Conditionally add the sdpa decomposition only for Torch < 2.2.0. (#396)

This was causing an exception at startup and blocking any other work on
Torch 2.2.0 compat.

core/shark_turbine/dynamo/utils.py

@@ -10,54 +10,61 @@
 from typing import Dict, List, Tuple, Optional
 
 
-@register_decomposition(torch.ops.aten._scaled_dot_product_flash_attention.default)
-def scaled_dot_product_flash_attention(
-    query,
-    key,
-    value,
-    dropout_p: float = 0.0,
-    is_causal: bool = False,
-    return_debug_mask: bool = False,
-    *,
-    scale: float = None,
-) -> Tuple[Tensor, Tensor, Tensor, Tensor, int, int, Tensor, Tensor, Tensor]:
-    dtype = query.dtype
-    batchSize, num_head, qSize, headSize = (
-        query.shape[0],
-        query.shape[1],
-        query.shape[2],
-        query.shape[3],
-    )
+if torch.__version__ < "2.2.0":
+    # Torch versions prior to 2.2.0 lacked some decompositions, which we
+    # add manually.
+    @register_decomposition(torch.ops.aten._scaled_dot_product_flash_attention.default)
+    def scaled_dot_product_flash_attention(
+        query,
+        key,
+        value,
+        dropout_p: float = 0.0,
+        is_causal: bool = False,
+        return_debug_mask: bool = False,
+        *,
+        scale: float = None,
+    ) -> Tuple[Tensor, Tensor, Tensor, Tensor, int, int, Tensor, Tensor, Tensor]:
+        dtype = query.dtype
+        batchSize, num_head, qSize, headSize = (
+            query.shape[0],
+            query.shape[1],
+            query.shape[2],
+            query.shape[3],
+        )
 
-    logsumexp = torch.empty([batchSize, qSize, num_head, headSize], dtype=torch.float)
-    cum_seq_q, cum_seq_k = torch.empty([], dtype=torch.long), torch.empty(
-        [], dtype=torch.long
-    )
-    max_q, max_k = 0, 0
-    philox_seed, philox_offset = torch.empty([], dtype=torch.long), torch.empty(
-        [], dtype=torch.long
-    )
-    debug_attn_mask = torch.empty(
-        [],
-        dtype=query.dtype,
-        device="cpu",
-        requires_grad=query.requires_grad,
-    )
-    output, _ = torch.ops.aten._scaled_dot_product_attention_math.default(
-        query, key, value, None, dropout_p, is_causal, None, scale=scale
-    )
-    output = output.transpose(1, 2).contiguous(memory_format=torch.contiguous_format)
-    return (
-        output.transpose(1, 2),
-        logsumexp,
-        cum_seq_q,
-        cum_seq_k,
-        max_q,
-        max_k,
-        philox_seed,
-        philox_offset,
-        debug_attn_mask,
-    )
+        logsumexp = torch.empty(
+            [batchSize, qSize, num_head, headSize], dtype=torch.float
+        )
+        cum_seq_q, cum_seq_k = torch.empty([], dtype=torch.long), torch.empty(
+            [], dtype=torch.long
+        )
+        max_q, max_k = 0, 0
+        philox_seed, philox_offset = torch.empty([], dtype=torch.long), torch.empty(
+            [], dtype=torch.long
+        )
+        debug_attn_mask = torch.empty(
+            [],
+            dtype=query.dtype,
+            device="cpu",
+            requires_grad=query.requires_grad,
+        )
+        output, _ = torch.ops.aten._scaled_dot_product_attention_math.default(
+            query, key, value, None, dropout_p, is_causal, None, scale=scale
+        )
+        output = output.transpose(1, 2).contiguous(
+            memory_format=torch.contiguous_format
+        )
+        return (
+            output.transpose(1, 2),
+            logsumexp,
+            cum_seq_q,
+            cum_seq_k,
+            max_q,
+            max_k,
+            philox_seed,
+            philox_offset,
+            debug_attn_mask,
+        )
 
 
 # manually add decomposition to bypass the error that comes