Remove support for TorchScript tracing

curated_transformers/layers/attention.py

@@ -12,7 +12,6 @@
 from torch.nn import Dropout, Linear, Module
 
 from ..semver import Default, FutureMandatory
-from ..util.dataclass import DataclassAsDict
 from .cache import KeyValueCache
 from .embeddings import QueryKeyRotaryEmbeddings
 
@@ -47,7 +46,7 @@ def enable_torch_sdp(use_torch_sdp: bool = True):
 
 
 @dataclass
-class AttentionMask(DataclassAsDict):
+class AttentionMask:
     """
     Mask for attention calculation. Sequence elements for which the
     corresponding mask element is set to ``False`` are ignored during
@@ -74,33 +73,6 @@ def __init__(self, bool_mask: Tensor):
                 "[batch_len, heads, query_len, key_len]"
             )
 
-        self.__post_init__()
-
-    @classmethod
-    def jit_rewrap(
-        cls: Type["AttentionMask"],
-        attention_mask: Union["AttentionMask", Dict[str, Tensor]],
-    ) -> "AttentionMask":
-        """
-        Rewrap TorchScript dictionary conversion of an attention mask
-        as an ``AttentionMask``.
-
-        :param attention_mask:
-            The attention mask or its dictionary representation. If the
-            value is an ``AttentionMask``, the value will be returned as-is.
-        :returns:
-            The attention mask.
-        """
-        if isinstance(attention_mask, AttentionMask):
-            return attention_mask
-
-        bool_mask = attention_mask.get("bool_mask")
-        if bool_mask is None:
-            raise ValueError(
-                "Attention mask is not of the `AttentionMask` type, nor a dict with 'bool_mask'."
-            )
-        return AttentionMask(bool_mask=bool_mask)
-
     def apply_logit_mask(self, input: Tensor) -> Tensor:
         """
         Use the attention mask to mask attention logits.
@@ -919,20 +891,13 @@ def forward(
 
             *Shape:* ``(batch_size, seq_len, width)``
         """
-        # The attention mask is converted to a dict for traced models. Rewrap as
-        # AttentionMask to get validation and utility methods.
-        attention_mask = AttentionMask.jit_rewrap(attention_mask)
-
         query, key, value = self._query_key_value(input)
 
         if self.rotary_embeds is not None:
             query, key = self.rotary_embeds(
                 query=query, key=key, cache=cache, positions=positions
             )
 
-        # The key-value is converted to a dict for traced models. Rewrap as
-        # KeyValueCache to get validation and utility methods.
-        cache = KeyValueCache.jit_rewrap(cache)
         if cache is not None:
             cache_k = cache.key
             cache_v = cache.value
@@ -953,7 +918,7 @@ def forward(
         output = self.output(attn)
 
         if store_cache:
-            return output, KeyValueCache(key, value)
+            return output, KeyValueCache(key=key, value=value)
         else:
             return output, None
 

curated_transformers/layers/cache.py

@@ -4,8 +4,6 @@
 import torch
 from torch import Tensor
 
-from ..util.dataclass import DataclassAsTuple
-
 CacheProtocolSelf = TypeVar("CacheProtocolSelf", bound="CacheProtocol")
 
 
@@ -27,7 +25,7 @@ def filter_batch_items(self: CacheProtocolSelf, mask: Tensor) -> CacheProtocolSe
 
 
 @dataclass
-class KeyValueCache(DataclassAsTuple):
+class KeyValueCache:
     """
     Cache type for layers that cache keys and values.
 
@@ -52,41 +50,4 @@ def filter_batch_items(self, mask: Tensor) -> "KeyValueCache":
         if mask.dtype != torch.bool:
             raise ValueError(f"Cache mask dtype must be bool, was: {mask.dtype}.")
 
