ccy/update ops interface

lmdeploy/pytorch/engine/devices/ascend.py

@@ -1,10 +1,10 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
 
-from .dipu import DIPUDeviceUtils
+from .base_device_utils import BaseDeviceUtils
 
 
-class ASCENDDeviceUtils(DIPUDeviceUtils):
+class ASCENDDeviceUtils(BaseDeviceUtils):
 
     device = 'ascend'
 
@@ -38,4 +38,7 @@ def update_step_context(cls, step_context):
             kv_start_indices, device=step_context.block_offsets.device)
         setattr(step_context, 'kv_start_indices', kv_start_indices)
         setattr(step_context, 'attention_mask', attention_mask)
+        is_unpaged_prefill = (not step_context.is_decoding) and all(
+            (step_context.q_seq_length == step_context.kv_seq_length).tolist())
+        setattr(step_context, 'is_unpaged_prefill', is_unpaged_prefill)
         return step_context

lmdeploy/pytorch/kernels/ascend/apply_rotary_pos_emb.py

@@ -27,7 +27,7 @@ def apply_rotary_pos_emb(
     cached_cos = context.cos if context else cos
     cached_sin = context.sin if context else sin
     ext_ops.apply_rotary_pos_emb(query_states_reshaped, key_states_reshaped,
-                                 cached_cos, cached_sin, None, None, None)
+                                 cached_cos, cached_sin, None, None)
     if q_embed is None:
         q_embed = query_states
     else:

lmdeploy/pytorch/kernels/ascend/fused_rotary_emb.py

@@ -33,7 +33,7 @@ def fused_rotary_emb(
     cached_cos = context.cos if context else cos
     cached_sin = context.sin if context else sin
     ext_ops.apply_rotary_pos_emb(query_states_reshaped, key_states_reshaped,
-                                 cached_cos, cached_sin, None, None, None)
+                                 cached_cos, cached_sin, None, None)
     if out_q is None:
         out_q = query_states
     else:

lmdeploy/pytorch/kernels/ascend/paged_attention_fwd.py

@@ -21,43 +21,41 @@ def flash_context_attention(
 ):
     num_q_heads, dim = query_states.shape[1:3]
     num_kv_heads = value_states.shape[1]
-    batch = q_start_loc.shape[0]
 
-    for i in range(batch):
-        if torch.equal(q_seq_len[i], kv_seq_len[i]):
-            ext_ops.context_attention(
-                query_states,
-                key_states,
-                value_states,
-                q_start_loc[i:i + 1],
-                q_seq_len[i:i + 1],
-                num_q_heads,
-                num_kv_heads,
-                attn_mask=context.attention_mask[i:i + 1],
-                attn_output=attn_output,
-            )
-        else:
-            key_cache = key_cache.reshape(1, kv_cache_len, num_kv_heads * dim)
-            value_cache = value_cache.reshape(1, kv_cache_len,
-                                              num_kv_heads * dim)
-            ext_ops.paged_prefill_attention(
-                query_states,
-                key_cache,
-                value_cache,
-                block_offsets,
-                block_size,
-                q_start_loc[i:i + 1],
-                q_seq_len[i:i + 1],
-                kv_seq_len[i:i + 1],
-                num_q_heads,
-                num_kv_heads,
-                attn_mask=context.attention_mask[i:i + 1],
-                attn_output=attn_output,
-            )
+    if context.is_unpaged_prefill:
+        ext_ops.prefill_attention(
+            query_states,
+            key_states,
+            value_states,
+            q_start_loc,
+            q_seq_len,
+            context.max_q_seq_length,
+            num_q_heads,
+            num_kv_heads,
+            attn_mask=context.attention_mask,
+            attn_output=attn_output,
+        )
+    else:
+        key_cache = key_cache.reshape(1, kv_cache_len, num_kv_heads * dim)
+        value_cache = value_cache.reshape(1, kv_cache_len, num_kv_heads * dim)
+        ext_ops.paged_prefill_attention(
+            query_states,
+            key_cache,
+            value_cache,
+            block_offsets,
+            block_size,
+            q_start_loc,
+            q_seq_len,
+            kv_seq_len,
+            num_q_heads,
+            num_kv_heads,
+            attn_mask=context.attention_mask,
+            attn_output=attn_output,
+        )
 
 
 def paged_token_attention(q, k_cache, v_cache, attn_output, kv_seq_len,
-                          block_offsets, block_size):
+                          max_kv_seq_len, block_offsets, block_size):
     num_kv_heads, num_q_heads = k_cache.shape[1], q.shape[1]
     ext_ops.paged_decode_attention(
         q,
@@ -66,6 +64,7 @@ def paged_token_attention(q, k_cache, v_cache, attn_output, kv_seq_len,
         block_offsets,
         block_size,
         kv_seq_len,
+        max_kv_seq_len,
         num_q_heads,
         num_kv_heads,
         attn_output=attn_output.view(q.shape),
@@ -115,6 +114,7 @@ def paged_attention_fwd(
             v,
             attn_output,
             kv_seqlens,
+            context.max_kv_seq_length,
             block_offsets,
             block_size,
         )