Merge remote-tracking branch 'origin/main' into lint_fixes_test_quant…

…ization

README.md

@@ -178,7 +178,7 @@ We're also fortunate to be integrated into some of the leading open-source libra
 3. Mobius HQQ backend leveraged our int4 kernels to get [195 tok/s on a 4090](https://github.com/mobiusml/hqq#faster-inference)
 4. [TorchTune](https://github.com/pytorch/torchtune) for our QLoRA and QAT recipes
 5. [torchchat](https://github.com/pytorch/torchchat) for post training quantization
-6. [SGLang](https://github.com/sgl-project/sglang/pull/1341) for LLM inference quantization
+6. SGLang for LLM serving: [usage](https://github.com/sgl-project/sglang/blob/4f2ee48ed1c66ee0e189daa4120581de324ee814/docs/backend/backend.md?plain=1#L83) and the major [PR](https://github.com/sgl-project/sglang/pull/1341).
 
 ## Videos
 * [Keynote talk at GPU MODE IRL](https://youtu.be/FH5wiwOyPX4?si=VZK22hHz25GRzBG1&t=1009)
@@ -205,4 +205,5 @@ If you find the torchao library useful, please cite it in your work as below.
   license = {BSD-3-Clause},
   month = oct,
   year = {2024}
+}
 ```

test/dtypes/test_affine_quantized.py

@@ -17,9 +17,11 @@
     int8_weight_only,
 )
 from torchao.quantization.quant_primitives import MappingType
-from torchao.utils import TORCH_VERSION_AT_LEAST_2_5, TORCH_VERSION_AT_LEAST_2_6
-
-is_cuda_8_9 = torch.cuda.is_available() and torch.cuda.get_device_capability() >= (8, 9)
+from torchao.utils import (
+    TORCH_VERSION_AT_LEAST_2_5,
+    TORCH_VERSION_AT_LEAST_2_6,
+    is_sm_at_least_89,
+)
 
 
 def get_quantization_functions(do_sparse: bool, do_int4: bool, device: str = "cuda"):
@@ -42,7 +44,7 @@ def get_quantization_functions(do_sparse: bool, do_int4: bool, device: str = "cu
             int8_dynamic_activation_int8_weight(layout=SemiSparseLayout())
         )
 
-    if is_cuda_8_9:
+    if is_sm_at_least_89():
         base_functions.append(float8_weight_only())
 
     return base_functions

test/dtypes/test_affine_quantized_float.py

@@ -37,13 +37,14 @@
     MappingType,
     choose_qparams_affine,
 )
+from torchao.utils import (
+    is_sm_at_least_89,
+    is_sm_at_least_90,
+)
 
 random.seed(0)
 torch.manual_seed(0)
 
-is_H100 = torch.cuda.is_available() and torch.cuda.get_device_capability() >= (9, 0)
-is_cuda_8_9 = torch.cuda.is_available() and torch.cuda.get_device_capability() >= (8, 9)
-
 
 class ToyLinearModel(torch.nn.Module):
     def __init__(self, in_features, out_features):
@@ -59,12 +60,14 @@ def forward(self, x):
 
 class TestAffineQuantizedFloat8Compile(InductorTestCase):
     @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
-    @unittest.skipIf(not is_cuda_8_9, "Requires GPU with compute capability >= 8.9")
+    @unittest.skipIf(
+        not is_sm_at_least_89(), "Requires GPU with compute capability >= 8.9"
+    )
     @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_H100 else [PerTensor()]
+        "granularity", [PerTensor(), PerRow()] if is_sm_at_least_90() else [PerTensor()]
     )
     # Inputs are (M,..), K, N
     @common_utils.parametrize(
@@ -134,20 +137,26 @@ def test_fp8_linear_variants(
                 compute_error(output_original, output_quantized) > 20
             ), f"Quantization error is too high got a SQNR of {error}"
 
-    @unittest.skipIf(not is_cuda_8_9, "Requires GPU with compute capability >= 8.9")
+    @unittest.skipIf(
+        not is_sm_at_least_89(), "Requires GPU with compute capability >= 8.9"