-        return KeyValueCache(self.key[mask], self.value[mask])
-
-    @classmethod
-    def jit_rewrap(
-        cls: Type["KeyValueCache"],
-        key_value_cache: Optional[Union["KeyValueCache", Tuple[Tensor, Tensor]]],
-    ) -> Optional["KeyValueCache"]:
-        """
-        Rewrap TorchScript dictionary conversion of a key-value cache.
-
-        :param key_value_cache:
-            The key-value cache or its dictionary representation. If the
-            value is a ``KeyValueCache`` or ``None``, it will be
-            returned as-is.
-        :returns:
-            The key-value cache.
-        """
-        if key_value_cache is None or isinstance(key_value_cache, KeyValueCache):
-            return key_value_cache
-
-        if (
-            not isinstance(key_value_cache, tuple)
-            or len(key_value_cache) != 2
-            or not all(isinstance(item, Tensor) for item in key_value_cache)
-        ):
-            raise ValueError(
-                f"Key-value cache is not of the `KeyValueCache` type, nor `Tuple[Tensor, Tensor]`: `{type(key_value_cache).__name__}`"
-            )
-
-        key_cache = key_value_cache[0]
-        value_cache = key_value_cache[1]
-
-        if key_cache.shape != value_cache.shape:
-            raise ValueError(
-                f"Key cache ({key_cache.shape}) and value cache ({value_cache.shape}) must have same shapes."
-            )
-
-        return cls(key_cache, value_cache)
+        return KeyValueCache(key=self.key[mask], value=self.value[mask])

curated_transformers/layers/embeddings.py

@@ -278,10 +278,6 @@ def forward(
         head_width = self.head_width
         rotary_width = self.rotary_width
 
-        # The key-value is converted to a dict for traced models. Rewrap as
-        # KeyValueCache to get validation and utility methods.
-        cache = KeyValueCache.jit_rewrap(cache)
-
         # If a cache was provided, but no positions, assume that the
         # positions of the current batch continue from the cache.
         if cache is not None and positions is None:

curated_transformers/models/output.py

@@ -4,13 +4,12 @@
 from torch import Tensor
 
 from ..layers.cache import CacheProtocol
-from ..util.dataclass import DataclassAsTuple
 
 CacheT = TypeVar("CacheT", bound=CacheProtocol)
 
 
 @dataclass
-class ModelOutput(DataclassAsTuple):
+class ModelOutput:
     """
     Base class for model outputs.
 

curated_transformers/tests/models/albert/test_encoder.py

@@ -44,20 +44,6 @@ def test_encoder_with_torch_compile(torch_device, with_torch_sdp):
     )
 
 
-@pytest.mark.slow
-@pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
-@pytest.mark.parametrize("torch_device", TORCH_DEVICES)
-@pytest.mark.parametrize("with_torch_sdp", [False, True])
-def test_encoder_with_torchscript_trace(torch_device, with_torch_sdp):
-    assert_encoder_output_equals_hf(
-        ALBERTEncoder,
-        "explosion-testing/albert-test",
-        torch_device,
-        jit_method=JITMethod.TorchScriptTrace,
-        with_torch_sdp=with_torch_sdp,
-    )
-
-
 @pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
 @pytest.mark.parametrize("torch_device", TORCH_DEVICES)
 def test_encoder_hf_serializtion_roundtrip(torch_device):

curated_transformers/tests/models/bert/test_encoder.py

@@ -38,20 +38,6 @@ def test_encoder_with_torch_compile(torch_device, with_torch_sdp):
     )
 
 
-@pytest.mark.slow
-@pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
-@pytest.mark.parametrize("torch_device", TORCH_DEVICES)
-@pytest.mark.parametrize("with_torch_sdp", [False, True])
-def test_encoder_with_torchscript_trace(torch_device, with_torch_sdp):
-    assert_encoder_output_equals_hf(
-        BERTEncoder,
-        "explosion-testing/bert-test",
-        torch_device,
-        jit_method=JITMethod.TorchScriptTrace,
-        with_torch_sdp=with_torch_sdp,
-    )
-
-
 @pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
 @pytest.mark.parametrize("torch_device", TORCH_DEVICES)
 def test_encoder_hf_serializtion_roundtrip(torch_device):

curated_transformers/tests/models/camembert/test_encoder.py

@@ -38,20 +38,6 @@ def test_encoder_with_torch_compile(torch_device, with_torch_sdp):
     )
 
