per-token scaling for fp8 linear (#160)

* per-token scaling for fp8 linear

* Update diffsynth_engine/utils/fp8_linear.py

Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

---------

Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

Author: akaitsuki-ii

diffsynth_engine/utils/fp8_linear.py

@@ -72,39 +72,33 @@ def fp8_linear(
     ) -> torch.Tensor:
         device = input.device
         origin_dtype = input.dtype
-        scale_a = 1.0
+        origin_shape = input.shape
+        input = input.reshape(-1, origin_shape[-1])
+
+        x_max = torch.max(torch.abs(input), dim=-1, keepdim=True).values
+        fp8_max = 448.0
         # For float8_e4m3fnuz, the maximum representable value is half of that of e4m3fn.
         # To avoid overflow and ensure numerical compatibility during FP8 computation,
         # we scale down the input by 2.0 in advance.
         # This scaling will be compensated later during the final result scaling.
         if DTYPE_FP8 == torch.float8_e4m3fnuz:
-            scale_a = 2.0
-            input = input / scale_a
+            fp8_max = fp8_max / 2.0
+        scale_a = torch.clamp(x_max / fp8_max, min=1.0).float().to(device=device)
+        scale_b = torch.ones((weight.shape[0], 1)).float().to(device=device)
+        input = input / scale_a
         input = input.to(DTYPE_FP8)
         weight = weight.to(DTYPE_FP8)
 
-        if len(input.shape) > 2:
-            origin_shape = input.shape
-            input = input.reshape(-1, origin_shape[-1])
-            result = torch._scaled_mm(
-                input,
-                weight.T,
-                scale_a=torch.tensor(scale_a).to(device=device),
-                scale_b=torch.tensor(1.0).to(device=device),
-                bias=bias,
-                out_dtype=origin_dtype,
-            )
-            new_shape = origin_shape[:-1] + result.shape[-1:]
-            result = result.reshape(new_shape)
-        else:
-            result = torch._scaled_mm(
-                input,
-                weight.T,
-                scale_a=torch.tensor(scale_a).to(device=device),
-                scale_b=torch.tensor(1.0).to(device=device),
-                bias=bias,
-                out_dtype=origin_dtype,
-            )
+        result = torch._scaled_mm(
+            input,
+            weight.T,
+            scale_a=scale_a,
+            scale_b=scale_b.T,
+            bias=bias,
+            out_dtype=origin_dtype,
+        )
+        new_shape = origin_shape[:-1] + result.shape[-1:]
+        result = result.reshape(new_shape)
         return result
 
     F.linear = fp8_linear