+    )
     def test_invalid_granularity(self):
         with pytest.raises(ValueError, match="Invalid granularity specification"):
             float8_dynamic_activation_float8_weight(granularity="invalid")
 
-    @unittest.skipIf(not is_cuda_8_9, "Requires GPU with compute capability >= 8.9")
+    @unittest.skipIf(
+        not is_sm_at_least_89(), "Requires GPU with compute capability >= 8.9"
+    )
     def test_mismatched_granularity(self):
         with pytest.raises(
             ValueError,
             match="Different granularities for activation and weight are not supported",
         ):
             float8_dynamic_activation_float8_weight(granularity=(PerTensor(), PerRow()))
 
-    @unittest.skipIf(not is_cuda_8_9, "Requires GPU with compute capability >= 8.9")
+    @unittest.skipIf(
+        not is_sm_at_least_89(), "Requires GPU with compute capability >= 8.9"
+    )
     def test_unsupported_granularity(self):
         class UnsupportedGranularity:
             pass
@@ -158,7 +167,9 @@ class UnsupportedGranularity:
             )
 
     @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
-    @unittest.skipIf(not is_cuda_8_9, "Requires GPU with compute capability >= 8.9")
+    @unittest.skipIf(
+        not is_sm_at_least_89(), "Requires GPU with compute capability >= 8.9"
+    )
     def test_per_row_with_float32(self):
         with pytest.raises(
             AssertionError,
@@ -170,7 +181,9 @@ def test_per_row_with_float32(self):
             )
 
     @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
-    @unittest.skipIf(not is_cuda_8_9, "Requires GPU with compute capability >= 8.9")
+    @unittest.skipIf(
+        not is_sm_at_least_89(), "Requires GPU with compute capability >= 8.9"
+    )
     @common_utils.parametrize("mode", ["dynamic", "weight-only", "static"])
     def test_serialization(self, mode: str):
         # Create and quantize the model
@@ -240,7 +253,9 @@ def test_serialization(self, mode: str):
                 ), f"Scales do not match for {layer_name}"
 
     @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
-    @unittest.skipIf(not is_cuda_8_9, "Requires GPU with compute capability >= 8.9")
+    @unittest.skipIf(
+        not is_sm_at_least_89(), "Requires GPU with compute capability >= 8.9"
+    )
     def test_fp8_weight_dimension_warning(self):
         # Create model with incompatible dimensions (not multiples of 16)
         model = ToyLinearModel(10, 25).cuda()  # 10x25 and 25x10 weights

test/float8/test_base.py

@@ -14,7 +14,11 @@
 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_at_least_89,
+    is_sm_at_least_90,
+)
 
 if not TORCH_VERSION_AT_LEAST_2_5:
     pytest.skip("Unsupported PyTorch version", allow_module_level=True)
@@ -60,10 +64,6 @@
 torch.manual_seed(0)
 
 
-is_cuda_8_9 = torch.cuda.is_available() and torch.cuda.get_device_capability() >= (8, 9)
-is_cuda_9_0 = torch.cuda.is_available() and torch.cuda.get_device_capability() >= (9, 0)
-
-
 def bitwise_identical(a: Float8Tensor, b: Float8Tensor) -> bool:
     assert torch.all(a._scale == b._scale).item(), "scales are not identical"
     assert torch.all(a._data == b._data).item(), "data is not identical"
@@ -219,7 +219,7 @@ def test_axiswise_reshape(self):
         ],
     )
     @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-    @unittest.skipIf(not is_cuda_9_0, "Requires CUDA capability >= 9.0")
