FA num splits option

tests/pytorch/attention/test_attention.py

@@ -117,7 +117,15 @@ def reset_global_fp8_state():
 @pytest.mark.parametrize("swa", [False])
 @pytest.mark.parametrize("pad_between_seqs", [False])
 def test_dot_product_attention(
-    dtype, model_configs, model, ckpt_attn, workspace_opt, qkv_layout, swa, pad_between_seqs
+    dtype,
+    model_configs,
+    model,
+    ckpt_attn,
+    workspace_opt,
+    qkv_layout,
+    swa,
+    pad_between_seqs,
+    num_splits=None,
 ):
     """Test DotProductAttention module"""
 
@@ -244,6 +252,7 @@ def test_dot_product_attention(
             workspace_opt,
             pad_between_seqs,
             is_training,
+            num_splits=num_splits,
         )
 
     # Compare results
@@ -301,11 +310,18 @@ def test_dpa_checkpoint(dtype, model_configs, model):
 @pytest.mark.parametrize("model_configs", [model_configs_max_logit])
 @pytest.mark.parametrize("model", model_configs_max_logit.keys())
 @pytest.mark.parametrize("qkv_layout", ["sbhd_sbhd_sbhd", "thd_thd_thd"])
-def test_dpa_max_logit(dtype, model_configs, model, qkv_layout):
+@pytest.mark.parametrize("num_splits", [None, 2])
+def test_dpa_max_logit(dtype, model_configs, model, qkv_layout, num_splits):
     """Test DotProductAttention module with checkpointing"""
     config = model_configs[model]
     config.return_max_logit = True
-    test_dot_product_attention(dtype, model_configs, model, False, True, qkv_layout, False, False)
+    # Minimal guard: if num_splits is requested, require FA3 be installed
+    if num_splits is not None:
+        if not FlashAttentionUtils.v3_is_installed:
+            pytest.skip("num_splits requires FlashAttention-3.")
+    test_dot_product_attention(
+        dtype, model_configs, model, False, True, qkv_layout, False, False, num_splits=num_splits
+    )
 
 
 model_configs_softmax = {
@@ -848,6 +864,7 @@ def _run_dot_product_attention(
     workspace_opt: bool,
     pad_between_seqs: bool,
     is_training: bool,
+    num_splits=None,
 ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]:
     """Run DotProductAttention module with one forward pass and one backward pass"""
     # Set RNG and environment varables
@@ -1152,6 +1169,8 @@ def get_dummy_cuda_rng_tracker() -> CudaRNGStatesTracker:
         core_attention_bias=bias,
         alibi_slopes=alibi_slopes,
         fast_zero_fill=True,
+        # Only pass num_splits when exercising the FlashAttention path
+        num_splits=(num_splits if backend == "FlashAttention" else None),
     )
     max_logit = None
     if config.return_max_logit:

transformer_engine/pytorch/attention/dot_product_attention/backends.py

@@ -669,6 +669,7 @@ def forward(
         inference_params: Optional[InferenceParams] = None,
         flash_attention_backend: Optional[PkgVersion] = PkgVersion("0"),
         fp8_output: bool = False,
+        num_splits: Optional[int] = None,
     ) -> torch.Tensor:
         """flash-attn fprop"""
 
