Upload modeling_phi.py

modeling_phi.py

@@ -358,7 +358,9 @@ class PhiAttention(nn.Module):
         key_states = repeat_kv(key_states, self.num_key_value_groups)
         value_states = repeat_kv(value_states, self.num_key_value_groups)
 
-        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+        attn_weights = torch.matmul(
+            query_states.to(torch.float32), key_states.to(torch.float32).transpose(2, 3)
+        ) / math.sqrt(self.head_dim)
 
         if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
             raise ValueError(
@@ -374,7 +376,7 @@ class PhiAttention(nn.Module):
             attn_weights = attn_weights + attention_mask
 
         # upcast attention to fp32
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_states.dtype)
         attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
 
         attn_output = torch.matmul(attn_weights, value_states)
@@ -483,8 +485,10 @@ class PhiFlashAttention2(PhiAttention):
         # in fp32.
 
         if query_states.dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
             # Handle the case where the model is quantized
-            if hasattr(self.config, "_pre_quantization_dtype"):
+            elif hasattr(self.config, "_pre_quantization_dtype"):
                 target_dtype = self.config._pre_quantization_dtype
             else:
                 target_dtype = self.q_proj.weight.dtype
@@ -1093,7 +1097,7 @@ class PhiForCausalLM(PhiPreTrainedModel):
 
             # Keep only the unprocessed tokens:
             # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
-            # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
             # input)
             if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
                 input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
@@ -1225,9 +1229,10 @@ class PhiForSequenceClassification(PhiPreTrainedModel):
             sequence_lengths = -1
         else:
             if input_ids is not None:
-                sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
-                    logits.device
-                )
+                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                sequence_lengths = sequence_lengths % input_ids.shape[-1]
+                sequence_lengths = sequence_lengths.to(logits.device)
             else:
                 sequence_lengths = -1