make dynamic scaling default in Float8Linear (#300)

Summary:
Pull Request resolved: #300

1. makes dynamic scaling default in Float8Linear for an easier migration
   of callsites which currently use Float8DynamicLinear. Fixes
   tests as needed.
2. updates the README to reference Float8Linear for dynamic scaling

Reviewed By: drisspg

Differential Revision: D59305790

fbshipit-source-id: 30d3813946239e0e958e0f7ed446082b578b0607

README.md

@@ -27,21 +27,23 @@ pip install -e ".[dev]"
 
 # User API
 
-We provide two per-tensor scaling strategies: dynamic and delayed.  See https://arxiv.org/pdf/2209.05433.pdf, Section 4.3 for more details.
+We provide two per-tensor scaling strategies: dynamic and delayed.  See https://arxiv.org/pdf/2209.05433.pdf, Section 4.3 for more details. These strategies are configurable separately for activations (`x`), weights (`w`) and gradients (`dL_dY`).
 
-## float8 linear with dynamic scaling
+## float8 linear with dynamic scaling for `x`, `w` and `dL_dY`
+
+This is the most accurate recipe as every tensor is scaled dynamically.
 
 ```python
 from float8_experimental.float8_linear_utils import (
     swap_linear_with_float8_linear,
 )
-from float8_experimental.float8_dynamic_linear import Float8DynamicLinear
+from float8_experimental.float8_linear import Float8Linear
 
 # create model
 m = Model(...)
 
-# convert all `torch.nn.Linear` modules to `Float8DynamicLinear`
-swap_linear_with_float8_linear(m, Float8DynamicLinear)
+# convert all `torch.nn.Linear` modules to `Float8Linear`
+swap_linear_with_float8_linear(m, Float8Linear)
 
 # optional: use FSDP
 model = FSDP(model, use_orig_params=True)
@@ -54,18 +56,27 @@ m = torch.compile(m)
 
 ## float8 linear with delayed scaling
 
+This is theoretically the most performant recipe as it minimizes memory reads.
+
 ```python
 from float8_experimental.float8_linear_utils import (
     swap_linear_with_float8_linear,
     sync_float8_amax_and_scale_history,
 )
-from float8_experimental.float8_linear import Float8Linear
+from float8_experimental.float8_linear import Float8Linear, TensorScalingType
 
 # create model
 m = Model(...)
 
-# convert all `torch.nn.Linear` modules to `Float8Linear`
-swap_linear_with_float8_linear(m, Float8Linear)
+# convert all `torch.nn.Linear` modules to `Float8Linear`, specifying scaling
+# type
+swap_linear_with_float8_linear(
+    m, 
+    Float8Linear,
+    scaling_type_x=TensorScalingType.DELAYED,
+    scaling_type_w=TensorScalingType.DELAYED,
+    scaling_type_dL_dY=TensorScalingType.DELAYED,
+)
 
 # optional: use FSDP. Note that workarounds gated with config.enable_amax_init and
 # config.enable_pre_and_post_forward are needed for autocast + compile + FSDP + float8 to work
@@ -93,9 +104,7 @@ for _ in range(N_ITER):
 # 🧭 Code Organization
 
 * `float8_experimental/float8_linear.py`
-    - `Float8Linear` (main user facing entry point for delayed scaling)
-* `float8_experimental/float8_dynamic_linear.py`
-    - `Float8DynamicLinear` (main user facing entry point for dynamic scaling)
+    - `Float8Linear` (main user facing entry point for Float8Linear)
 * `float8_experimental/float8_tensor.py`
     - `Float8Tensor`, which allows `Float8Linear` to abide by the `x.dtype == x.grad.dtype` restriction
     - `ScaledMMConfig` defines the semantics for matmul in the forward and backwards pass

float8_experimental/float8_linear_utils.py

@@ -191,9 +191,9 @@ def swap_linear_with_float8_linear(
     skip_fqn_list: Optional[List[str]] = None,
     emulate: bool = False,
     linear_layer_filter: Optional[Callable[[nn.Linear], bool]] = None,
-    scaling_type_x: TensorScalingType = TensorScalingType.DELAYED,
-    scaling_type_w: TensorScalingType = TensorScalingType.DELAYED,
-    scaling_type_dL_dY: TensorScalingType = TensorScalingType.DELAYED,
+    scaling_type_x: TensorScalingType = TensorScalingType.DYNAMIC,
+    scaling_type_w: TensorScalingType = TensorScalingType.DYNAMIC,
+    scaling_type_dL_dY: TensorScalingType = TensorScalingType.DYNAMIC,
 ) -> Optional[nn.Module]:
     """
     Swaps `torch.nn.Linear` in `module` with `Float8Linear` or `Float8DynamicLinear`.

test/test_compile.py

@@ -299,7 +299,13 @@ def test_sync_amax_func():
     module = torch.nn.Sequential(
         nn.Linear(16, 32, bias=True), nn.ReLU(), nn.Linear(32, 16, bias=True)
     )
-    float8_mod = swap_linear_with_float8_linear(module, Float8Linear)
+    float8_mod = swap_linear_with_float8_linear(
+        module,
+        Float8Linear,
+        scaling_type_x=TensorScalingType.DELAYED,
+        scaling_type_w=TensorScalingType.DELAYED,
+        scaling_type_dL_dY=TensorScalingType.DELAYED,
+    )
     compiled_swap_func = torch.compile(sync_float8_amax_and_scale_history, backend=cnts)
     compiled_swap_func(float8_mod)
     assert cnts.frame_count == 1, "Compiled graph should have 1 frame!"
@@ -329,7 +335,13 @@ def test_sync_amax_func_cuda_graph_success():
         my_module = nn.Sequential(
             nn.Linear(16, 32, bias=True), nn.ReLU(), nn.Linear(32, 16, bias=True)
         ).to("cuda")
-        swap_linear_with_float8_linear(my_module, Float8Linear)
+        swap_linear_with_float8_linear(
+            my_module,
+            Float8Linear,
+            scaling_type_x=TensorScalingType.DELAYED,
+            scaling_type_w=TensorScalingType.DELAYED,
+            scaling_type_dL_dY=TensorScalingType.DELAYED,
+        )
         inpt = torch.randn(
             16, 16, device="cuda", dtype=torch.float32, requires_grad=True
         )

test/test_fsdp.py

@@ -23,6 +23,8 @@
 import torch.nn as nn
 from float8_experimental.float8_linear import Float8Linear, TensorScalingType
 from float8_experimental.float8_linear_utils import (
+    linear_requires_sync,
+    LinearType,
     swap_linear_with_float8_linear,
     sync_float8_amax_and_scale_history,
 )
@@ -130,7 +132,12 @@ def forward_backward(model, optim, is_fp8, i):
         optim.zero_grad()
         y_local = model(ref_input_local[i])
         y_local.backward(ref_grad_local[i])
-        if is_fp8:
+        if is_fp8 and linear_requires_sync(
+            LinearType.DELAYED,
+            TensorScalingType.DYNAMIC,
+            scaling_type_w,
+            TensorScalingType.DYNAMIC,
+        ):
             sync_float8_func(model)
         optim.step()
         return y_local

test/test_fsdp_compile.py

@@ -18,7 +18,7 @@
 import torch.multiprocessing as mp
 import torch.nn as nn
 from float8_experimental import config
-from float8_experimental.float8_linear import Float8Linear
+from float8_experimental.float8_linear import Float8Linear, TensorScalingType
 from float8_experimental.float8_linear_utils import (
     swap_linear_with_float8_linear,
     sync_float8_amax_and_scale_history,
@@ -49,7 +49,14 @@ def get_model(K, N, is_fp8, emulate, base_dtype=torch.float32):
         nn.Linear(K, N, dtype=base_dtype),
         nn.ReLU(),
     )
-    swap_linear_with_float8_linear(m, Float8Linear, emulate=emulate)
+    swap_linear_with_float8_linear(
+        m,
+        Float8Linear,
+        emulate=emulate,
+        scaling_type_x=TensorScalingType.DELAYED,
+        scaling_type_w=TensorScalingType.DELAYED,
+        scaling_type_dL_dY=TensorScalingType.DELAYED,
+    )
     return m