Correct input sizes

awq/models/llama.py

@@ -99,6 +99,7 @@ class LlamaFuser:
             attn = QuantLlamaAttention(
                 module.hidden_size,
                 module.num_heads,
+                module.num_key_value_heads,
                 qkv_layer,
                 module.o_proj,
                 qkv_layer.qweight.device,

awq/modules/fused_attn.py

@@ -30,6 +30,7 @@ class QuantLlamaRotaryEmbedding(nn.Module):
         sin = freqs.sin()
         cache = torch.cat((cos, sin), dim=-1)
         
+        # [max_position, rot_dim]
         self.register_buffer("cos_sin_cache", cache.half(), persistent=False)
     
     def forward(
@@ -38,11 +39,6 @@ class QuantLlamaRotaryEmbedding(nn.Module):
         key: torch.Tensor,
         positions: torch.Tensor,
     ):
-        batch_size, seq_len, _ = query.shape
-        query = query.view(batch_size * seq_len, -1)
-        key = key.view(batch_size * seq_len, -1)
-        positions = positions.view(-1).to(query.device)
-
         # Apply rotary embedding to the query and key before passing them
         # to the attention op.
         query = query.contiguous()
@@ -57,9 +53,6 @@ class QuantLlamaRotaryEmbedding(nn.Module):
             True # is_neox
         )
 
-        query = query.view(batch_size, seq_len, -1)
-        key = key.view(batch_size, seq_len, -1)
-
         return query, key
 
 class QuantLlamaAttention(nn.Module):
@@ -69,15 +62,17 @@ class QuantLlamaAttention(nn.Module):
         self,
         hidden_size,
         num_heads,
+        num_kv_heads,
         qkv_proj,
         o_proj,
         dev,
         max_new_tokens,
-        use_hf_rotary=True
+        use_hf_rotary=False
     ):
         super().__init__()
         self.hidden_size = hidden_size
         self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads
         self.head_dim = hidden_size // num_heads
         self.use_hf_rotary = use_hf_rotary
 
@@ -100,32 +95,44 @@ class QuantLlamaAttention(nn.Module):
         qkv_states = self.qkv_proj(hidden_states)
 
         if self.use_hf_rotary:
+            # get qkv
             qkv_states = qkv_states.view(bsz, q_len, 3, self.num_heads, self.head_dim)
-
-            # This updates the query and key states in-place, saving VRAM.
-            query_states, key_states, value_states = torch.split(qkv_states, 1, dim=2)
+            query, key, value = torch.split(qkv_states, 1, dim=2)
+            del qkv_states
             
-            query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-            key_states = key_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-            value_states = value_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+            # reshape for hf rotary
+            query = query.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+            key = key.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+            value = value.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
 
-            kv_seq_len = key_states.shape[-2]
+            kv_seq_len = key.shape[-2]
             if past_key_value is not None:
                 kv_seq_len += past_key_value[0].shape[-2]
             
-            cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+            cos, sin = self.rotary_emb(value, seq_len=kv_seq_len)
+            query, key = apply_rotary_pos_emb(query, key, cos, sin, position_ids)
 
         else:
-            query_states, key_states, value_states = qkv_states.chunk(chunks=3, dim=-1)
-            query_states, key_states = self.rotary_emb(query_states, key_states, position_ids)
+            # get qkv
+            query, key, value = qkv_states.chunk(chunks=3, dim=-1)
+            del qkv_states
 
-            query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-            key_states = key_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-            value_states = value_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-            
-        
-        del qkv_states
+            # [num_tokens, num_heads * head_size]
+            query_batch_size, query_len, _ = query.shape
+            query = query.view(query_len*query_batch_size, self.num_heads * self.head_dim)
+
+            # [num_tokens, num_kv_heads * head_size]
+            key_batch_size, key_len, _ = key.shape
+            key = key.view(key_len*key_batch_size, self.num_kv_heads * self.head_dim)
+
+            # [num_tokens]
+            positions = position_ids.view(-1).to(query.device)
+
+            query, key = self.rotary_emb(query, key, positions)
+
+            query = query.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+            key = key.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+            value = value.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
         
         is_causal = past_key_value is None
 
@@ -133,25 +140,25 @@ class QuantLlamaAttention(nn.Module):
         if past_key_value is not None:
             kv_seq_len += past_key_value[0].shape[-2]
         
-        value_states = value_states.to(key_states.device)
+        value = value.to(key.device)
 
         if past_key_value is not None:
             # reuse k, v, self_attention
-            key_states = torch.cat([past_key_value[0], key_states], dim=2)
-            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+            key = torch.cat([past_key_value[0], key], dim=2)
+            value = torch.cat([past_key_value[1], value], dim=2)
 
         if use_cache:
-            # Since qkv_proj is fused, query_states etc will hold a reference to the original qkv_states tensor
+            # Since qkv_proj is fused, query etc will hold a reference to the original qkv_states tensor
             # which can cause excessive memory usage by the cache. `contiguous` is a convenient way to workaround this.
-            key_states = key_states.contiguous()
-            value_states = value_states.contiguous()
-            query_states = query_states.contiguous()
+            key = key.contiguous()
+            value = value.contiguous()
+            query = query.contiguous()
 
-        past_key_value = (key_states, value_states) if use_cache else None
+        past_key_value = (key, value) if use_cache else None
 
         # with torch.backends.cuda.sdp_kernel(enable_math=False):
-        attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, is_causal=is_causal)
-        del query_states, key_states, value_states
+        attn_output = F.scaled_dot_product_attention(query, key, value, is_causal=is_causal)
+        del query, key, value
 
         attn_output = attn_output.transpose(1, 2).reshape(bsz, q_len, self.hidden_size)
         attn_output = self.o_proj(attn_output)