[wip] hooks

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float8_experimental/config.py

@@ -14,3 +14,8 @@
 # this doesn't work with autocast + torch.compile + FSDP. Enabling this
 # option is useful for safety, but not strictly necessary.
 enable_pre_and_post_forward = True
+
+# If True, dynamic linear uses hooks for activation casting
+# TODO(before land): add test coverage for both cases
+dynamic_use_activation_hooks = True
+# dynamic_use_activation_hooks = False

float8_experimental/float8_dynamic_linear.py

@@ -11,6 +11,7 @@
 
 from float8_experimental.float8_tensor import Float8Tensor
 from float8_experimental.float8_utils import tensor_to_scale, to_fp8_saturated
+import float8_experimental.config as config
 
 
 @torch._dynamo.allow_in_graph
@@ -39,25 +40,54 @@ def backward(ctx, gradY):
             None,
         )
 
+def cast_x_to_float8_e4m3fn_pre_hook(module, args):
+    """
+    Hook to cast the incoming activation to `torch.float8_e4m3fn`
+    """
+    return module.cast_to_float8(args[0])
+
+def cast_dldy_to_float8_e5m2_backward_pre_hook(module, grad_output):
+    """
+    Hook to cast the incoming gradient to `torch.float8_e5m2`
+    """
+    gradY = grad_output[0]
+    gradY_scale = tensor_to_scale(gradY, torch.float8_e5m2)
+    gradY_scaled = gradY * gradY_scale
+    bits_fp8 = to_fp8_saturated(gradY_scaled, torch.float8_e5m2)
+    tensor_fp8 = Float8Tensor(bits_fp8, gradY_scale, gradY.dtype, emulate=module.emulate)
+    return (tensor_fp8,)
 
 class Float8DynamicLinear(torch.nn.Linear):
     """
     A wrapper around a `torch.nn.Linear` module which does fp8 compute. By on the fly
     conversion to fp8 of the input and weight tensors.
     """
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.use_activation_hooks = config.dynamic_use_activation_hooks
 
     def forward(self, x):
-        x_fp8 = self.cast_to_float8(x)
+        # cast x to float8_e4m3fn
+        if self.use_activation_hooks:
+            x_fp8 = x
+        else:
+            x_fp8 = self.cast_to_float8(x)
+
+        # cast w to float8_e4m3fn
         w_fp8 = self.cast_to_float8(self.weight)
 
         y = torch.nn.functional.linear(x_fp8, w_fp8, self.bias)
 
         # Cast gradY to float8_e5m2 during backward
-        y = self.cast_to_float8e5m2_bw(y)
+        if self.use_activation_hooks:
+            pass
+        else:
+            y = self.cast_to_float8e5m2_bw(y)
 
         return y
 
     def cast_to_float8(self, inpt_tensor):
+        # TODO rename this function to clarify e4m3
         scale = tensor_to_scale(inpt_tensor, torch.float8_e4m3fn)
         return Float8Tensor.to_float8(
             inpt_tensor, scale, torch.float8_e4m3fn, emulate=self.emulate
@@ -80,4 +110,9 @@ def from_float(cls, mod, emulate: bool = False):
         new_mod.weight = mod.weight
         new_mod.bias = mod.bias
         new_mod.emulate = emulate
+        new_mod.use_activation_hooks = config.dynamic_use_activation_hooks
+        if new_mod.use_activation_hooks:
+            # install the hooks
+            new_mod.register_forward_pre_hook(cast_x_to_float8_e4m3fn_pre_hook)
+            new_mod.register_full_backward_pre_hook(cast_dldy_to_float8_e5m2_backward_pre_hook)
         return new_mod