@@ -843,6 +844,16 @@ def forward(
         use_flash_attn_3 = False
         if flash_attention_backend is not None and flash_attention_backend > PkgVersion("3.0.0b"):
             use_flash_attn_3 = True
+        # Enforce FA3 when num_splits is provided
+        if num_splits is not None and not use_flash_attn_3:
+            if not fa_utils.v3_is_installed:
+                raise ValueError(
+                    "num_splits is only supported with FlashAttention-3, which is not installed. "
+                )
+            raise ValueError(
+                "num_splits is only supported with FlashAttention-3. "
+                "Please adjust configuration to enable FA3 for these inputs."
+            )
         if context_parallel and all(
             not isinstance(x, Float8Tensor) for x in [query_layer, key_layer, value_layer]
         ):
@@ -925,6 +936,9 @@ def forward(
                         fa_optional_forward_kwargs["alibi_slopes"] = alibi_slopes
                     if fa_utils.v2_4_1_plus:
                         fa_optional_forward_kwargs["deterministic"] = self.deterministic
+                    if num_splits is not None:
+                        # Forward optional split control to flash-attn if available
+                        fa_optional_forward_kwargs["num_splits"] = num_splits
                     if inference_params is not None:
                         # use block_table kwarg to support thd_2bshd for non-paged
                         fa_optional_forward_kwargs["block_table"] = (
@@ -959,6 +973,9 @@ def forward(
                             fa_3_optional_forward_kwargs["page_table"] = (
                                 inference_params.cache_manager.page_table[:batch_size]
                             )
+                    if num_splits is not None:
+                        # Forward optional split control to flash-attn v3 if supported
+                        fa_3_optional_forward_kwargs["num_splits"] = num_splits
                     if fp8:
                         QKV_quantizer = quantizers["scaling_fwd"][META_QKV]
                         torch_dtype = get_fp8_torch_dtype(fp8_meta["recipe"], fprop_tensor=True)

transformer_engine/pytorch/attention/dot_product_attention/dot_product_attention.py

@@ -799,6 +799,7 @@ def forward(
         inference_params: Optional[InferenceParams] = None,
         pad_between_seqs: Optional[bool] = None,
         fp8_output: Optional[bool] = False,
+        num_splits: Optional[int] = None,
     ) -> torch.Tensor:
         """
         Dot Product Attention Layer.
@@ -973,6 +974,11 @@ def forward(
             If true, there are padding tokens between individual sequences in a packed batch.
         fp8_output: Optional[bool], default = `False`
             Whether to enforce output to be in FP8 or not.
+        num_splits: Optional[int], default = `None`
+            Optional split control for FlashAttention-3 only. When set, this value is forwarded
+            to the FA3 backend to control internal kernel splitting behavior. It is ignored for
+            other backends and will raise a ValueError if a non-FA3 backend is selected or if
+            FlashAttention-3 is not installed.
         """
 
         with torch.cuda.device(query_layer.device), self.prepare_forward(
@@ -1366,6 +1372,34 @@ def forward(
                     fused_attention_backend = _attention_backends["fused_attention_backend"]
                     use_unfused_attention = _attention_backends["use_unfused_attention"]
 
+            # If num_splits is requested, ensure we are using FlashAttention-3.
+            if num_splits is not None:
+                is_fa3_selected = (
+                    use_flash_attention
+                    and flash_attention_backend is not None
+                    and flash_attention_backend == dpa_utils.FlashAttentionUtils.fa3_version
+                )
+                if not is_fa3_selected:
+                    backend_name = (
+                        f"FlashAttention ({str(flash_attention_backend)})"
+                        if use_flash_attention
+                        else (
+                            "FusedAttention"
+                            if use_fused_attention
+                            else "UnfusedDotProductAttention"
+                        )
+                    )
+                    if not dpa_utils.FlashAttentionUtils.v3_is_installed:
+                        raise ValueError(
+                            "num_splits is only supported with FlashAttention-3, which is not"
+                            " installed. "
+                        )
+                    raise ValueError(
+                        "num_splits is only supported with FlashAttention-3. Selected backend is"
+                        f" {backend_name}. Please adjust configuration to enable FA3 for these"
+                        " inputs."
+                    )
+
             # raise exception if no backend is available
             if sum([use_flash_attention, use_fused_attention, use_unfused_attention]) == 0:
                 raise ValueError(
@@ -1413,6 +1447,7 @@ def forward(
                     inference_params=inference_params,
                     flash_attention_backend=flash_attention_backend,
                     fp8_output=fp8_output,
+                    num_splits=num_splits,
                 )
 
             if use_fused_attention: