added bettertransformerlayer for detr model

docs/source/bettertransformer/overview.mdx

@@ -38,6 +38,7 @@ The list of supported model below:
 - [Data2VecText](https://arxiv.org/abs/2202.03555)
 - [DistilBert](https://arxiv.org/abs/1910.01108)
 - [DeiT](https://arxiv.org/abs/2012.12877)
+- [DETR](https://arxiv.org/abs/2005.12872)
 - [Electra](https://arxiv.org/abs/2003.10555)
 - [Ernie](https://arxiv.org/abs/1904.09223)
 - [FSMT](https://arxiv.org/abs/1907.06616)

optimum/bettertransformer/models/__init__.py

@@ -34,6 +34,7 @@
     BartEncoderLayerBetterTransformer,
     BertLayerBetterTransformer,
     CLIPLayerBetterTransformer,
+    DetrEncoderLayerBetterTransformer,
     DistilBertLayerBetterTransformer,
     FSMTEncoderLayerBetterTransformer,
     MBartEncoderLayerBetterTransformer,
@@ -60,6 +61,7 @@ class BetterTransformerManager:
         "codegen": {"CodeGenAttention": CodegenAttentionLayerBetterTransformer},
         "data2vec-text": {"Data2VecTextLayer": BertLayerBetterTransformer},
         "deit": {"DeiTLayer": ViTLayerBetterTransformer},
+        "DETR": {"DetrEncoderLayer": DetrEncoderLayerBetterTransformer},
         "distilbert": {"TransformerBlock": DistilBertLayerBetterTransformer},
         "electra": {"ElectraLayer": BertLayerBetterTransformer},
         "ernie": {"ErnieLayer": BertLayerBetterTransformer},