+    @unittest.skipIf(not is_sm_at_least_90(), "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")
@@ -333,7 +333,9 @@ def _test_linear_impl(
             # verify initialization flags got updated
             assert m_fp8.is_amax_initialized, "Amax was not properly initialized"
 
-    @pytest.mark.parametrize("emulate", [True, False] if is_cuda_8_9 else [True])
+    @pytest.mark.parametrize(
+        "emulate", [True, False] if is_sm_at_least_89() else [True]
+    )
     @pytest.mark.parametrize("x_shape", [(16, 16), (2, 16, 16), (3, 2, 16, 16)])
     @pytest.mark.parametrize(
         "scaling_type_input",
@@ -415,7 +417,9 @@ def test_linear_from_recipe(
             config,
         )
 
-    @pytest.mark.parametrize("emulate", [True, False] if is_cuda_8_9 else [True])
+    @pytest.mark.parametrize(
+        "emulate", [True, False] if is_sm_at_least_89() else [True]
+    )
     @pytest.mark.parametrize(
         "linear_dtype", [torch.float16, torch.bfloat16, torch.float32]
     )
@@ -462,7 +466,9 @@ def test_autocast_outputs(
     @pytest.mark.parametrize(
         "linear_dtype", [torch.float16, torch.bfloat16, torch.float32]
     )
-    @pytest.mark.parametrize("emulate", [True, False] if is_cuda_8_9 else [True])
+    @pytest.mark.parametrize(
+        "emulate", [True, False] if is_sm_at_least_89() else [True]
+    )
     @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
     def test_type_cast(self, linear_dtype: torch.dtype, emulate: bool):
         m = nn.Linear(32, 16, device="cuda", dtype=linear_dtype)
@@ -523,18 +529,33 @@ def test_repr(self):
         s = m.__repr__()
         assert "i:dyn_ten,w:del_ten,go:dyn_ten" in s
 
-    @unittest.skipIf(not is_cuda_8_9, "CUDA 8.9 not available")
+    @unittest.skipIf(not is_sm_at_least_89(), "CUDA 8.9 not available")
     def test_inference_mode(self):
         x = torch.randn(32, 32, device="cuda")
         m = nn.Sequential(nn.Linear(32, 32)).cuda()
         m = convert_to_float8_training(m)
         with torch.inference_mode(mode=True):
             m(x)
 
+    @unittest.skipIf(not is_sm_at_least_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(
-        not is_cuda_8_9,
+        not is_sm_at_least_89(),
         "CUDA not available",
     )
     @pytest.mark.parametrize(
@@ -576,10 +597,10 @@ 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")
+    @unittest.skipIf(not is_sm_at_least_89(), "CUDA not available")
     def test_different_configs_error(self):
         x_fp32 = torch.randn(16, 16, device="cuda")
         x_scale = torch.tensor(1.0, device="cuda")
@@ -615,7 +636,7 @@ def test_different_configs_error(self):
             a @ b
 
     @unittest.skipIf(
-        not is_cuda_8_9,
+        not is_sm_at_least_89(),
         "CUDA not available",
     )
     @pytest.mark.parametrize(

test/float8/test_compile.py

@@ -11,7 +11,11 @@
 
 import pytest
 
-from torchao.utils import TORCH_VERSION_AT_LEAST_2_5
+from torchao.utils import (
+    TORCH_VERSION_AT_LEAST_2_5,
+    is_sm_at_least_89,
+    is_sm_at_least_90,
+)
 
 if not TORCH_VERSION_AT_LEAST_2_5:
     pytest.skip("Unsupported PyTorch version", allow_module_level=True)
@@ -46,10 +50,6 @@
 from torchao.float8.float8_utils import e4m3_dtype
 from torchao.testing.float8.test_utils import get_test_float8_linear_config
 
-# TODO(future PR): standardize IS_H100 with the rest of the codebase
-is_H100 = torch.cuda.is_available() and torch.cuda.get_device_capability() >= (9, 0)
