3405,321 +3733,9 @@ class ModelTesterMixin:
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def test_eager_matches_sdpa_inference(
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self, name, torch_dtype, padding_side, use_attention_mask, output_attentions, enable_kernels
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):
|
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# TODO: we shouldn't need to do this skip, i.e. the test would be composable from the model tester. CLIP-like
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# models have a custom mixin, which we detect to skip this test.
|
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if any(".CLIPModelTesterMixin" in str(base) for base in self.__class__.__bases__):
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self.skipTest(reason="CLIP-like models have a different `test_eager_matches_sdpa_inference`")
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if not self.has_attentions:
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self.skipTest(reason="Model architecture does not support attentions")
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if not self.all_model_classes[0]._supports_sdpa:
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self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
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# convert shorthand name to torch.dtype
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if torch_dtype == "fp16":
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torch_dtype = torch.float16
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elif torch_dtype == "bf16":
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torch_dtype = torch.bfloat16
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elif torch_dtype == "fp32":
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torch_dtype = torch.float32
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if not is_torch_fp16_available_on_device(torch_device) and torch_dtype == torch.float16:
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self.skipTest(f"float16 not supported on {torch_device} (on the specific device currently used)")
|
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|
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if not is_torch_bf16_available_on_device(torch_device) and torch_dtype == torch.bfloat16:
|
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self.skipTest(
|
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f"bfloat16 not supported on {torch_device} (on the specific device currently used, e.g. Nvidia T4 GPU)"
|
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)
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# Dictionary of tolerances for eager <> sdpa tests. Key = (device, sdpa_kernels_enabled, dtype)
|
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atols = {
|
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("cpu", False, torch.float32): 1e-6,
|
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("cpu", False, torch.float16): 5e-3,
|
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("cpu", False, torch.bfloat16): 1e-2,
|
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("cpu", True, torch.float32): 1e-6,
|
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("cpu", True, torch.float16): 5e-3,
|
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("cpu", True, torch.bfloat16): 1e-2,
|
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("cuda", False, torch.float32): 1e-6,
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("cuda", False, torch.bfloat16): 1e-2,
|
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("cuda", False, torch.float16): 5e-3,
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("cuda", True, torch.float32): 1e-6,
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("cuda", True, torch.bfloat16): 1e-2,
|
||||
("cuda", True, torch.float16): 5e-3,
|
||||
}
|
||||
rtols = {
|
||||
("cpu", False, torch.float32): 1e-4,
|
||||
("cpu", False, torch.float16): 5e-3,
|
||||
("cpu", False, torch.bfloat16): 1e-2,
|
||||
("cpu", True, torch.float32): 1e-4,
|
||||
("cpu", True, torch.float16): 5e-3,
|
||||
("cpu", True, torch.bfloat16): 1e-2,
|
||||
("cuda", False, torch.float32): 1e-4,
|
||||
("cuda", False, torch.bfloat16): 1e-2,
|
||||
("cuda", False, torch.float16): 5e-3,
|
||||
("cuda", True, torch.float32): 1e-4,
|
||||
("cuda", True, torch.bfloat16): 3e-2, # (different from others)
|
||||
("cuda", True, torch.float16): 5e-3,
|
||||
}
|
||||
|
||||
set_model_tester_for_less_flaky_test(self)
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
set_config_for_less_flaky_test(config)
|
||||
model = model_class(config)
|
||||
# TODO: standardize the interfaces for musicgen models, see other todo in this test
|
||||
if model.__class__.__name__ == "MusicgenMelodyForConditionalGeneration":
|
||||
is_encoder_decoder = True
|
||||
else:
|
||||
is_encoder_decoder = model.config.is_encoder_decoder
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
model.save_pretrained(tmpdirname)
|
||||
model_from_pretrained_kwargs = {
|
||||
"pretrained_model_name_or_path": tmpdirname,
|
||||
"torch_dtype": torch_dtype,
|
||||
}
|
||||
|
||||
if (
|
||||
hasattr(config, "use_mask_token")
|
||||
or "use_mask_token" in inspect.signature(model.__init__).parameters
|
||||
):
|
||||
model_from_pretrained_kwargs["use_mask_token"] = True
|
||||
|
||||
# TODO: remove this try/except, models should have a shared API
|
||||
try:
|
||||
model_sdpa = model_class.from_pretrained(
|
||||
**model_from_pretrained_kwargs, attn_implementation="sdpa"
|
||||
)
|
||||
except ValueError:
|
||||
model_sdpa = model_class.from_pretrained(**model_from_pretrained_kwargs)
|
||||
model_sdpa = model_sdpa.eval().to(torch_device, dtype=torch_dtype)
|
||||
|
||||
model_eager = model_class.from_pretrained(**model_from_pretrained_kwargs, attn_implementation="eager")
|
||||
model_eager = model_eager.eval().to(torch_device, dtype=torch_dtype)
|
||||
|
||||
set_model_for_less_flaky_test(model_eager)
|
||||
set_model_for_less_flaky_test(model_sdpa)
|
||||
|
||||
can_output_attn = "output_attentions" in inspect.signature(model_sdpa.forward).parameters
|
||||
if not (self.has_attentions and can_output_attn) and output_attentions:
|
||||
self.skipTest(reason="Model does not support output_attentions")
