[MoE training] torch.compile support for ScaledGroupedMMTensor

test/prototype/moe_training/test_training.py

@@ -35,7 +35,10 @@
         ["does.not.exist"],
     ],
 )
-def test_moe_float8_training(target_fqns: list[str]):
+@pytest.mark.parametrize(
+    "compile", [False, True]
+)
+def test_moe_float8_training(target_fqns: list[str], compile: bool):
     model_args = TransformerModelArgs(
         moe_enabled=True,
         num_experts=8,
@@ -73,6 +76,10 @@ def moe_module_filter_fn(mod: nn.Module, cur_fqn: str) -> bool:
         target_fqns=target_fqns,
     )
 
+    if compile:
+        model = torch.compile(model, fullgraph=False)
+        ref_model = torch.compile(ref_model, fullgraph=False)
+
     # inputs
     batch, seq, dim = 8, 2048, 256
     ref_x = torch.randn(

torchao/prototype/moe_training/kernels/jagged_float8_scales.py

@@ -42,7 +42,11 @@
     for block_size_cols in block_sizes
 ]
 
+from torch.library import triton_op, wrap_triton
 
+
+
+@triton_op("torchao::triton_fp8_row_major_jagged_rowwise_scales", mutates_args={})
 def triton_fp8_row_major_jagged_rowwise_scales(
     hp_tensor: torch.Tensor,
     offsets: torch.Tensor,
@@ -90,7 +94,7 @@ def triton_fp8_row_major_jagged_rowwise_scales(
         triton.cdiv(m, meta["BLOCK_SIZE_ROWS"]),
         offsets.numel(),
     )
-    _triton_fp8_row_major_jagged_rowwise_scales[grid](
+    wrap_triton(_triton_fp8_row_major_jagged_rowwise_scales)[grid](
         hp_tensor,
         offsets,
         output_buffer,
@@ -203,7 +207,7 @@ def _triton_fp8_row_major_jagged_rowwise_scales(
         )
         tl.store(out_ptr + out_offs, fp8_data, mask=block_mask)
 
-
+@triton_op("torchao::triton_fp8_col_major_jagged_colwise_scales", mutates_args={})
 def triton_fp8_col_major_jagged_colwise_scales(
     hp_tensor: torch.Tensor,
     offsets: torch.Tensor,
@@ -251,7 +255,7 @@ def triton_fp8_col_major_jagged_colwise_scales(
         triton.cdiv(n, meta["BLOCK_SIZE_COLS"]),
         offsets.numel(),
     )
-    _triton_fp8_col_major_jagged_colwise_scales[grid](
+    wrap_triton(_triton_fp8_col_major_jagged_colwise_scales)[grid](
         hp_tensor,
         offsets,
         output_buffer,

torchao/prototype/moe_training/scaled_grouped_mm.py

@@ -40,8 +40,6 @@ def _scaled_grouped_mm(
         offs (int32 torch.Tensor): The offsets to use to mark the starting index of each group along dim0 of the A tensor.
         out_dtype (Optional[torch.dtype]): The dtype of the output tensor. Currently only torch.bfloat16 is supported.
     """
-    # TODO: Remove once prototype is more mature. This is currently very useful for development and debugging.
-    logger.info("Using scaled_grouped_mm")
     return _Float8GroupedMM.apply(
         A,
         B_t,

torchao/prototype/moe_training/tensor.py

@@ -123,7 +123,9 @@ def __repr__(self):
         return f"ScaledGroupedMMTensor(data={self._data})"
 
     def __tensor_flatten__(self):
-        return ["_data"]
+        # Metadata is empty but needed to make the subclass traceable for torch.compile.
+        metadata = {}
+        return ["_data"], metadata
 
     @staticmethod
     def __tensor_unflatten__(inner_tensors, flatten_spec, outer_size, outer_stride):