Added Decoder layer class in Qeff for granite

QEfficient/transformers/models/granite/modeling_granite.py

@@ -17,6 +17,7 @@
 from transformers.models.granite.modeling_granite import (
     GraniteAttention,
     GraniteConfig,
+    GraniteDecoderLayer,
     GraniteForCausalLM,
     GraniteModel,
     GraniteRotaryEmbedding,
@@ -173,6 +174,80 @@ def forward(
         return attn_output, attn_weights
 
 
+class QEffGraniteDecoderLayer(GraniteDecoderLayer):
+    """
+    Copied from LlamaForCausalLM: https://github.com/huggingface/transformers/blob/main/src/transformers/models/granite/modeling_granite.py
+    The only differences are:
+    - add new args batch idx for the CB models although its not supported yet.
+    """
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        output_attentions: Optional[bool] = False,
+        batch_index: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+        **kwargs,
+    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_values (`Cache`, *optional*): cached past key and value projection states
+            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+                Indices depicting the position of the input sequence tokens in the sequence
+            position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
+                Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
+                with `head_dim` being the embedding dimension of each attention head.
+            kwargs (`dict`, *optional*):
+                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+                into the model
+        """
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+        # Self Attention
+        hidden_states, self_attn_weights = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            output_attentions=output_attentions,
+            batch_index=batch_index,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states * self.residual_multiplier
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states * self.residual_multiplier  # main diff with Llama
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        return outputs
+
+
 class QEffGraniteModel(GraniteModel):
     def forward(
         self,

QEfficient/transformers/models/pytorch_transforms.py

@@ -63,6 +63,7 @@
 from transformers.models.gptj.modeling_gptj import GPTJAttention, GPTJBlock, GPTJForCausalLM, GPTJModel
 from transformers.models.granite.modeling_granite import (
     GraniteAttention,
+    GraniteDecoderLayer,
     GraniteForCausalLM,
     GraniteModel,
     GraniteRMSNorm,
@@ -268,6 +269,7 @@
 )
 from QEfficient.transformers.models.granite.modeling_granite import (
     QEffGraniteAttention,
+    QEffGraniteDecoderLayer,
     QEffGraniteForCausalLM,
     QEffGraniteModel,
 )
@@ -531,6 +533,7 @@ class KVCacheTransform(ModuleMappingTransform):
         GraniteModel: QEffGraniteModel,
         GraniteForCausalLM: QEffGraniteForCausalLM,
         GraniteAttention: QEffGraniteAttention,
+        GraniteDecoderLayer: QEffGraniteDecoderLayer,
         # GraniteMoe
         GraniteMoeModel: QEffGraniteMoeModel,
         GraniteMoeForCausalLM: QEffGraniteMoeForCausalLM,