enable autoTP for mpt in huggingface model hub without trust_remote_c…

deepspeed/inference/engine.py

@@ -26,7 +26,7 @@
 from ..module_inject.auto_tp import AutoTP
 
 from ..module_inject.replace_policy import generic_policies
-from ..module_inject.auto_tp_model_utils import build_bloom_alibi_tensor, build_mpt_atten_bias_tensor
+from ..module_inject.auto_tp_model_utils import build_bloom_alibi_tensor, build_mpt_atten_bias_tensor, build_mpt_alibi_tensor
 
 DS_INFERENCE_ENABLED = False
 from torch import nn
@@ -187,6 +187,9 @@ def build_alibi_tensor(self):
         if hasattr(self.module, 'transformer'):
             if hasattr(self.module.transformer, 'build_alibi_tensor'):
                 self.module.transformer.build_alibi_tensor = build_bloom_alibi_tensor
+            if hasattr(self.module.transformer, 'build_mpt_alibi_tensor'):
+                self.module.transformer.build_mpt_alibi_tensor_orig = self.module.transformer.build_mpt_alibi_tensor
+                self.module.transformer.__class__.build_mpt_alibi_tensor = build_mpt_alibi_tensor
 
     def build_attn_bias(self):
         if hasattr(self.module, 'transformer'):

deepspeed/module_inject/auto_tp.py

@@ -296,21 +296,6 @@ def _replace(self, child, name, conv_linear_layer):
         if getattr(child, "replaced", False) == True:
             return
         weight_shape = child.weight.shape
-        if name == 'attn.Wqkv' and self.module._get_name() == 'MPTBlock':
-            # MPT block qkv weight's allocation is different from other models, it's [3,num_head,head_dim,hidden_size]
-            # instead of [num_head,3,head_dim,hidden_size]
-            new_weight = torch.empty((
-                weight_shape[0] // self.mp_size,
-                weight_shape[1],
-            ),
-                                     device=child.weight.device,
-                                     dtype=child.weight.dtype)
-            reversed_dim = True
-            mp_replace = ReplaceWithTensorSlicing(mp_group=self.mp_group, out_dim=0)
-            # todo: can we remove new tensor allocation if we use strided copy?
-            mp_replace.strided_copy(new_weight, child.weight.data, num_splits=3, int8=reversed_dim)
-            setattr(child, "replaced", True)
-            return LinearLayer(weight=new_weight.to(get_accelerator().current_device_name()), bias=None)
         mp_replace = ReplaceWithTensorSlicing(mp_group=self.mp_group)
         if name in self.all_reduce_linears:
             # if conv_linear_layer [weight_shape[1], weight_shape[0] // mp_size]

deepspeed/module_inject/auto_tp_model_utils.py

@@ -76,3 +76,18 @@ def build_mpt_atten_bias_tensor(self,
         offset = dist.get_rank() * num_heads_per_rank
         attn_bias = attn_bias[:, offset:num_heads_per_rank + offset, :, :]
     return attn_bias, attention_mask
+
+
+def build_mpt_alibi_tensor(self, num_heads, sequence_length, alibi_bias_max=8, device=None) -> torch.Tensor:
+    r"""
+    Link to paper: https://arxiv.org/abs/2108.12409 - Alibi tensor is not causal as the original paper mentions, it
+    relies on a translation invariance of softmax for quick implementation. This implementation has been copied from
+    the alibi implementation of MPT source code that led to slightly different results than the Bloom alibi:
+    https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L292
+    """
+    alibi = self.build_mpt_alibi_tensor_orig(num_heads, sequence_length, alibi_bias_max, device)
+    if dist.is_initialized():
+        num_heads_per_rank = int(num_heads / dist.get_world_size())
+        offset = dist.get_rank() * num_heads_per_rank
+        alibi = alibi[offset:num_heads_per_rank + offset, :, :]
+    return alibi

deepspeed/module_inject/fusedqkv_utils.py

@@ -16,7 +16,7 @@ def split_by_qkvlist_and_refuse(qkv_list, split_size, split_dim=0, cat_dim=0):
 
 
 def require_tp_fused_qkvw(name, mp_size):
-    fused_qkvw_name_list = ['qkv_proj', 'query_key_value']
+    fused_qkvw_name_list = ['qkv_proj', 'query_key_value', 'attn.Wqkv']
 
     if mp_size == 1:
         return False
@@ -33,6 +33,8 @@ def prepare_tp_fused_qkvw(module_str, src, mp_size, gpu_index):
         'CodeGenBlock': 'codegentype',
         'BloomBlock': 'bloomtype',
         'GLMBlock': 'glmtype',
+        "MPTBlock": 'glmtype',
+        "MptBlock": 'glmtype',
     }
 
     def _codegen_type_transpose(input, mp_size, codegen_mp_num=4):