Feat (llm): QuantizableBert

src/brevitas_examples/llm/llm_quant/mha_layers.py

@@ -7,6 +7,26 @@
 from brevitas.utils.torch_utils import KwargsForwardHook
 
 
+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
+
+
 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
@@ -76,6 +96,41 @@ def num_heads(self):
     def batch_first(self):
         return self.wrapped_mha.batch_first
 
+
+class QuantizableOPTAttention(MultiheadAttentionWrapper):
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        key_value_states: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        layer_head_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if key_value_states is None:
+            key_value_states = hidden_states
+        if layer_head_mask is not None:
+            raise RuntimeError("layer_head_mask is not supported.")
+        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,
+            key_value_states,
+            attn_mask=attention_mask,
+            need_weights=output_attentions,
+            average_attn_weights=False)
+        past_key_value = None
+        return attn_output, attn_output_weights, past_key_value
+
     def _load_from_state_dict(
             self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
             error_msgs):
@@ -142,36 +197,104 @@ def set_weight(value):
             state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
 
 
-class QuantizableOPTAttention(MultiheadAttentionWrapper):
+class QuantizableBertAttention(MultiheadAttentionWrapper):
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
 
     def forward(
         self,
         hidden_states: torch.Tensor,
-        key_value_states: Optional[torch.Tensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        layer_head_mask: Optional[torch.Tensor] = None,
-        output_attentions: bool = False,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        output_attentions: Optional[bool] = False,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        if key_value_states is None:
-            key_value_states = hidden_states
-        if layer_head_mask is not None:
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        if encoder_attention_mask is not None:
+            attention_mask = encoder_attention_mask
+        if head_mask is not None:
             raise RuntimeError("layer_head_mask is not supported.")
-        if self.batch_first:
+
+        if self.mha.batch_first:
             batch_size, query_seq_length = hidden_states.shape[:2]
-            key_value_seq_length = key_value_states.shape[1]
+            key_value_seq_length = encoder_hidden_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.num_heads
+            key_value_seq_length = encoder_hidden_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,
-            key_value_states,
+            encoder_hidden_states,
+            encoder_hidden_states,
             attn_mask=attention_mask,
             need_weights=output_attentions,
             average_attn_weights=False)
         past_key_value = None
         return attn_output, attn_output_weights, past_key_value
+
+    def _load_from_state_dict(
+            self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
+            error_msgs):
+
+        def set_bias(value):
+            bias_name = f'{prefix}mha.in_proj_bias'
+            if bias_name in state_dict:
+                state_dict[bias_name] += value
+            else:
+                state_dict[bias_name] = value
+
+        def set_weight(value):
+            weight_name = f'{prefix}mha.in_proj_weight'
+            if weight_name in state_dict:
+                state_dict[weight_name] += value
+            else:
+                state_dict[weight_name] = value
+
+        embed_dim = self.mha.embed_dim
+        for name, value in list(state_dict.items()):
+            if prefix + 'query.weight' in name:
+                weight = torch.zeros((3 * embed_dim, embed_dim),
+                                     device=value.device,
+                                     dtype=value.dtype)
+                weight[:embed_dim] = value
+                set_weight(weight)
+                del state_dict[name]
+            elif prefix + 'key.weight' in name:
+                weight = torch.zeros((3 * embed_dim, embed_dim),
+                                     device=value.device,
+                                     dtype=value.dtype)
+                weight[embed_dim:2 * embed_dim] = value
+                set_weight(weight)
+                del state_dict[name]
+            elif prefix + 'value.weight' in name:
+                weight = torch.zeros((3 * embed_dim, embed_dim),
+                                     device=value.device,
+                                     dtype=value.dtype)
+                weight[2 * embed_dim:3 * embed_dim] = value
+                set_weight(weight)
+                del state_dict[name]
+            if prefix + 'query.bias' in name:
+                bias = torch.zeros(3 * embed_dim, device=value.device, dtype=value.dtype)
+                bias[:embed_dim] = value
+                set_bias(bias)
+                del state_dict[name]
+            elif prefix + 'key.bias' in name:
+                bias = torch.zeros(3 * embed_dim, device=value.device, dtype=value.dtype)
+                bias[embed_dim:2 * embed_dim] = value
+                set_bias(bias)
+                del state_dict[name]
+            elif prefix + 'value.bias' in name:
+                bias = torch.zeros(3 * embed_dim, device=value.device, dtype=value.dtype)
+                bias[2 * embed_dim:3 * embed_dim] = value
+                set_bias(bias)
+                del state_dict[name]
+        state_dict[prefix + 'mha.out_proj.weight'] = torch.eye(self.mha.out_proj.weight.shape[0])
+        state_dict[prefix + 'mha.out_proj.bias'] = torch.zeros(self.mha.out_proj.bias.shape)
+        return super()._load_from_state_dict(
+            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)