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Fix MegaModel
CI (#22652)
* fix --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
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@ -896,7 +896,7 @@ class MegaMovingAverageGatedAttention(nn.Module):
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# apply causal mask (presumed to be 1/0 for not masked / masked)
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# additive, but convert to 0/-inf (which is not explicitly in the Mega source code)
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if causal_mask is not None:
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additive_causal_mask = torch.zeros_like(causal_mask, dtype=torch.float)
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additive_causal_mask = torch.zeros_like(causal_mask, dtype=qk.dtype)
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additive_causal_mask = additive_causal_mask.masked_fill((1 - causal_mask).bool(), float("-inf"))
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qk = qk + additive_causal_mask
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@ -387,6 +387,8 @@ class MegaModelTester:
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config.use_chunking = True
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config.chunk_size = input_ids.size(1) + 25
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model = MegaModel(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
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@ -400,6 +402,8 @@ class MegaModelTester:
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# we want the chunk size to be < sequence length, and the sequence length to be a multiple of chunk size
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config.chunk_size = input_ids.size(1) * 2
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model = MegaModel(config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids.repeat(1, 8),
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@ -412,6 +416,8 @@ class MegaModelTester:
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):
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config.attention_activation = "laplace"
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model = MegaModel(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
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@ -422,6 +428,8 @@ class MegaModelTester:
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):
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config.attention_activation = "relu2"
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model = MegaModel(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
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@ -432,6 +440,8 @@ class MegaModelTester:
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):
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config.max_positions = self.seq_length - 2
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model = MegaModel(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
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@ -615,6 +625,39 @@ class MegaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
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model = MegaModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.")
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def test_cpu_offload(self):
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super().test_cpu_offload()
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@unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.")
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def test_disk_offload(self):
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super().test_disk_offload()
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@unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.")
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def test_model_parallelism(self):
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super().test_model_parallelism()
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@unittest.skip(
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reason=(
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"Calling `self.attention_function` in `MegaMovingAverageGatedAttention.forward` changes the submodules on "
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"device 1 to device 0 (also changes `requires_grad`). No idea how this could happen for now."
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)
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)
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def test_multi_gpu_data_parallel_forward(self):
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super().test_multi_gpu_data_parallel_forward()
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@unittest.skip(reason="Tracing of the dynamically computed `MegaMultiDimensionDampedEma._kernel` doesn't work.")
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def test_torchscript_simple(self):
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super().test_torchscript_simple()
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@unittest.skip(reason="Tracing of the dynamically computed `MegaMultiDimensionDampedEma._kernel` doesn't work.")
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def test_torchscript_output_hidden_state(self):
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super().test_torchscript_output_hidden_state()
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@unittest.skip(reason="Tracing of the dynamically computed `MegaMultiDimensionDampedEma._kernel` doesn't work.")
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def test_torchscript_output_attentions(self):
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super().test_torchscript_output_attentions()
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@require_torch
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class MegaModelIntegrationTest(TestCasePlus):
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