[4/x] add tests for DTensor TP/SP + Float8Linear

Summary:

Makes the DTensor TP/SP tests also test `Float8Linear` with all scaling
types configured to be dynamic.

We can add support for delayed scaling with float8 all-gather for `x`
and `dL_dY` in a future PR, as needed.

Test Plan:

```
./test/test_dtensor.sh
```

Reviewers:

Subscribers:

Tasks:

Tags:

ghstack-source-id: 8e121ecaf4e05c5eb69b1612c084c459abe95589
Pull Request resolved: #294

float8_experimental/float8_tensor_parallel.py

@@ -4,6 +4,7 @@
     cast_to_float8_e4m3_dynamic,
     cast_to_float8_e5m2_dynamic_bw,
 )
+from float8_experimental.float8_linear import TensorScalingType
 from torch.distributed._tensor import DTensor
 from torch.distributed.device_mesh import DeviceMesh
 from torch.distributed.tensor.parallel import (
@@ -22,6 +23,15 @@
 # NOTE: This only works and tested with the DynamicLinear
 
 
+def _float8_linear_supports_float8_allgather(m):
+    # TODO(future PR): add support for delayed scaling for activations
+    # and gradients
+    return (
+        m.scaling_type_x == TensorScalingType.DYNAMIC
+        and m.scaling_type_dL_dY == TensorScalingType.DYNAMIC
+    )
+
+
 class Float8ColwiseParallel(ColwiseParallel):
     @staticmethod
     def _prepare_input_fn(
@@ -61,11 +71,16 @@ def _prepare_output_fn(output_layouts, use_local_output, mod, outputs, device_me
 
     def _apply(self, module: nn.Module, device_mesh: DeviceMesh) -> nn.Module:
         from float8_experimental.float8_dynamic_linear import Float8DynamicLinear
+        from float8_experimental.float8_linear import Float8Linear
 
-        if not isinstance(module, Float8DynamicLinear):
+        if not isinstance(module, (Float8DynamicLinear, Float8Linear)):
             raise ValueError(
-                f"Expecting module to be Float8DynamicLinear but found {type(module)}"
+                f"Expecting module to be Float8DynamicLinear or Float8Linear but found {type(module)}"
             )
+        elif isinstance(
+            module, Float8Linear
+        ) and not _float8_linear_supports_float8_allgather(module):
+            raise AssertionError("unsupported")
 
         return super()._apply(module, device_mesh)
 
@@ -107,11 +122,16 @@ def _prepare_output_fn(output_layouts, use_local_output, mod, outputs, device_me
 
     def _apply(self, module: nn.Module, device_mesh: DeviceMesh) -> nn.Module:
         from float8_experimental.float8_dynamic_linear import Float8DynamicLinear
+        from float8_experimental.float8_linear import Float8Linear
 
-        if not isinstance(module, Float8DynamicLinear):
+        if not isinstance(module, (Float8DynamicLinear, Float8Linear)):
             raise ValueError(
-                f"Expecting module to be Float8DynamicLinear but found {type(module)}"
+                f"Expecting module to be Float8DynamicLinear or Float8Linear but found {type(module)}"
             )
+        elif isinstance(
+            module, Float8Linear
+        ) and not _float8_linear_supports_float8_allgather(module):
+            raise AssertionError("unsupported")
 
         return super()._apply(module, device_mesh)
 
@@ -184,22 +204,23 @@ def _prepare_input_arg(self, input, mesh, input_layout, desired_layout):
 
     def _apply(self, module: nn.Module, device_mesh: DeviceMesh) -> nn.Module:
         from float8_experimental.float8_dynamic_linear import Float8DynamicLinear
+        from float8_experimental.float8_linear import Float8Linear
 
         fwd_linear_config = None
         if self.fwd_config_submodule_fqn is not None:
             fwd_linear = module.get_submodule(self.fwd_config_submodule_fqn)
-            assert isinstance(fwd_linear, Float8DynamicLinear)
+            assert isinstance(fwd_linear, (Float8DynamicLinear, Float8Linear))
             fwd_linear_config = fwd_linear.forward_config
         else:
             # search for ScaledMM configs for all the submodules and make sure they are the same
             for mod in module.modules():
-                if isinstance(mod, Float8DynamicLinear):
+                if isinstance(mod, (Float8DynamicLinear, Float8Linear)):
                     if fwd_linear_config is None:
                         fwd_linear_config = mod.forward_config
                     else:
                         assert (
                             fwd_linear_config == mod.forward_config
-                        ), "All the Float8DynamicLinear modules should have same forward config!"
+                        ), "All the Float8DynamicLinear and Float8Linear modules should have same forward config!"
 
