Merge pull request #902 from kvcache-ai/rollback-triton-prefill

rollback-triton-prefill

ktransformers/operators/attention.py

@@ -325,27 +325,18 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
             key_states[:, :, :, self.qk_nope_head_dim:] = k_pe.view(bsz, kv_seq_len, 1, -1)
             
             value_states = value_states.view(bsz, kv_seq_len, self.num_heads, self.v_head_dim)
+            value_states_padded = torch.nn.functional.pad(value_states, [0, query_states.shape[-1] - value_states.shape[-1]], value=0)
 
-            # for bsz = 1
-            attn_output = torch.zeros(bsz * q_len, self.num_heads, self.v_head_dim, device=hidden_states.device)
-            b_start_loc = torch.zeros(bsz, dtype=torch.int64, device=hidden_states.device) 
-            b_seq_len = torch.full((bsz,), q_len, dtype=torch.int64, device=hidden_states.device)
-
-            max_input_len = q_len
-
-            context_attention_fwd(
-                q=query_states.squeeze(0).view(-1, self.num_heads, self.q_head_dim),
-                k=key_states.squeeze(0).view(-1, self.num_heads, self.q_head_dim),
-                v=value_states.squeeze(0).view(-1, self.num_heads, self.v_head_dim),
-                o=attn_output,
-                b_start_loc=b_start_loc,
-                b_seq_len=b_seq_len,
-                max_input_len=max_input_len,
-                is_causal=True
+            attn_output = flash_attn_func(
+                query_states,
+                key_states,
+                value_states_padded,
+                softmax_scale=self.softmax_scale,
+                causal=True,
             )
 
             if self.q_head_dim != self.v_head_dim:
-                attn_output = attn_output[:, :, : self.v_head_dim]
+                attn_output = attn_output[:, :, :, : self.v_head_dim]
 
             attn_output = attn_output.reshape(
                 bsz, q_len, self.num_heads * self.v_head_dim