add ONNX support for BLOOM

docs/source/en/serialization.mdx

@@ -53,6 +53,7 @@ Ready-made configurations include the following architectures:
 - BigBird-Pegasus
 - Blenderbot
 - BlenderbotSmall
+- BLOOM
 - CamemBERT
 - CodeGen
 - ConvBERT

src/transformers/models/bloom/__init__.py

@@ -22,10 +22,7 @@
 
 
 _import_structure = {
-    "configuration_bloom": [
-        "BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP",
-        "BloomConfig",
-    ],
+    "configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
 }
 try:
     if not is_tokenizers_available():
@@ -51,7 +48,7 @@
     ]
 
 if TYPE_CHECKING:
-    from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig
+    from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
 
     try:
         if not is_tokenizers_available():

src/transformers/models/bloom/configuration_bloom.py

@@ -13,7 +13,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """ Bloom configuration"""
+from collections import OrderedDict
+from typing import Any, List, Mapping, Optional
+
+from transformers import PreTrainedTokenizer, TensorType, is_torch_available
+
 from ...configuration_utils import PretrainedConfig
+from ...onnx import OnnxConfigWithPast, PatchingSpec
 from ...utils import logging
 
 
@@ -153,3 +159,85 @@ def __init__(
         self.slow_but_exact = slow_but_exact
 
         super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+
+
+# Copied from transformers.models.gpt2.configuration_gpt2.GPT2OnnxConfig
+class BloomOnnxConfig(OnnxConfigWithPast):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        task: str = "default",
+        patching_specs: List[PatchingSpec] = None,
+        use_past: bool = False,
+    ):
+        super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past)
+        if not getattr(self._config, "pad_token_id", None):
+            # TODO: how to do that better?
+            self._config.pad_token_id = 0
+
+    @property
+    def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        common_inputs = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
+        if self.use_past:
+            self.fill_with_past_key_values_(common_inputs, direction="inputs")
+            common_inputs["attention_mask"] = {0: "batch", 1: "past_sequence + sequence"}
+        else:
+            common_inputs["attention_mask"] = {0: "batch", 1: "sequence"}
+
+        return common_inputs
+
+    @property
+    def num_layers(self) -> int:
+        return self._config.n_layer
+
+    @property
+    def num_attention_heads(self) -> int:
+        return self._config.n_head
+
+    def generate_dummy_inputs(
+        self,
+        tokenizer: PreTrainedTokenizer,
+        batch_size: int = -1,
+        seq_length: int = -1,
+        is_pair: bool = False,
+        framework: Optional[TensorType] = None,
+    ) -> Mapping[str, Any]:
+        common_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
+            tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
+        )
+
+        # We need to order the input in the way they appears in the forward()
+        ordered_inputs = OrderedDict({"input_ids": common_inputs["input_ids"]})
+
+        # Need to add the past_keys
+        if self.use_past:
+            if not is_torch_available():
+                raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
+            else:
+                import torch
+
+                batch, seqlen = common_inputs["input_ids"].shape
+                # Not using the same length for past_key_values
+                past_key_values_length = seqlen + 2
+                past_shape = (
+                    batch,
+                    self.num_attention_heads,
+                    past_key_values_length,
+                    self._config.hidden_size // self.num_attention_heads,
+                )
+                ordered_inputs["past_key_values"] = [
+                    (torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(self.num_layers)
+                ]
+
+        ordered_inputs["attention_mask"] = common_inputs["attention_mask"]
+        if self.use_past:
+            mask_dtype = ordered_inputs["attention_mask"].dtype
+            ordered_inputs["attention_mask"] = torch.cat(
+                [ordered_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
+            )
+
+        return ordered_inputs
+
+    @property
+    def default_onnx_opset(self) -> int:
+        return 13

src/transformers/models/bloom/modeling_bloom.py

@@ -78,17 +78,14 @@ def split_tensor_along_last_dim(tensor, num_partitions, contiguous_split_chunks=
 
 
 def attention_mask_func(attention_scores, attention_mask, causal_mask):
-    if attention_mask.dtype == torch.bool:
-        attention_mask_bool = ~attention_mask
-    else:
-        attention_mask_bool = (1 - attention_mask).bool()
+    attention_mask_bool = 1 - attention_mask.int()
 
     query_length, key_length, n_heads = attention_scores.size(2), attention_scores.size(3), attention_scores.size(1)
-    padded_causal_mask = (
-        attention_mask_bool[:, None, key_length - query_length : key_length, None]
-        + ~causal_mask[:, :, key_length - query_length : key_length, :key_length]
-    ).bool()
-    padded_causal_mask = padded_causal_mask + attention_mask_bool[:, None, None, :key_length].bool()
+    padded_causal_mask = attention_mask_bool[:, None, key_length - query_length : key_length, None] + (
+        1 - causal_mask[:, :, key_length - query_length : key_length, :key_length].int()
+    )
+    padded_causal_mask = padded_causal_mask + attention_mask_bool[:, None, None, :key_length]
+    padded_causal_mask = padded_causal_mask.bool()
     # Make use of floats
     return (
         attention_scores.masked_fill_(padded_causal_mask.expand(-1, n_heads, -1, -1), -10000.0),
@@ -296,8 +293,9 @@ def forward(self, input, mask, max_positions):
             mask = torch.ones(input.shape[0], max_positions, dtype=torch.bool, device=input.device)
 
         mask = mask.to(input.device)
+        seq_ids = torch.arange(max_positions, device=input.device)
         causal_mask = (
-            torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool))
+            (seq_ids[None, None, :] <= seq_ids[None, :, None])
             .view(1, 1, max_positions, max_positions)
             .to(input.device)
         )

src/transformers/onnx/features.py

@@ -182,6 +182,15 @@ class FeaturesManager:
             "seq2seq-lm-with-past",
             onnx_config_cls="models.blenderbot_small.BlenderbotSmallOnnxConfig",
         ),
+        "bloom": supported_features_mapping(
+            "default",
+            "default-with-past",
+            "causal-lm",
+            "causal-lm-with-past",
+            "sequence-classification",
+            "token-classification",
+            onnx_config_cls="models.bloom.BloomOnnxConfig",
+        ),
         "camembert": supported_features_mapping(
             "default",
             "masked-lm",

tests/onnx/test_onnx_v2.py

@@ -204,6 +204,7 @@ def test_values_override(self):
 }
 
 PYTORCH_EXPORT_WITH_PAST_MODELS = {
+    ("bloom", "bigscience/bloom-350m"),
     ("gpt2", "gpt2"),
     ("gpt-neo", "EleutherAI/gpt-neo-125M"),
 }