         self.fwd_linear_config = fwd_linear_config
         super()._apply(module, device_mesh)

test/test_dtensor.py

@@ -18,6 +18,7 @@
     Float8DynamicLinear,
     NoopFwToFloat8E5M2Bw,
 )
+from float8_experimental.float8_linear import Float8Linear, TensorScalingType
 from float8_experimental.float8_linear_utils import swap_linear_with_float8_linear
 from float8_experimental.float8_tensor import Float8Tensor, ScaledMMConfig
 from float8_experimental.float8_tensor_parallel import (
@@ -169,23 +170,37 @@ def test_dtensor_fp8_autograd(mesh: DeviceMesh, size=16):
     loss.backward()
 
 
-def test_fp8_mlp_tensor_parallelism_base(
-    mesh: DeviceMesh, size=16, compile: bool = False
+def _test_fp8_mlp_tensor_parallelism_base(
+    mesh: DeviceMesh, size=16, compile: bool = False, use_float8_linear: bool = False
 ):
     device = mesh.device_type
+    # TODO(future): delete Float8DynamicLinear from this test once all the
+    # code is unified
+    float8_cls = Float8Linear if use_float8_linear else Float8DynamicLinear
+    extra_kwargs = {}
+    if use_float8_linear:
+        # For now, just use Float8Linear with dynamic scaling, which is the
+        # same behavior as Float8Linear.
+        # TODO(future): add support for float8 all-gather with delayed scaling
+        # for activations and gradients.
+        extra_kwargs = {
+            "scaling_type_x": TensorScalingType.DYNAMIC,
+            "scaling_type_w": TensorScalingType.DYNAMIC,
+            "scaling_type_dL_dY": TensorScalingType.DYNAMIC,
+        }
 
     toy_model = ToyModel().to(device)
     toy_model_fp8 = swap_linear_with_float8_linear(
-        toy_model, Float8DynamicLinear, emulate=True
+        toy_model, float8_cls, emulate=True, **extra_kwargs
     )
 
     tp_model = copy.deepcopy(toy_model)
     tp_model = swap_linear_with_float8_linear(
-        tp_model, Float8DynamicLinear, emulate=True
+        tp_model, float8_cls, emulate=True, **extra_kwargs
     )
     sp_model = copy.deepcopy(toy_model)
     sp_model = swap_linear_with_float8_linear(
-        sp_model, Float8DynamicLinear, emulate=True
+        sp_model, float8_cls, emulate=True, **extra_kwargs
     )
 
     # vanilla TP
@@ -218,7 +233,7 @@ def test_fp8_mlp_tensor_parallelism_base(
     # PrepareFloat8ModuleInput with specific submodule fqn
     sp_model2 = copy.deepcopy(toy_model)
     sp_model2 = swap_linear_with_float8_linear(
-        sp_model2, Float8DynamicLinear, emulate=True
+        sp_model2, Float8DynamicLinear, emulate=True, **extra_kwargs
     )
 
     sp_model2 = parallelize_module(
@@ -271,8 +286,28 @@ def test_fp8_mlp_tensor_parallelism_base(
     )
 
 
+def test_fp8_mlp_tensor_parallelism_eager(mesh: DeviceMesh, size=16):
+    _test_fp8_mlp_tensor_parallelism_base(
+        mesh, size, compile=False, use_float8_linear=False
+    )
+
+
+def test_fp8_mlp_tensor_parallelism_eager_float8_linear(mesh: DeviceMesh, size=16):
+    _test_fp8_mlp_tensor_parallelism_base(
+        mesh, size, compile=False, use_float8_linear=True
+    )
+
+
 def test_fp8_mlp_tensor_parallelism_compile(mesh: DeviceMesh, size=16):
-    test_fp8_mlp_tensor_parallelism_base(mesh, size, compile=True)
+    _test_fp8_mlp_tensor_parallelism_base(
+        mesh, size, compile=True, use_float8_linear=False
+    )
+
+
+def test_fp8_mlp_tensor_parallelism_compile_float8_linear(mesh: DeviceMesh, size=16):
+    _test_fp8_mlp_tensor_parallelism_base(
+        mesh, size, compile=True, use_float8_linear=True
+    )
 
 
 if __name__ == "__main__":
@@ -285,8 +320,10 @@ def test_fp8_mlp_tensor_parallelism_compile(mesh: DeviceMesh, size=16):
         test_fp8_redistribute,
         test_dtensor_cast_to_fp8,
         test_dtensor_fp8_autograd,
-        test_fp8_mlp_tensor_parallelism_base,
+        test_fp8_mlp_tensor_parallelism_eager,
+        test_fp8_mlp_tensor_parallelism_eager_float8_linear,
         test_fp8_mlp_tensor_parallelism_compile,
+        test_fp8_mlp_tensor_parallelism_compile_float8_linear,
     ]
 
     for test in tqdm(tests, desc="Running tests"):