|
||||
|
||||
# TODO: if we can also check with `batch_size=1` without being flaky?
|
||||
for batch_size in [7]:
|
||||
# musicgen decoder models; TODO: find better abstraction
|
||||
if (
|
||||
model.__class__.__name__.startswith("Musicgen")
|
||||
and hasattr(self.model_tester, "num_codebooks")
|
||||
and not hasattr(model_eager, "text_encoder")
|
||||
):
|
||||
input_data_batch_size = batch_size * self.model_tester.num_codebooks
|
||||
else:
|
||||
input_data_batch_size = batch_size
|
||||
|
||||
processed_inputs = {}
|
||||
processed_inputs[model.main_input_name] = inputs_dict[model.main_input_name]
|
||||
|
||||
for key in getattr(self, "additional_model_inputs", []):
|
||||
# Some models don't have all `additional_model_inputs`, especially when we
|
||||
# craft cases to test model in different settings
|
||||
if key in inputs_dict:
|
||||
processed_inputs[key] = inputs_dict[key]
|
||||
|
||||
for key, value in processed_inputs.items():
|
||||
if torch.is_floating_point(value):
|
||||
value = value.to(torch_dtype)
|
||||
|
||||
# extend value to have at least `input_data_batch_size` elements
|
||||
if value.shape[0] < input_data_batch_size:
|
||||
size = (input_data_batch_size - value.shape[0], *value.shape[1:])
|
||||
if torch.is_floating_point(value):
|
||||
extension = torch.rand(size=size, dtype=value.dtype, device=torch_device)
|
||||
else:
|
||||
extension = torch.randint(high=5, size=size, dtype=value.dtype, device=torch_device)
|
||||
value = torch.cat((value, extension), dim=0).to(torch_device)
|
||||
|
||||
processed_inputs[key] = value[:input_data_batch_size]
|
||||
|
||||
if not use_attention_mask:
|
||||
dummy_attention_mask = None
|
||||
else:
|
||||
dummy_attention_mask = inputs_dict.get("attention_mask", None)
|
||||
if dummy_attention_mask is None:
|
||||
if is_encoder_decoder:
|
||||
seqlen = inputs_dict.get(
|
||||
"decoder_input_ids", processed_inputs[model.main_input_name]
|
||||
).shape[-1]
|
||||
else:
|
||||
seqlen = processed_inputs[model.main_input_name].shape[-1]
|
||||
dummy_attention_mask = torch.ones(batch_size, seqlen).to(torch.int64).to(torch_device)
|
||||
|
||||
# extend dummy_attention_mask to have at least `batch_size` elements
|
||||
if dummy_attention_mask.shape[0] < batch_size:
|
||||
size = (batch_size - dummy_attention_mask.shape[0], *dummy_attention_mask.shape[1:])
|
||||
extension = torch.ones(size=size, dtype=dummy_attention_mask.dtype, device=torch_device)
|
||||
dummy_attention_mask = torch.cat((dummy_attention_mask, extension), dim=0)
|
||||
|
||||
dummy_attention_mask = dummy_attention_mask[:batch_size].to(torch_device)
|
||||
|
||||
dummy_attention_mask[:] = 1
|
||||
if padding_side == "left":
|
||||
dummy_attention_mask[-1, :2] = 0
|
||||
dummy_attention_mask[-1, 2:] = 1
|
||||
elif padding_side == "right":
|
||||
dummy_attention_mask[-1, -2:] = 0
|
||||
dummy_attention_mask[-1, :-2] = 1
|
||||
|
||||
if is_encoder_decoder:
|
||||
# musicgen encoder-decoder models; TODO: find better abstraction
|
||||
if model.__class__.__name__.startswith("Musicgen") and hasattr(self.model_tester, "num_codebooks"):
|
||||
input_data_batch_size = batch_size * self.model_tester.num_codebooks
|
||||
else:
|
||||
input_data_batch_size = batch_size
|
||||
|
||||
decoder_input_ids = inputs_dict.get("decoder_input_ids", processed_inputs[model.main_input_name])
|
||||
decoder_input_ids = decoder_input_ids[:input_data_batch_size]
|
||||
if decoder_input_ids.shape[0] != input_data_batch_size:
|
||||
extension = torch.ones(
|
||||
input_data_batch_size - decoder_input_ids.shape[0],
|
||||
*decoder_input_ids.shape[1:],
|
||||
dtype=decoder_input_ids.dtype,
|
||||
device=torch_device,
|
||||
)
|
||||
decoder_input_ids = torch.cat((decoder_input_ids, extension), dim=0)
|
||||
decoder_input_ids = decoder_input_ids.to(torch_device)
