Align GLU implementation with LLaMa

llmfoundry/models/layers/ffn.py

@@ -117,7 +117,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         return self.down_proj(self.act(self.up_proj(x)))
 
 
-class MPTGeGLU(MPTMLP):
+class MPTGLU(MPTMLP):
 
     def __init__(
         self,
@@ -138,19 +138,19 @@ def __init__(
             device=device,
             bias=bias,
         )
-        self.gate = FC_CLASS_REGISTRY[fc_type](
+        self.gate_proj = FC_CLASS_REGISTRY[fc_type](
             d_model,
             self.up_proj.out_features,
             **self.fc_kwargs,
         )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.down_proj(self.act(self.up_proj(x)) * self.gate(x))
+        return self.down_proj(self.act(self.gate_proj(x)) * self.up_proj(x))
 
 
 FFN_CLASS_REGISTRY = {
     'mptmlp': MPTMLP,
-    'mptgeglu': MPTGeGLU,
+    'mptglu': MPTGLU,
 }
 
 if te is not None:
@@ -169,10 +169,10 @@ def build_ffn(
     **kwargs: Any,
 ) -> nn.Module:
     ffn_type = kwargs.pop('ffn_type')
-    if ffn_type in ['mptmlp', 'mptgeglu']:
+    if ffn_type in ['mptmlp', 'mptglu']:
         if len(kwargs) > 0:
             raise ValueError(
-                f'MPTMLP (or MPTGeGLU) got an unexpected keyword argument: {kwargs}'
+                f'MPTMLP (or MPTGLU) got an unexpected keyword argument: {kwargs}'
             )
         return FFN_CLASS_REGISTRY[ffn_type](
             d_model=d_model,

llmfoundry/models/mpt/configuration_mpt.py

@@ -107,7 +107,7 @@ def __init__(
                     factor (float): Scaling factor to use if using 'linear' or 'dynamic' as rope_scaling.type.
                 kv_n_heads (Optional[int]): For grouped_query_attention only, allow user to specify number of kv heads.
             ffn_config (Dict): A dictionary used to configure the model's ffn module:
-                ffn_type (str): type of ffn to use. Options: mptmlp, mptgeglu, te_ln_mlp
+                ffn_type (str): type of ffn to use. Options: mptmlp, mptglu, te_ln_mlp
             init_device (str): The device to use for parameter initialization.
             logit_scale (Optional[Union[float, str]]): If not None, scale the logits by this value.
             no_bias (bool): Whether to use bias in all layers.
@@ -291,7 +291,7 @@ def _validate_config(self) -> None:
                     + 'pip install flash-attn==1.0.6 --no-build-isolation \n' +
                     'pip install git+https://github.com/NVIDIA/TransformerEngine.git@144e4888b2cdd60bd52e706d5b7a79cb9c1a7156'
                 )
-        if self.ffn_config['ffn_type'] in ['mptmlp', 'mptgeglu']:
+        if self.ffn_config['ffn_type'] in ['mptmlp', 'mptglu']:
             self.ffn_config['fc_type'] = self.fc_type
         elif self.ffn_config['ffn_type'] == 'te_ln_mlp':
             self.ffn_config['bias'] = not self.no_bias

tests/models/test_model.py

@@ -350,7 +350,7 @@ def test_full_forward_and_backward_t5_small(batch_size: int = 2):
     [('torch', torch.float16), ('torch', torch.bfloat16),
      pytest.param('flash', torch.float16, marks=pytest.mark.gpu),
      pytest.param('flash', torch.bfloat16, marks=pytest.mark.gpu)])
-@pytest.mark.parametrize('ffn_type', ['mptmlp', 'mptgeglu'])
+@pytest.mark.parametrize('ffn_type', ['mptmlp', 'mptglu'])
 @pytest.mark.parametrize('ffn_act_fn', [
     None,
     {