Minor refactoring of ds kernel integration

openfold/model/primitives.py

@@ -14,15 +14,16 @@
 # limitations under the License.
 import importlib
 import math
-from typing import Optional, Callable, List, Tuple, Sequence
+from typing import Optional, Callable, List, Tuple
 import numpy as np
 
 deepspeed_is_installed = importlib.util.find_spec("deepspeed") is not None
+ds4s_is_installed = deepspeed_is_installed and importlib.util.find_spec("deepspeed.ops.deepspeed4science") is not None
 if deepspeed_is_installed:
     import deepspeed
 
-    if importlib.util.find_spec("deepspeed.ops.deepspeed4science") is not None:
-        from deepspeed.ops.deepspeed4science import DS4Sci_EvoformerAttention
+if ds4s_is_installed:
+    from deepspeed.ops.deepspeed4science import DS4Sci_EvoformerAttention
 
 fa_is_installed = importlib.util.find_spec("flash_attn") is not None
 if fa_is_installed:
@@ -504,39 +505,7 @@ class Attention(nn.Module):
                     "If use_deepspeed_evo_attention is True, you may only "
                     "provide up to two bias terms"
                 )
-
-            orig_shape = q.shape
-            no_batch_dims = len(orig_shape[:-3])
-            if no_batch_dims > 2:
-                raise ValueError(
-                    f"Q is of shape {list(orig_shape)} but must be "
-                    "of shape [B, N, Q/K, H, C_hidden] if "
-                    "use_deepspeed_evo_attention is True."
-                )
-
-            # Bypass asserts for bias shapes in DS4Sci_EvoformerAttention()
-            # by adding batch and N_seq dims if needed.
-            if no_batch_dims < 2:
-                addl_dims = (1,) * (2 - no_batch_dims)
-                q = q.view(*(addl_dims + q.shape))
-                k = k.view(*(addl_dims + k.shape))
-                v = v.view(*(addl_dims + v.shape))
-                biases = [b.view(*(addl_dims + b.shape)) for b in biases]
-
-            # DeepSpeed attn. kernel requires inputs to be type bf16 or fp16
-            # Cast to bf16 so kernel can be used during inference
-            orig_dtype = q.dtype
-            if orig_dtype not in [torch.bfloat16, torch.float16]:
-                o = DS4Sci_EvoformerAttention(q.to(dtype=torch.bfloat16),
-                                              k.to(dtype=torch.bfloat16),
-                                              v.to(dtype=torch.bfloat16),
-                                              [b.to(dtype=torch.bfloat16) for b in biases])
-
-                o = o.to(dtype=orig_dtype)
-            else:
-                o = DS4Sci_EvoformerAttention(q, k, v, biases)
-
-            o = o.view(orig_shape)
+            o = _deepspeed_evo_attn(q, k, v, biases)
         elif use_lma:
             biases = [
                 b.expand(b.shape[:-2] + (q_x.shape[-2],) + (kv_x.shape[-2],)) 
@@ -644,6 +613,67 @@ class GlobalAttention(nn.Module):
         return m
 
 
+@torch.jit.ignore
+def _deepspeed_evo_attn(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    biases: List[torch.Tensor],
+):
+    """""
+    Compute attention using the DeepSpeed DS4Sci_EvoformerAttention kernel.
+
+    Args:
+        q:
+            [*, Q, H, C_hidden] query data
+        k:
+            [*, K, H, C_hidden] key data
+        v:
+            [*, V, H, C_hidden] value data
+        biases:
+            List of biases that broadcast to [*, H, Q, K]
+    """
+
+    if not ds4s_is_installed:
+        raise ValueError(
+            "_deepspeed_evo_attn requires that DeepSpeed be installed "
+            "and that the deepspeed.ops.deepspeed4science package exists"
+        )
+
+    def reshape_dims(x):
+        no_batch_dims = len(x.shape[:-3])
+        if no_batch_dims < 2:
+            return x.reshape(*((1,) * (2 - no_batch_dims) + x.shape))
+        if no_batch_dims > 2:
+            return x.reshape(*((x.shape[0], -1) + x.shape[-3:]))
+        return x
+
+    # Reshape tensors to match expected input shape [B, N, Q/K, H, C_hidden]
+    # for DS4Sci_EvoformerAttention() by adding or flattening batch dims as needed.
+    orig_shape = q.shape
+    if len(orig_shape[:-3]) != 2:
+        q = reshape_dims(q)
+        k = reshape_dims(k)
+        v = reshape_dims(v)
+        biases = [reshape_dims(b) for b in biases]
+
+    # DeepSpeed attn. kernel requires inputs to be type bf16 or fp16
+    # Cast to bf16 so kernel can be used during inference
+    orig_dtype = q.dtype
+    if orig_dtype not in [torch.bfloat16, torch.float16]:
+        o = DS4Sci_EvoformerAttention(q.to(dtype=torch.bfloat16),
+                                      k.to(dtype=torch.bfloat16),
+                                      v.to(dtype=torch.bfloat16),
+                                      [b.to(dtype=torch.bfloat16) for b in biases])
+
+        o = o.to(dtype=orig_dtype)
+    else:
+        o = DS4Sci_EvoformerAttention(q, k, v, biases)
+
+    o = o.reshape(orig_shape)
+    return o
+
+
 def _lma(
     q: torch.Tensor, 
     k: torch.Tensor,