|
||||
|
||||
# TODO: never an `attention_mask` arg here?
|
||||
processed_inputs.update(
|
||||
{
|
||||
"decoder_input_ids": decoder_input_ids,
|
||||
"decoder_attention_mask": dummy_attention_mask,
|
||||
"output_hidden_states": True,
|
||||
}
|
||||
)
|
||||
else:
|
||||
processed_inputs.update(
|
||||
{
|
||||
"output_hidden_states": True,
|
||||
}
|
||||
)
|
||||
|
||||
# Otherwise fails for e.g. WhisperEncoderModel
|
||||
if "attention_mask" in inspect.signature(model_eager.forward).parameters:
|
||||
processed_inputs["attention_mask"] = dummy_attention_mask
|
||||
|
||||
if self.has_attentions and "output_attentions" in inspect.signature(model_sdpa.forward).parameters:
|
||||
processed_inputs["output_attentions"] = output_attentions
|
||||
if "bool_masked_pos" in inspect.signature(model_eager.forward).parameters:
|
||||
dummy_mask = torch.ones((self.model_tester.num_masks,))
|
||||
|
||||
# In case of additional token (like class) we define a custom `mask_length`
|
||||
if hasattr(self.model_tester, "mask_length"):
|
||||
mask_length = self.model_tester.mask_length - dummy_mask.size(0)
|
||||
else:
|
||||
mask_length = self.model_tester.seq_length - dummy_mask.size(0)
|
||||
dummy_mask = torch.cat([dummy_mask, torch.zeros(mask_length)])
|
||||
dummy_bool_masked_pos = dummy_mask.expand(batch_size, -1).bool()
|
||||
processed_inputs["bool_masked_pos"] = dummy_bool_masked_pos.to(torch_device)
|
||||
|
||||
if "noise" in inspect.signature(model_eager.forward).parameters:
|
||||
np.random.seed(2)
|
||||
num_patches = int((self.model_tester.image_size // self.model_tester.patch_size) ** 2)
|
||||
noise = np.random.uniform(size=(batch_size, num_patches))
|
||||
processed_inputs["noise"] = torch.from_numpy(noise)
