Add support for quantize_() with Float8Linear module

test/float8/test_base.py

@@ -14,7 +14,7 @@
 import torch
 import torch.nn as nn
 
-from torchao.utils import TORCH_VERSION_AT_LEAST_2_5
+from torchao.utils import TORCH_VERSION_AT_LEAST_2_5, is_sm_89
 
 if not TORCH_VERSION_AT_LEAST_2_5:
     pytest.skip("Unsupported PyTorch version", allow_module_level=True)
@@ -531,6 +531,21 @@ def test_inference_mode(self):
         with torch.inference_mode(mode=True):
             m(x)
 
+    @unittest.skipIf(not is_sm_89(), "CUDA arch 8.9 not available")
+    def test_quantize(self):
+        x = torch.randn(32, 32, device="cuda")
+        m = nn.Sequential(nn.Linear(32, 32)).cuda()
+        m = convert_to_float8_training(m)
+        assert isinstance(m[0], Float8Linear), "Module is not a Float8Linear"
+        from torchao.quantization.quant_api import float8_weight_only, quantize_
+
+        quantize_(m, float8_weight_only())
+        assert (
+            m[0].weight.tensor_impl.float8_data.dtype == torch.float8_e4m3fn
+        ), "Post quantization dtype should be torch.float8_e4m3fn"
+        with torch.no_grad():
+            m(x)
+
 
 class TestScaledMM:
     @unittest.skipIf(
@@ -576,7 +591,7 @@ def test_scaled_mm_vs_emulated(self, base_dtype, use_fast_accum):
         if base_dtype in {torch.bfloat16, torch.float16}:
             atol, rtol = 7e-2, 7e-2
         else:
-            atol, rtol = 2e-3, 2e-3
+            atol, rtol = 3e-3, 3e-3
         torch.testing.assert_close(out_scaled_mm, out_emulated, atol=atol, rtol=rtol)
 
     @unittest.skipIf(not is_cuda_8_9, "CUDA not available")

torchao/quantization/quant_api.py

@@ -39,6 +39,7 @@
     to_affine_quantized_intx,
     to_marlinqqq_quantized_intx,
 )
+from torchao.float8.float8_linear import Float8Linear
 from torchao.float8.inference import Float8MMConfig
 from torchao.quantization.linear_activation_weight_observed_tensor import (
     LinearActivationWeightObservedTensor,
@@ -222,6 +223,12 @@ def _replace_with_custom_fn_if_matches_filter(
     Returns:
         None
     """
+    if isinstance(model, Float8Linear):
+        with torch.device("meta"):
+            new_module = nn.Linear(model.in_features, model.out_features)
+        new_module.weight = model.weight
+        new_module.bias = model.bias
+        model = new_module
     if filter_fn(model, cur_fqn[:-1]):
         if device is not None:
             model.to(device=device)  # move to device before quantization