Use hf_rotary per default

awq/entry.py

@@ -122,9 +122,9 @@ def run_speed(model_path, quant_file, device, n_generate=128, n_context=256, bat
     # Prints
     memory_used = torch.cuda.max_memory_allocated(device) / (1024 ** 2)
     context_tokens_per_second = n_context / context_time * batch_size
-    context_ms_per_token = (context_time*1000) / n_context * batch_size
+    context_ms_per_token = (context_time*1000) / n_context / batch_size
     inference_tokens_per_second = n_generate / generation_time * batch_size
-    inference_ms_per_token = (generation_time*1000) / n_generate * batch_size
+    inference_ms_per_token = (generation_time*1000) / n_generate / batch_size
 
     print(f"[======] Model summary: {model_path} [======]")
     print(f"[*] Load time: {load_time:.2f} seconds")
@@ -185,9 +185,6 @@ if __name__ == '__main__':
         run_eval(args.model_path, args.quant_file, args.device,
                        args.tasks, args.task_batch_size, args.task_n_shot, args.task_use_pretrained)
     elif args.entry_type == 'speed':
-        if args.batch_size > 1 and not args.disable_fused_layers:
-            raise Exception('Fused layers only support batch_size=1. Pass --disable_fused_layers to run batch_size>1 (much slower).')
-        
         run_speed(args.model_path, args.quant_file, args.device, args.n_generate, args.n_context, args.batch_size, args.disable_fused_layers)
     else:
         raise Exception('--entry_type must be one of (search|quant|eval|speed)')

awq/modules/fused_attn.py

@@ -2,6 +2,7 @@ import torch
 import torch.nn as nn
 import awq_inference_engine
 from torch.nn import functional as F
+from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, LlamaRotaryEmbedding
 
 class QuantLlamaRotaryEmbedding(nn.Module):
     def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
@@ -41,6 +42,7 @@ class QuantLlamaRotaryEmbedding(nn.Module):
         # to the attention op.
         query = query.contiguous()
         key = key.contiguous()
+
         awq_inference_engine.rotary_embedding_neox(
             positions,
             query,
@@ -60,19 +62,25 @@ class QuantLlamaAttention(nn.Module):
         qkv_proj,
         o_proj,
         dev,
-        max_new_tokens
+        max_new_tokens,
+        use_hf_rotary=True
     ):
         super().__init__()
         self.hidden_size = hidden_size
         self.num_heads = num_heads
         self.head_dim = hidden_size // num_heads
+        self.use_hf_rotary = use_hf_rotary
 
         if (self.head_dim * num_heads) != self.hidden_size:
             raise ValueError(f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
                              f" and `num_heads`: {num_heads}).")
         self.qkv_proj = qkv_proj
         self.o_proj = o_proj
-        self.rotary_emb = QuantLlamaRotaryEmbedding(self.head_dim, max_position_embeddings=max_new_tokens, device = dev)
+
+        if use_hf_rotary:
+            self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_new_tokens, device=dev)
+        else:
+            self.rotary_emb = QuantLlamaRotaryEmbedding(self.head_dim, max_position_embeddings=max_new_tokens, device = dev)
 
     def forward(self, hidden_states, past_key_value=None, attention_mask=None, position_ids=None, output_attentions=False, use_cache=False):
         """Input shape: Batch x Time x Channel"""
@@ -84,13 +92,27 @@ class QuantLlamaAttention(nn.Module):
 
         # 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_states, key_states = self.rotary_emb(query_states, key_states, position_ids)
+
+        if self.use_hf_rotary:
+            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)
+
+            kv_seq_len = key_states.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)
+
+        else:
+            query_states, key_states = self.rotary_emb(query_states, key_states, position_ids)
+            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
-        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)
-
+        
         is_causal = past_key_value is None
 
         kv_seq_len = q_len