|
||||
|
||||
# TODO: test gradients as well (& for FA2 as well!)
|
||||
with torch.no_grad():
|
||||
with sdpa_kernel(
|
||||
enable_flash=enable_kernels,
|
||||
enable_math=True,
|
||||
enable_mem_efficient=enable_kernels,
|
||||
):
|
||||
prepared_inputs = self._prepare_for_class(processed_inputs, model_class)
|
||||
prepared_inputs = {
|
||||
k: v.to(torch_device) if isinstance(v, torch.Tensor) else v
|
||||
for k, v in prepared_inputs.items()
|
||||
}
|
||||
outputs_eager = model_eager(**prepared_inputs)
|
||||
outputs_sdpa = model_sdpa(**prepared_inputs)
|
||||
|
||||
if "logits_per_text" in outputs_eager:
|
||||
key = "logits_per_text"
|
||||
elif "vision_hidden_states" in outputs_eager:
|
||||
key = "vision_hidden_states"
|
||||
elif "audio_values" in outputs_eager:
|
||||
key = "audio_values"
|
||||
elif "decoder_hidden_states" in outputs_eager:
|
||||
key = "decoder_hidden_states"
|
||||
elif "logits" in outputs_eager and "Classification" in model_class.__name__:
|
||||
key = "logits"
|
||||
elif "language_model_outputs" in outputs_eager and "blip" in model_class.__name__.lower():
|
||||
outputs_eager = outputs_eager["language_model_outputs"]
|
||||
outputs_sdpa = outputs_sdpa["language_model_outputs"]
|
||||
key = "hidden_states" if "hidden_states" in outputs_eager else "decoder_hidden_states"
|
||||
else:
|
||||
key = "hidden_states"
|
||||
|
||||
# TODO: rename logits -> hidden_states
|
||||
logits_eager = outputs_eager[key]
|
||||
logits_sdpa = outputs_sdpa[key]
|
||||
|
||||
if key in ["vision_hidden_states", "decoder_hidden_states", "hidden_states"]:
|
||||
logits_eager = logits_eager[-1]
|
||||
logits_sdpa = logits_sdpa[-1]
|
||||
|
||||
if key == "logits_per_text":
|
||||
nan_mask = torch.isnan(logits_eager)
|
||||
logits_eager[nan_mask] = 0
|
||||
logits_sdpa[nan_mask] = 0
|
||||
|
||||
if torch_device in ["cpu", "cuda"]:
|
||||
atol = atols[torch_device, enable_kernels, torch_dtype]
|
||||
rtol = rtols[torch_device, enable_kernels, torch_dtype]
|
||||
elif torch_device == "hpu":
|
||||
atol = atols["cuda", enable_kernels, torch_dtype]
|
||||
rtol = rtols["cuda", enable_kernels, torch_dtype]
|
||||
elif torch_device == "xpu":
|
||||
# As of PyTorch 2.5 XPU backend supports only torch.nn.attention.SDPBackend.MATH
|
||||
# which is implemented on PyTorch level using aten operators and is
|
||||
# device agnostic with respect to implementation of each aten operator.
|
||||
atol = atols["cuda", False, torch_dtype]
|
||||
rtol = rtols["cuda", False, torch_dtype]
|
||||
else:
|
||||
atol = 1e-7
|
||||
rtol = 1e-4
|
||||
|
||||
# Masked tokens output slightly deviates - we don't mind that.
|
||||
if use_attention_mask:
|
||||
_logits_sdpa = torch.zeros_like(input=logits_sdpa)
|
||||
_logits_eager = torch.zeros_like(input=logits_eager)
|
||||
|
||||
_logits_sdpa[:-1] = logits_sdpa[:-1]
|
||||
_logits_eager[:-1] = logits_eager[:-1]
|
||||
|
||||
if padding_side == "left":
|
||||
_logits_sdpa[-1:, 2:] = logits_sdpa[-1:, 2:]
|
||||
_logits_eager[-1:, 2:] = logits_eager[-1:, 2:]
|
||||
|
||||
elif padding_side == "right":
|
||||
_logits_sdpa[-1:, 2:] = logits_sdpa[-1:, :-2]
|
||||
_logits_eager[-1:, 2:] = logits_eager[-1:, :-2]
|
||||
|
||||
logits_sdpa = _logits_sdpa
|
||||
logits_eager = _logits_eager
|
||||
|
||||
results = [
|
||||
torch.allclose(_logits_sdpa, _logits_eager, atol=atol, rtol=rtol)
|
||||
for (_logits_sdpa, _logits_eager) in zip(logits_sdpa, logits_eager)
|
||||
]
|
||||
# If 80% batch elements have matched results, it's fine
|
||||
if np.mean(results) < 0.8:
|
||||
mean_relative_diff = ((logits_sdpa - logits_eager).abs() / (logits_eager.abs() + 1e-12)).mean()
|
||||
raise ValueError(
|
||||
f"mean relative difference for {key}: {mean_relative_diff:.3e}, torch atol = {atol}, torch rtol = "
|
||||
f"{rtol}"
|
||||
)
|
||||
_test_eager_matches_sdpa_inference(
|
||||
self, name, torch_dtype, padding_side, use_attention_mask, output_attentions, enable_kernels
|
||||
)
|
||||
|
||||
@require_torch_sdpa
|
||||
@require_torch_accelerator
|
||||
|
||||
Reference in New Issue
Block a user