refactor glm edge

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

@@ -37,12 +37,12 @@
 
 from typing import Optional, Tuple
 from transformers.cache_utils import Cache
-from transformers.models.glm.modeling_glm import repeat_kv, apply_rotary_pos_emb
+from transformers.models.glm.modeling_glm import apply_rotary_pos_emb
 from ipex_llm.transformers.kv import DynamicNormalCache, DynamicFp8Cache
 from ipex_llm.transformers.models.common import merge_qkv_base
-from ipex_llm.transformers.models.utils import use_sdp, use_sdp_causal
+from ipex_llm.transformers.models.common import scaled_dot_product_attention
 from ipex_llm.transformers.models.utils import make_cache_contiguous_inplaced
-from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
+from ipex_llm.transformers.models.utils import use_quantize_kv_cache
 
 
 def merge_qkv(module: torch.nn.Module):
@@ -102,52 +102,16 @@ def glm_attention_forward(
     else:
         query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
 
-    use_quantizekv = isinstance(past_key_value, DynamicFp8Cache)
     # sin and cos are specific to RoPE models; cache_position needed for the static cache
     cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
     key_states, value_states = past_key_value.update(key_states, value_states,
                                                      self.layer_idx, cache_kwargs)
 
-    kv_seq_len = key_states.size(-2)
-    if attention_mask is not None:  # no matter the length, we just slice it
-        attention_mask = attention_mask[:, :, :, : kv_seq_len]
-
-    if use_sdp(q_len, kv_seq_len, self.head_dim, query_states):
-        import xe_addons
-        if use_quantizekv:
-            attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states,
-                                            attention_mask)
-        else:
-            attn_output = xe_addons.sdp(query_states, key_states, value_states,
-                                        attention_mask)
-    elif use_sdp_causal(q_len, kv_seq_len, self.head_dim, query_states, self.training):
-        import xe_addons
-        if use_quantizekv:
-            attn_output = xe_addons.sdp_fp8_causal(query_states, key_states,
-                                                   value_states, attention_mask)
-        else:
-            attn_output = xe_addons.sdp_causal(query_states, key_states,
-                                               value_states, attention_mask)
-    else:
-        if use_quantizekv:
-            key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
-                                                            query_states.dtype)
-        # repeat k/v heads if n_kv_heads < n_heads
-        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)) * self.scaling
-
-        if attention_mask is not None:
-            attn_weights = attn_weights + attention_mask
-
-        # upcast attention to fp32
-        attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1,
-                                                   dtype=torch.float32).to(query_states.dtype)
-        attn_weights = torch.nn.functional.dropout(attn_weights, p=self.attention_dropout,
-                                                   training=self.training)
-        attn_output = torch.matmul(attn_weights, value_states)
+    attn_weights = None
+    attn_output = scaled_dot_product_attention(
+        query_states, key_states, value_states,
+        attention_mask, q_len == key_states.size(2), self.scaling
+    )
 
     attn_output = attn_output.transpose(1, 2).contiguous()
     attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)