use new fused layer norm

python/llm/src/ipex_llm/transformers/convert.py

@@ -1296,10 +1296,9 @@ def _optimize_post(model, lightweight_bmm=False):
     trans_version = transformers.__version__
 
     # convert all nn.LayerNorm
-    from ipex_llm.transformers.models.bloom import bloom_layer_norm_forward
-    convert_forward(model,
-                    nn.LayerNorm,
-                    bloom_layer_norm_forward)
+    from ipex_llm.transformers.models.common import layer_norm_forward
+    convert_forward(model, nn.LayerNorm, layer_norm_forward)
+
     from ipex_llm.transformers.models.llama import llama_rms_norm_forward
     from ipex_llm.transformers.models.llama import llama_mlp_forward
 

python/llm/src/ipex_llm/transformers/models/bloom.py

@@ -64,23 +64,6 @@ def dropout_add(x: torch.Tensor, residual: torch.Tensor, prob: float, training:
     return out
 
 
-def bloom_layer_norm_forward(self, hidden_states):
-    if use_fused_layer_norm(hidden_states, self.training):
-        import xe_addons
-        result = xe_addons.fused_layer_norm(hidden_states,
-                                            [self.weight.size(0)],
-                                            self.weight,
-                                            self.bias,
-                                            self.eps)
-        # if nelement == 0, means fused norm failed, go back to python implement.
-        if result.nelement != 0:
-            return result
-    input_dtype = hidden_states.dtype
-    result = F.layer_norm(hidden_states.to(self.weight.dtype),
-                          self.normalized_shape, self.weight, self.bias, self.eps)
-    return result.to(input_dtype)
-
-
 def bloom_attention_forward(
         self,
         hidden_states: torch.Tensor,

python/llm/src/ipex_llm/transformers/models/common.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 
 
+import math
 import torch
 from typing import List
 
@@ -159,7 +160,7 @@ def rms_norm_forward(self, hidden_states: torch.Tensor):
     else:
         eps = self.epsilon
 
-    if hidden_states.device.type == 'xpu':
+    if hidden_states.device.type == 'xpu' and hidden_states.dtype in [torch.float, torch.half]:
         import xe_addons
         x_2d = hidden_states.reshape(-1, hidden_states.size(-1)).contiguous()
         output = xe_addons.rms_norm(weight, x_2d, eps)
@@ -169,3 +170,17 @@ def rms_norm_forward(self, hidden_states: torch.Tensor):
         variance = hidden_states.to(torch.float32).pow(2).mean(dim=-1, keepdim=True)
         hidden_states = hidden_states * torch.rsqrt(variance + eps)
         return weight * hidden_states.to(input_dtype)
+
+
+def layer_norm_forward(self, hidden_states: torch.Tensor):
+    if hidden_states.device.type == 'xpu' and hidden_states.dtype in [torch.float, torch.half]:
+        import xe_addons
+        hidden_size = math.prod(self.normalized_shape)
+        x_2d = hidden_states.reshape(-1, hidden_size).contiguous()
+        output = xe_addons.layer_norm(x_2d, self.weight, self.bias, self.eps)
+        return output.reshape(hidden_states.shape)
+    else:
+        return torch.nn.functional.layer_norm(
+            hidden_states, self.normalized_shape,
+            self.weight, self.bias, self.eps
+        )

python/llm/test/inference_gpu/test_transformers_api_layernorm.py

@@ -13,39 +13,39 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
- 
+
 import os
 import pytest
 import gc
- 
+
 import torch
 from ipex_llm.transformers import AutoModelForCausalLM, AutoModel
 from transformers import LlamaTokenizer, AutoTokenizer
- 
+
 device = os.environ['DEVICE']
 print(f'Running on {device}')
- 
+
 PROMPT = "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun"
 TEST_MODEL_LIST = [
     ("Falcon-7B", AutoModelForCausalLM, AutoTokenizer, os.environ.get('FALCON_7B_ORIGIN_PATH'))
 ]
- 
+
 class Test_Optimize_Gpu_Model:
     def setup_method(self):
         self.layer_outputs = []
         self.pre_layer_outputs = []
- 
+
     def run_optimize_gpu_model(self, Name, Model, Tokenizer, model_path, LayerNorm_layer, layer_before_LayerNorm, lower_bound):
         with torch.inference_mode():
             def pre_forward_hook(module, input, output, layer_name):
                 self.pre_layer_outputs.append(output)
-                
+
             def forward_hook(module, input, output, layer_name):
                 self.layer_outputs.append(output)
- 
+
             tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
             input_ids = tokenizer.encode(PROMPT, return_tensors="pt").to(device)
- 
+
             model = Model.from_pretrained(model_path,
                                         load_in_4bit=True,
                                         optimize_model=False,
@@ -64,18 +64,18 @@ def forward_hook(module, input, output, layer_name):
             # the list `layer_output` has only one element.
             layer_tensor = self.layer_outputs.pop()
             model.to('cpu')
- 
+
             opt_model = Model.from_pretrained(model_path,
                                             load_in_4bit=True,
                                             optimize_model=True,
                                             trust_remote_code=True)
             opt_model = opt_model.to(device)
- 
- 
+
+
             def replace_forward_hook(module, input, output, layer_name):
                 output = self.pre_layer_outputs[0]
                 return output
- 
+
             for layer_name, layer_module in opt_model.named_modules():
                 if layer_name == layer_before_LayerNorm:
                     layer_module.register_forward_hook(
@@ -89,29 +89,29 @@ def replace_forward_hook(module, input, output, layer_name):
             # the list `layer_output` has only one element.
             opt_layer_tensor = self.layer_outputs[0]
             opt_model.to('cpu')
- 
- 
+
+
             LayerNorm_output_diff = []
             for i, (t1, t2) in enumerate(zip(layer_tensor, opt_layer_tensor)):
                 LayerNorm_output_diff.append(t1 - t2)
- 
+
             max_diff_tensor = [torch.max(item).item() for item in LayerNorm_output_diff]
             print(max_diff_tensor)
             torch.xpu.empty_cache()
             del model
             del opt_model
             gc.collect()
             assert all(max_diff <= lower_bound for max_diff in max_diff_tensor)
-   
+
     @pytest.mark.parametrize('Name, Model, Tokenizer, model_path',TEST_MODEL_LIST)
     def test_dynamic_functions(self, Name, Model, Tokenizer, model_path):
         if Name == "Falcon-7B":
             self.Falcon_7B_gpu_model(Name, Model, Tokenizer, model_path)
 
-    
+
     def Falcon_7B_gpu_model(self, Name, Model, Tokenizer, model_path):
         # currently only compare the output of the last LayerNorm layer.
         layer_before_LayerNorm = "transformer.h.30"
         LayerNorm_layer = "transformer.h.31.input_layernorm"
-        lower_bound = 0
+        lower_bound = 1e-5
         self.run_optimize_gpu_model(Name, Model, Tokenizer, model_path, LayerNorm_layer, layer_before_LayerNorm, lower_bound)