Update float8 tests to run nightly

.github/workflows/float8_test.yml

@@ -1,4 +1,4 @@
-name: Run Float8 Tests
+name: Run Float8 Tests Nightly
 
 on:
   push:
@@ -9,6 +9,10 @@ on:
     branches:
       - main
       - 'gh/**'
+  # schedule:
+  #   # 3.27 am PST every day
+  #   - cron: "27 11 * * *"
+  # workflow_dispatch:
 
 concurrency:
   group: float8_test-${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_number || github.ref }}
@@ -28,11 +32,11 @@ jobs:
             torch-spec: '--pre torch --index-url https://download.pytorch.org/whl/nightly/cu124'
             gpu-arch-type: "cuda"
             gpu-arch-version: "12.4"
-          - name: H100
-            runs-on: linux.aws.h100
-            torch-spec: '--pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu124'
-            gpu-arch-type: "cuda"
-            gpu-arch-version: "12.4"
+          # - name: H100
+          #   runs-on: linux.aws.h100
+          #   torch-spec: '--pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu124'
+          #   gpu-arch-type: "cuda"
+          #   gpu-arch-version: "12.4"
 
     permissions:
       id-token: write
@@ -53,3 +57,4 @@ jobs:
         pip install -r dev-requirements.txt
         pip install .
         pytest test/float8 --verbose -s
+        pytest test/dtypes/test_affine_quantized_float.py --verbose -s

test/dtypes/test_affine_quantized_float.py

@@ -71,9 +71,9 @@ class TestAffineQuantizedFloat8Compile(InductorTestCase):
     @common_utils.parametrize("dtype", [torch.bfloat16, torch.float32])
     @common_utils.parametrize("mode", ["dynamic", "weight-only", "static"])
     @common_utils.parametrize("compile", [True, False])
-    @common_utils.parametrize(
-        "granularity", [PerTensor(), PerRow()] if is_sm_at_least_90() else [PerTensor()]
-    )
+    # @common_utils.parametrize(
+    #     "granularity", [PerTensor(), PerRow()] if is_sm_at_least_90() else [PerTensor()]
+    # )
     # Inputs are (M,..), K, N
     @common_utils.parametrize(
         "sizes",
@@ -147,7 +147,10 @@ def test_fp8_linear_variants(
     )
     def test_invalid_granularity(self):
         with pytest.raises(ValueError, match="Invalid granularity specification"):
-            float8_dynamic_activation_float8_weight(granularity="invalid")
+            model = ToyLinearModel(64, 64).eval().to(torch.float32).to("cuda")
+            quantize_(
+                model, float8_dynamic_activation_float8_weight(granularity="invalid")
+            )
 
     @unittest.skipIf(
         not is_sm_at_least_89(), "Requires GPU with compute capability >= 8.9"
@@ -157,7 +160,13 @@ def test_mismatched_granularity(self):
             ValueError,
             match="Different granularities for activation and weight are not supported",
         ):
-            float8_dynamic_activation_float8_weight(granularity=(PerTensor(), PerRow()))
+            model = ToyLinearModel(64, 64).eval().to(torch.float32).to("cuda")
+            quantize_(
+                model,
+                float8_dynamic_activation_float8_weight(
+                    granularity=(PerTensor(), PerRow())
+                ),
+            )
 
     @unittest.skipIf(
         not is_sm_at_least_89(), "Requires GPU with compute capability >= 8.9"
@@ -166,9 +175,16 @@ def test_unsupported_granularity(self):
         class UnsupportedGranularity:
             pass
 
-        with pytest.raises(ValueError, match="Invalid granularity types"):
-            float8_dynamic_activation_float8_weight(
-                granularity=(UnsupportedGranularity(), UnsupportedGranularity())
+        with pytest.raises(
+            ValueError,
+            match="Invalid granularity types:",
+        ):
+            model = ToyLinearModel(64, 64).eval().to(torch.float32).to("cuda")
+            quantize_(
+                model,
+                float8_dynamic_activation_float8_weight(
+                    granularity=(UnsupportedGranularity(), UnsupportedGranularity())
+                ),
             )
 
     @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")

test/float8/test_base.py

@@ -239,7 +239,7 @@ def test_axiswise_reshape(self):
         ],
     )
     @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-    @unittest.skipIf(not is_sm_at_least_90(), "Requires CUDA capability >= 9.0")
+    @unittest.skipIf(not is_sm_at_least_89(), "Requires CUDA capability >= 9.0")
     def test_axiswise_gemm(self, a_shape, a_granularity, b_granularity):
         a = torch.randn(*a_shape, dtype=torch.bfloat16, device="cuda")
         b = torch.randn(64, 32, dtype=torch.bfloat16, device="cuda")

test/float8/test_compile.py

@@ -198,9 +198,9 @@ def test_inductor_from_config_params(
         Float8LinearRecipeName.ROWWISE_WITH_GW_HP,
     ],
 )
-@unittest.skipIf(
-    not is_sm_at_least_90(), "CUDA with capability 9.0 or greater not available"
-)
+# @unittest.skipIf(
+#     not is_sm_at_least_90(), "CUDA with capability 9.0 or greater not available"
+# )
 def test_inductor_from_recipe(recipe_name):
     torch._dynamo.reset()
     config = Float8LinearConfig.from_recipe_name(recipe_name)
@@ -233,10 +233,10 @@ def forward(self, x):
             return x_fp8
 
     # TODO(future): figure out why the test below fails on CUDA capability 8.9
-    @unittest.skipIf(
-        not torch.cuda.is_available() or not is_sm_at_least_90(),
-        "CUDA with capability 9.0 or greater not available",
-    )
+    # @unittest.skipIf(
+    #     not torch.cuda.is_available() or not is_sm_at_least_90(),
+    #     "CUDA with capability 9.0 or greater not available",
+    # )
     def test_float8_with_graph_break_in_the_middle(self):
         """Test that having Float8Tensor object at the boundary of a subgraph"""
         cnts = CompileCounterWithBackend("inductor")

test/float8/test_numerics_integration.py

@@ -185,7 +185,7 @@ def test_encoder_fw_bw_from_config_params(
         ],
     )
     @pytest.mark.skipif(
-        not is_sm_at_least_90(), reason="requires SM90 compatible machine"
+        not is_sm_at_least_89(), reason="requires SM90 compatible machine"
     )
     @pytest.mark.skipif(IS_ROCM, reason="test doesn't currently work on the ROCm stack")
     def test_encoder_fw_bw_from_recipe(