Update llama.py

unsloth/models/llama.py

@@ -416,7 +416,6 @@ def LlamaDecoderLayer_fast_forward(
         past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
     """
     if past_key_value is not None and hasattr(self.self_attn, "paged_attention"):
-        print("1", end = "")
         # Self Attention
         residual = hidden_states
         hidden_states = fast_rms_layernorm_inference(self.input_layernorm, hidden_states)
@@ -434,7 +433,6 @@ def LlamaDecoderLayer_fast_forward(
         hidden_states = fast_mlp_inference(self.mlp, hidden_states)
         hidden_states += residual
     elif past_key_value is not None:
-        print("0", end = "")
         # Self Attention
         residual = hidden_states
         hidden_states = fast_rms_layernorm_inference(self.input_layernorm, hidden_states)
@@ -680,6 +678,51 @@ def LlamaModel_fast_forward(
 pass
 
 
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L825
+@torch.compile
+@torch.inference_mode
+def LlamaModel_fast_forward_inference(
+    self,
+    input_ids
+    past_key_values,
+):
+    # Fix out of bounds tokenization
+    input_ids = input_ids[:,:self.max_seq_length]
+
+    hidden_states = self.embed_tokens(input_ids)
+
+    next_decoder_cache = []
+    for idx, decoder_layer in enumerate(self.layers):
+        # Self Attention
+        residual = hidden_states
+        hidden_states = fast_rms_layernorm_inference(decoder_layer.input_layernorm, hidden_states)
+        hidden_states, present_key_value = LlamaAttention_fast_forward_inference(
+            decoder_layer.self_attn,
+            hidden_states,
+            past_key_values[idx],
+            position_ids,
+        )
+        hidden_states += residual
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = fast_rms_layernorm_inference(decoder_layer.post_attention_layernorm, hidden_states)
+        hidden_states = fast_mlp_inference(decoder_layer.mlp, hidden_states)
+        hidden_states += residual
+
+        next_decoder_cache.append(present_key_value)
+    pass
+    hidden_states = fast_rms_layernorm_inference(self.norm, hidden_states)
+
+    return BaseModelOutputWithPast(
+        last_hidden_state = hidden_states,
+        past_key_values   = next_decoder_cache,
+        hidden_states     = [],
+        attentions        = [],
+    )
+pass
+
+
 def LlamaForCausalLM_fast_forward(
     self,
     input_ids: torch.LongTensor = None,
@@ -707,18 +750,29 @@ def LlamaForCausalLM_fast_forward(
 
     # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
     self.model._has_no_labels = labels is None
-    outputs = self.model(
-        input_ids=input_ids,
-        causal_mask=causal_mask,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-    )
+
+    if past_key_value is not None and \
+        hasattr(self.model.layers[0].self_attn, "paged_attention"):
+        
+        outputs = LlamaModel_fast_forward_inference(
+            self.model,
+            input_ids,
+            past_key_values,
+        )
+    else:
+        outputs = self.model(
+            input_ids=input_ids,
+            causal_mask=causal_mask,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+    pass
 
     hidden_states = outputs[0]
     bsz, q_len, hd = hidden_states.shape