optimum/bettertransformer/models/encoder_models.py

@@ -667,6 +667,140 @@ def forward(self, x, attn_mask, head_mask=None, output_attentions=None, *_):
         if x.is_nested and self.is_last_layer:
             x = x.to_padded_tensor(0.0)
         return (x,)
+
+
+
+class DetrEncoderLayerBetterTransformer(BetterTransformerBaseLayer):
+    def __init__(self, detr_layer, config):
+        r"""
+        A simple conversion of the ProphetNet Encoder layer to its `BetterTransformer` implementation.
+        Args:
+            prophet_net_layer (`torch.nn.Module`):
+                The original ProphetNet Layer where the weights needs to be retrieved.
+        """
+        super().__init__(config)
+        self.config = config
+        # In_proj layer
+        self.in_proj_weight = nn.Parameter(
+            torch.cat(
+                [
+                    detr_layer.self_attn.query_proj.weight,
+                    detr_layer.self_attn.key_proj.weight,
+                    detr_layer.self_attn.value_proj.weight,
+                ]
+            )
+        )
+        self.in_proj_bias = nn.Parameter(
+            torch.cat(
+                [
+                    detr_layer.self_attn.query_proj.bias,
+                    detr_layer.self_attn.key_proj.bias,
+                    detr_layer.self_attn.value_proj.bias,
+                ]
+            )
+        )
+
+        # Out proj layer
+        self.out_proj_weight = detr_layer.self_attn.out_proj.weight
+        self.out_proj_bias = detr_layer.self_attn.out_proj.bias
+
+        # Linear layer 1
+        self.linear1_weight = detr_layer.feed_forward.intermediate.weight
+        self.linear1_bias = detr_layer.feed_forward.intermediate.bias
+
+        # Linear layer 2
+        self.linear2_weight = detr_layer.feed_forward.output.weight
+        self.linear2_bias = detr_layer.feed_forward.output.bias
+
+        # Layer norm 1
+        self.norm1_eps = detr_layer.self_attn_layer_norm.eps
+        self.norm1_weight = detr_layer.self_attn_layer_norm.weight
+        self.norm1_bias = detr_layer.self_attn_layer_norm.bias
+
+        # Layer norm 2
+        self.norm2_eps = detr_layer.feed_forward_layer_norm.eps
+        self.norm2_weight = detr_layer.feed_forward_layer_norm.weight
+        self.norm2_bias = detr_layer.feed_forward_layer_norm.bias
+
+        # Model hyper parameters
+        self.num_heads = detr_layer.self_attn.num_attn_heads
+        self.embed_dim = detr_layer.self_attn.head_dim * self.num_heads
+
+        # Last step: set the last layer to `False` -> this will be set to `True` when converting the model
+        self.is_last_layer = False
+
+        self.original_layers_mapping = {
+            "in_proj_weight": [
+                "self_attn.query_proj.weight",
+                "self_attn.key_proj.weight",
+                "self_attn.value_proj.weight",
+            ],
+            "in_proj_bias": ["self_attn.query_proj.bias", "self_attn.key_proj.bias", "self_attn.value_proj.bias"],
+            "out_proj_weight": "self_attn.out_proj.weight",
+            "out_proj_bias": "self_attn.out_proj.bias",
+            "linear1_weight": "feed_forward.intermediate.weight",
+            "linear1_bias": "feed_forward.intermediate.bias",
+            "linear2_weight": "feed_forward.output.weight",
+            "linear2_bias": "feed_forward.output.bias",
+            "norm1_weight": "self_attn_layer_norm.weight",
+            "norm1_bias": "self_attn_layer_norm.bias",
+            "norm2_weight": "feed_forward_layer_norm.weight",
+            "norm2_bias": "feed_forward_layer_norm.bias",
+        }
+
+        self.validate_bettertransformer()
+
+    def forward(self, hidden_states, attention_mask, *_, **__):
+        r"""
+        This is just a wrapper around the forward function proposed in:
+        https://github.com/huggingface/transformers/pull/19553
+        """
+        super().forward_checker()
+
+        if not hasattr(hidden_states, "original_shape"):
+            original_shape = hidden_states.shape
+        else:
+            original_shape = hidden_states.original_shape
+
+        if hidden_states.is_nested:
+            attention_mask = None
+
+        if attention_mask is not None:
+            # attention mask comes in with values 0 and -inf. we convert to torch.nn.TransformerEncoder style bool mask
+            # 0->false->keep this token -inf->true->mask this token
+            attention_mask = attention_mask.squeeze(1)[:, 0]
+            attention_mask = attention_mask.bool()
+            attention_mask = torch.reshape(attention_mask, (attention_mask.shape[0], attention_mask.shape[-1]))
+            hidden_states = torch._nested_tensor_from_mask(hidden_states, ~attention_mask)
+            attention_mask = None
+
+        hidden_states = torch._transformer_encoder_layer_fwd(
+            hidden_states,
+            self.embed_dim,
+            self.num_heads,
+            self.in_proj_weight,
+            self.in_proj_bias,
+            self.out_proj_weight,
+            self.out_proj_bias,
+            self.use_gelu,
+            self.norm_first,
+            self.norm1_eps,
+            self.norm1_weight,
+            self.norm1_bias,
+            self.norm2_weight,
+            self.norm2_bias,
+            self.linear1_weight,
+            self.linear1_bias,
+            self.linear2_weight,
+            self.linear2_bias,
+            attention_mask,
+        )
+        if not self.is_last_layer:
+            hidden_states.original_shape = original_shape
+        elif hidden_states.is_nested and self.is_last_layer:
+            hidden_states = hidden_states.to_padded_tensor(0.0, original_shape)
+        return (hidden_states,)
+
 
 
 class WhisperEncoderLayerBetterTransformer(BetterTransformerBaseLayer, nn.Module):

tests/bettertransformer/test_encoder_decoder.py

@@ -38,6 +38,7 @@ class BetterTransformersEncoderDecoderTest(BetterTransformersTestMixin, unittest
     SUPPORTED_ARCH = [
         "bart",
         "blenderbot",
+        "DETR"
         "fsmt",
         "m2m_100",
         "marian",

tests/bettertransformer/testing_utils.py

@@ -38,6 +38,7 @@
     "codegen": "hf-internal-testing/tiny-random-CodeGenModel",
     "data2vec-text": "hf-internal-testing/tiny-random-Data2VecTextModel",
     "deit": "hf-internal-testing/tiny-random-deit",
+    "DETR": "hf-internal-testing/tiny-random-DETR",
     "distilbert": "hf-internal-testing/tiny-random-DistilBertModel",
     "electra": "hf-internal-testing/tiny-random-ElectraModel",
     "ernie": "hf-internal-testing/tiny-random-ErnieModel",