Propagate bias through model (#627)

llmfoundry/models/layers/attention.py

@@ -5,7 +5,7 @@
 
 import math
 import warnings
-from typing import List, Optional, Tuple
+from typing import Any, List, Optional, Tuple
 
 import torch
 import torch.nn as nn
@@ -419,6 +419,7 @@ def __init__(
         norm_type: str = 'low_precision_layernorm',
         fc_type: str = 'torch',
         device: Optional[str] = None,
+        bias: bool = True,
     ):
         super().__init__()
 
@@ -450,7 +451,9 @@ def __init__(
             self.softmax_scale = 1 / math.sqrt(self.d_model / self.n_heads)
         self.attn_dropout_p = attn_pdrop
 
-        fc_kwargs = {}
+        fc_kwargs: dict[str, Any] = {
+            'bias': bias,
+        }
         if fc_type != 'te':
             fc_kwargs['device'] = device
         self.Wqkv = FC_CLASS_REGISTRY[fc_type](
@@ -557,6 +560,7 @@ def __init__(
         norm_type: str = 'low_precision_layernorm',
         fc_type: str = 'torch',
         device: Optional[str] = None,
+        bias: bool = True,
     ):
         super().__init__(
             d_model=d_model,
@@ -569,7 +573,9 @@ def __init__(
             attn_pdrop=attn_pdrop,
             norm_type=norm_type,
             fc_type=fc_type,
-            device=device)
+            device=device,
+            bias=bias,
+        )
 
 
 class MultiQueryAttention(GroupedQueryAttention):
@@ -591,6 +597,7 @@ def __init__(
         norm_type: str = 'low_precision_layernorm',
         fc_type: str = 'torch',
         device: Optional[str] = None,
+        bias: bool = True,
     ):
         super().__init__(
             d_model=d_model,
@@ -603,7 +610,9 @@ def __init__(
             attn_pdrop=attn_pdrop,
             norm_type=norm_type,
             fc_type=fc_type,
-            device=device)
+            device=device,
+            bias=bias,
+        )
 
 
 def attn_bias_shape(

llmfoundry/models/layers/blocks.py

@@ -26,6 +26,7 @@ def __init__(
         norm_type: str = 'low_precision_layernorm',
         fc_type: str = 'torch',
         device: Optional[str] = None,
+        no_bias: bool = False,
         **kwargs: Any,
     ):
         if attn_config is None:
@@ -66,11 +67,14 @@ def __init__(
         }
 
         self.norm_1 = norm_class(d_model, device=device)
-        self.attn = attn_class(d_model=d_model,
-                               n_heads=n_heads,
-                               fc_type=fc_type,
-                               device=device,
-                               **attn_config_subset_for_attn_class)
+        self.attn = attn_class(
+            d_model=d_model,
+            n_heads=n_heads,
+            fc_type=fc_type,
+            device=device,
+            **attn_config_subset_for_attn_class,
+            bias=not no_bias,
+        )
         self.norm_2 = None
         if not getattr(FFN_CLASS_REGISTRY[ffn_config['ffn_type']], '_has_norm',
                        False):
@@ -79,6 +83,7 @@ def __init__(
             d_model=d_model,
             expansion_ratio=expansion_ratio,
             device=device,
+            bias=not no_bias,
             **ffn_config,
         )
         self.resid_attn_dropout = nn.Dropout(resid_pdrop)

llmfoundry/models/layers/ffn.py

@@ -24,9 +24,12 @@ def __init__(
         expansion_ratio: int,
         fc_type: str = 'torch',
         device: Optional[str] = None,
+        bias: bool = True,
     ):
         super().__init__()
-        fc_kwargs = {}
+        fc_kwargs: dict[str, Any] = {
+            'bias': bias,
+        }
         if fc_type != 'te':
             fc_kwargs['device'] = device
         self.up_proj = FC_CLASS_REGISTRY[fc_type](
@@ -60,6 +63,7 @@ def build_ffn(
     expansion_ratio: int,
     fc_type: str = 'torch',
     device: Optional[str] = None,
+    bias: bool = True,
     **kwargs: Any,
 ) -> nn.Module:
     ffn_type = kwargs.pop('ffn_type')
@@ -72,12 +76,14 @@ def build_ffn(
             expansion_ratio=expansion_ratio,
             fc_type=fc_type,
             device=device,
+            bias=bias,
         )
     elif ffn_type == 'te_ln_mlp':
         assert te is not None
         return te.LayerNormMLP(
             hidden_size=d_model,
             ffn_hidden_size=d_model * expansion_ratio,
+            bias=bias,
             **kwargs,
         )
 

llmfoundry/models/mpt/modeling_mpt.py

@@ -150,6 +150,11 @@ def __init__(self, config: MPTConfig):
                     log.info(f'Removing bias ({module.bias}) from {module}.')
                     module.register_parameter('bias', None)
 
+                # For transformer engine
+                if hasattr(module, 'use_bias'):
+                    log.info(f'Setting use_bias=False for {module}.')
+                    module.use_bias = False
+
         log.debug(self)
         log.debug(f'Using {self.config.init_config["name"]} initialization.')
 

setup.py

@@ -89,7 +89,7 @@
     'flash-attn==1.0.9',
     'mosaicml-turbo==0.0.4',
     # PyPI does not support direct dependencies, so we remove this line before uploading from PyPI
-    'xentropy-cuda-lib@git+https://github.com/HazyResearch/[email protected].3#subdirectory=csrc/xentropy',
+    'xentropy-cuda-lib@git+https://github.com/HazyResearch/[email protected].9#subdirectory=csrc/xentropy',
 ]
 
 extra_deps['peft'] = [