 
-@pytest.mark.slow
-@pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
-@pytest.mark.parametrize("torch_device", TORCH_DEVICES)
-@pytest.mark.parametrize("with_torch_sdp", [False, True])
-def test_encoder_with_torchscript_trace(torch_device, with_torch_sdp):
-    assert_encoder_output_equals_hf(
-        CamemBERTEncoder,
-        "explosion-testing/camembert-test",
-        torch_device,
-        jit_method=JITMethod.TorchScriptTrace,
-        with_torch_sdp=with_torch_sdp,
-    )
-
-
 @pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
 @pytest.mark.parametrize("torch_device", TORCH_DEVICES)
 def test_encoder_hf_serializtion_roundtrip(torch_device):

curated_transformers/tests/models/falcon/test_decoder.py

@@ -100,27 +100,6 @@ def test_decoder_with_torch_compile(torch_device, model_revision, with_torch_sdp
     )
 
 
-@pytest.mark.slow
-@pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
-@pytest.mark.parametrize("torch_device", TORCH_DEVICES)
-@pytest.mark.parametrize("model_revision", FALCON_TEST_MODELS)
-@pytest.mark.parametrize("with_torch_sdp", [False, True])
-def test_decoder_with_torchscript_trace(torch_device, model_revision, with_torch_sdp):
-    model, revision = model_revision
-    assert_decoder_output_equals_hf(
-        FalconDecoder,
-        model,
-        torch_device,
-        model_revision=revision,
-        trust_remote_code=True,
-        with_cache=False,
-        with_mask=False,
-        with_positions=False,
-        jit_method=JITMethod.TorchScriptTrace,
-        with_torch_sdp=with_torch_sdp,
-    )
-
-
 @pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
 @pytest.mark.parametrize("torch_device", TORCH_DEVICES)
 @pytest.mark.parametrize("model_revision", FALCON_TEST_MODELS)

curated_transformers/tests/models/gpt_neox/test_causal_lm.py

@@ -39,20 +39,6 @@ def test_causal_lm_with_torch_compile(torch_device, with_torch_sdp):
     )
 
 
-@pytest.mark.slow
-@pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
-@pytest.mark.parametrize("torch_device", TORCH_DEVICES)
-@pytest.mark.parametrize("with_torch_sdp", [False, True])
-def test_causal_lm_with_torchscript_trace(torch_device, with_torch_sdp):
-    assert_causal_lm_output_equals_hf(
-        GPTNeoXCausalLM,
-        "trl-internal-testing/tiny-random-GPTNeoXForCausalLM",
-        torch_device,
-        jit_method=JITMethod.TorchScriptTrace,
-        with_torch_sdp=with_torch_sdp,
-    )
-
-
 @pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
 @pytest.mark.parametrize("torch_device", TORCH_DEVICES)
 def test_causal_lm_hf_serializtion_roundtrip(torch_device):

curated_transformers/tests/models/gpt_neox/test_decoder.py

@@ -45,22 +45,6 @@ def test_decoder_with_torch_compile(torch_device, with_torch_sdp):
     )
 
 
-@pytest.mark.slow
-@pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
-@pytest.mark.parametrize("torch_device", TORCH_DEVICES)
-@pytest.mark.parametrize("with_torch_sdp", [False, True])
-def test_decoder_with_torchscript_trace(torch_device, with_torch_sdp):
-    assert_decoder_output_equals_hf(
-        GPTNeoXDecoder,
-        "trl-internal-testing/tiny-random-GPTNeoXForCausalLM",
-        torch_device,
-        with_cache=True,
-        with_positions=True,
-        jit_method=JITMethod.TorchScriptTrace,
-        with_torch_sdp=with_torch_sdp,
-    )
-
-
 @pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
 @pytest.mark.parametrize("torch_device", TORCH_DEVICES)
 def test_decoder_hf_serializtion_roundtrip(torch_device):

curated_transformers/tests/models/llama/test_causal_lm.py

@@ -48,21 +48,6 @@ def test_causal_lm_torch_compile(torch_device, model, with_torch_sdp):
     )
 
