Fix (llm): correct handling of attention mask shape

src/brevitas_examples/llm/llm_quant/mha_layers.py

@@ -4,6 +4,26 @@
 from torch import nn
 
 
+def attention_mask_handler(
+        attention_mask, batch_size, num_heads, query_seq_length, key_value_seq_length):
+    """Re-arrange attention mask to go from 4D to 3D (explicit batch_size and n_heads) or 2D
+    (implicit batch_size and n_heads)."""
+    if len(attention_mask.shape) == 4:
+        if attention_mask.shape[0] == 1:
+            attention_mask = attention_mask.repeat(batch_size, 1, 1, 1)
+        if attention_mask.shape[1] == 1:
+            attention_mask = attention_mask.repeat(1, num_heads, 1, 1)
+        if attention_mask.shape[2] == 1:
+            attention_mask = attention_mask.repeat(1, 1, query_seq_length, 1)
+        attention_mask = attention_mask.view(
+            batch_size * num_heads, query_seq_length, key_value_seq_length)
+    elif len(attention_mask.shape) == 2 and attention_mask.shape[0] == 1:
+        # This could happen in Encoder-like architecture
+        assert query_seq_length == key_value_seq_length
+        attention_mask = attention_mask.repeat(query_seq_length, 1)
+    return attention_mask
+
+
 class MultiheadAttentionWrapper(nn.Module):
 
     def __init__(
@@ -114,8 +134,15 @@ def forward(
             key_value_states = hidden_states
         if layer_head_mask is not None:
             raise RuntimeError("layer_head_mask is not supported.")
-        if attention_mask is not None:
-            attention_mask = attention_mask.squeeze()
+        if self.mha.batch_first:
+            batch_size, query_seq_length = hidden_states.shape[:2]
+            key_value_seq_length = key_value_states.shape[1]
+        else:
+            query_seq_length, batch_size = hidden_states.shape[:2]
+            key_value_seq_length = key_value_states.shape[0]
+        num_heads = self.mha.num_heads
+        attention_mask = attention_mask_handler(
+            attention_mask, batch_size, num_heads, query_seq_length, key_value_seq_length)
         attn_output, attn_output_weights = self.mha(
             hidden_states,
             key_value_states,