fix dtype bug

Author: danielvegamyhre

torchao/prototype/moe_training/conversion_utils.py

@@ -84,7 +84,7 @@ def _swap_params(
                 f"Does not support a root nn.Parameter with children: {module}"
             )
         if not isinstance(module.data, ScaledGroupedMMTensor):
-            new_data = ScaledGroupedMMTensor(module.data)
+            new_data = ScaledGroupedMMTensor(module.data, module.data.dtype)
             return nn.Parameter(new_data, requires_grad=module.requires_grad)
         return module
 
@@ -110,7 +110,8 @@ def post_order_traversal(
             for param_name, param in module.named_parameters(recurse=False):
                 if not isinstance(param.data, ScaledGroupedMMTensor):
                     new_param = nn.Parameter(
-                        ScaledGroupedMMTensor(param), requires_grad=param.requires_grad
+                        ScaledGroupedMMTensor(param.data, param.data.dtype),
+                        requires_grad=param.requires_grad,
                     )
                     setattr(module, param_name, new_param)
                     logger.info(

torchao/prototype/moe_training/scaled_grouped_mm.py

@@ -40,7 +40,7 @@ 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.
     """
-    logger.debug("Using differentiable _scaled_grouped_mm")
+    logger.info("Using differentiable _scaled_grouped_mm")
     return _Float8GroupedMM.apply(
         A,
         B_t,

torchao/prototype/moe_training/tensor.py

@@ -4,6 +4,7 @@
 # This source code is licensed under the BSD 3-Clause license found in the
 # LICENSE file in the root directory of this source tree.
 
+import logging
 from typing import Any, Optional, Tuple
 
 import torch
@@ -12,6 +13,9 @@
 
 from torchao.prototype.moe_training import _scaled_grouped_mm
 
+logger: logging.Logger = logging.getLogger(__name__)
+
+
 _ops_to_preserve_subclass = {
     torch.ops.aten.empty_like.default,
     torch.ops.aten.new_zeros.default,
@@ -40,14 +44,18 @@ class ScaledGroupedMMTensor(torch.Tensor):
     def __new__(
         cls,
         tensor: torch.Tensor,
+        dtype: torch.dtype,
     ):
+        logger.info(
+            f"__new__ tensor={tensor}, tensor.dtype={tensor.dtype}, dtype={dtype}"
+        )
         return torch.Tensor._make_wrapper_subclass(
             cls,
             tensor.size(),
             strides=tensor.stride(),
             storage_offset=tensor.storage_offset(),
             memory_format=suggest_memory_format(tensor),
-            dtype=tensor.dtype,
+            dtype=dtype,
             layout=tensor.layout,
             device=tensor.device,
             pin_memory=tensor.is_pinned(),
@@ -57,8 +65,10 @@ def __new__(
     def __init__(
         self,
         tensor: torch.Tensor,
+        dtype: torch.dtype,
     ):
         self._data = tensor
+        self._dtype = dtype
 
     @classmethod
     def __torch_function__(cls, func, types, args, kwargs={}):
@@ -87,12 +97,22 @@ def __torch_function__(cls, func, types, args, kwargs={}):
 
     @classmethod
     def __torch_dispatch__(cls, func, types, args, kwargs={}):
+        logger.debug(f"{func.__name__}, args={args}, kwargs={kwargs}")
         # detach is special case
         if func == torch.ops.aten.detach.default:
-            return ScaledGroupedMMTensor(args[0]._data)
+            return ScaledGroupedMMTensor(args[0]._data, args[0]._dtype)
 
         # unwrap args and kwargs
-        unwrap = lambda tensor: tensor._data
+        dtype: Optional[torch.dtype] = None
+
+        def unwrap(t):
+            nonlocal dtype
+            if dtype is None:
+                dtype = t._dtype
+            else:
+                assert t._dtype == dtype
+            return t._data
+
         args, kwargs = pytree.tree_map_only(
             ScaledGroupedMMTensor, unwrap, (args, kwargs or {})
         )
@@ -107,13 +127,27 @@ def __torch_dispatch__(cls, func, types, args, kwargs={}):
         # wrap outputs back into ScaledGroupedMMTensor for ops that do preserve subclass
         return pytree.tree_map_only(
             torch.Tensor,
-            lambda x: ScaledGroupedMMTensor(x),
+            lambda x: ScaledGroupedMMTensor(x, dtype),
             out,
         )
 
+    def __repr__(self):
+        return f"ScaledGroupedMMTensor(data={self._data}, dtype={self._dtype})"
+
+    def __tensor_flatten__(self):
+        return ["_data"], {"dtype": self._dtype}
+
+    @staticmethod
+    def __tensor_unflatten__(inner_tensors, flatten_spec, outer_size, outer_stride):
+        return ScaledGroupedMMTensor(
+            inner_tensors["_data"],
+            flatten_spec["dtype"],
+        )
+
     def fsdp_pre_all_gather(self, mesh):
-        metadata = ()
-        return (self._data,), metadata
+        all_gather_inputs = (self._data,)
+        all_gather_metadata = ()
+        return all_gather_inputs, all_gather_metadata
 
     def fsdp_post_all_gather(
         self,
@@ -124,6 +158,6 @@ def fsdp_post_all_gather(
         out: Optional[torch.Tensor] = None,
     ):
         (data,) = all_gather_outputs
-        return ScaledGroupedMMTensor(
-            data,
-        ), (data,)
+        out = ScaledGroupedMMTensor(data, param_dtype)
+        inner_tensors = (data,)
+        return out, inner_tensors