 
-@pytest.mark.slow
-@pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
-@pytest.mark.parametrize("torch_device", TORCH_DEVICES)
-@pytest.mark.parametrize("model", LLAMA_TEST_MODELS)
-@pytest.mark.parametrize("with_torch_sdp", [False, True])
-def test_causal_lm_with_torchscript_trace(torch_device, model, with_torch_sdp):
-    assert_causal_lm_output_equals_hf(
-        LlamaCausalLM,
-        model,
-        torch_device,
-        jit_method=JITMethod.TorchScriptTrace,
-        with_torch_sdp=with_torch_sdp,
-    )
-
-
 @pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
 @pytest.mark.parametrize("model", LLAMA_TEST_MODELS)
 @pytest.mark.parametrize("torch_device", TORCH_DEVICES)

curated_transformers/tests/models/llama/test_decoder.py

@@ -45,21 +45,6 @@ def test_decoder_with_torch_compile(torch_device, model, with_torch_sdp):
     )
 
 
-@pytest.mark.slow
-@pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
-@pytest.mark.parametrize("torch_device", TORCH_DEVICES)
-@pytest.mark.parametrize("model", LLAMA_TEST_MODELS)
-@pytest.mark.parametrize("with_torch_sdp", [False, True])
-def test_decoder_with_torchscript_trace(torch_device, model, with_torch_sdp):
-    assert_decoder_output_equals_hf(
-        LlamaDecoder,
-        model,
-        torch_device,
-        jit_method=JITMethod.TorchScriptTrace,
-        with_torch_sdp=with_torch_sdp,
-    )
-
-
 @pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
 @pytest.mark.parametrize("model", LLAMA_TEST_MODELS)
 @pytest.mark.parametrize("torch_device", TORCH_DEVICES)

curated_transformers/tests/models/mpt/test_causal_lm.py

@@ -40,20 +40,6 @@ def test_causal_lm_with_torch_compile(torch_device, with_torch_sdp):
     )
 
 
-@pytest.mark.slow
-@pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
-@pytest.mark.parametrize("torch_device", TORCH_DEVICES)
-@pytest.mark.parametrize("with_torch_sdp", [False, True])
-def test_causal_lm_with_torchscript_trace(torch_device, with_torch_sdp):
-    assert_causal_lm_output_equals_hf(
-        MPTCausalLM,
-        "explosion-testing/mpt-test",
-        torch_device,
-        jit_method=JITMethod.TorchScriptTrace,
-        with_torch_sdp=with_torch_sdp,
-    )
-
-
 @pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
 @pytest.mark.parametrize("torch_device", TORCH_DEVICES)
 def test_causal_lm_hf_serializtion_roundtrip(torch_device):

curated_transformers/tests/models/mpt/test_decoder.py

@@ -48,22 +48,6 @@ def test_decoder_with_torch_compile(torch_device, with_torch_sdp):
     )
 
 
-@pytest.mark.slow
-@pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
-@pytest.mark.parametrize("torch_device", TORCH_DEVICES)
-@pytest.mark.parametrize("with_torch_sdp", [False, True])
-def test_decoder_with_torchscript_trace(torch_device, with_torch_sdp):
-    assert_decoder_output_equals_hf(
-        MPTDecoder,
-        "explosion-testing/mpt-test",
-        torch_device,
-        with_cache=True,
-        with_positions=False,
-        jit_method=JITMethod.TorchScriptTrace,
-        with_torch_sdp=with_torch_sdp,
-    )
-
-
 @pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
 @pytest.mark.parametrize("torch_device", TORCH_DEVICES)
 def test_decoder_hf_serializtion_roundtrip(torch_device):