-is_cuda_8_9 = torch.cuda.is_available() and torch.cuda.get_device_capability() >= (8, 9)
-
 
 def _test_compile_base(
     backend: str,
@@ -99,7 +99,7 @@ def _test_compile_base(
     "scaling_type_grad_output",
     [ScalingType.DELAYED, ScalingType.DYNAMIC, ScalingType.STATIC],
 )
-@pytest.mark.parametrize("emulate", [False, True] if is_cuda_8_9 else [True])
+@pytest.mark.parametrize("emulate", [False, True] if is_sm_at_least_89() else [True])
 @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float32])
 @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
 def test_eager_only(
@@ -126,7 +126,7 @@ def test_eager_only(
 
 
 @pytest.mark.parametrize("fullgraph", [True])
-@pytest.mark.parametrize("emulate", [False, True] if is_cuda_8_9 else [True])
+@pytest.mark.parametrize("emulate", [False, True] if is_sm_at_least_89() else [True])
 @pytest.mark.parametrize(
     "scaling_type_input", [ScalingType.DELAYED, ScalingType.DYNAMIC, ScalingType.STATIC]
 )
@@ -177,7 +177,7 @@ def test_aot_eager(
     [ScalingType.DELAYED, ScalingType.DYNAMIC, ScalingType.STATIC],
 )
 @unittest.skipIf(
-    not torch.cuda.is_available() or not is_cuda_8_9,
+    not torch.cuda.is_available() or not is_sm_at_least_89(),
     "CUDA with float8 support not available",
 )
 @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float32])
@@ -215,7 +215,9 @@ def test_inductor_from_config_params(
         Float8LinearRecipeName.LW_AXISWISE_WITH_GW_HP,
     ],
 )
-@unittest.skipIf(not is_H100, "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 = recipe_name_to_linear_config(recipe_name)
@@ -253,7 +255,7 @@ def forward(self, x):
 
     # TODO(future): figure out why the test below fails on CUDA capability 8.9
     @unittest.skipIf(
-        not torch.cuda.is_available() or not is_H100,
+        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):
@@ -269,7 +271,7 @@ def test_float8_with_graph_break_in_the_middle(self):
         torch.testing.assert_close(y_eager, y_compiled)
 
     @unittest.skipIf(
-        not torch.cuda.is_available() or not is_cuda_8_9,
+        not torch.cuda.is_available() or not is_sm_at_least_89(),
         "CUDA with float8 support not available",
     )
     def test_float8_graph_input(self):
@@ -293,7 +295,7 @@ def to_float(x):
         torch.testing.assert_close(y2_eager, y2_compiled)
 
     @unittest.skipIf(
-        not torch.cuda.is_available() or not is_cuda_8_9,
+        not torch.cuda.is_available() or not is_sm_at_least_89(),
         "CUDA with float8 support not available",
     )
     def test_float8_graph_output(self):
@@ -323,7 +325,7 @@ def test_float8_graph_output(self):
 
 
 @unittest.skipIf(
-    not torch.cuda.is_available() or not is_cuda_8_9,
+    not torch.cuda.is_available() or not is_sm_at_least_89(),
     "CUDA with float8 support not available",
 )
 def test_sync_amax_func():
@@ -364,7 +366,7 @@ def __exit__(self, *args):
 
 
 @unittest.skipIf(
-    not torch.cuda.is_available() or not is_cuda_8_9,
+    not torch.cuda.is_available() or not is_sm_at_least_89(),
     "CUDA with float8 support not available",
 )
 def test_sync_amax_func_cuda_graph_success():
@@ -396,7 +398,7 @@ def test_sync_amax_func_cuda_graph_success():
 
 
 @unittest.skipIf(
-    not is_cuda_8_9,
+    not is_sm_at_least_89(),
     "CUDA not available",
 )
 @pytest.mark.parametrize(

test/float8/test_fsdp2/test_fsdp2.py

@@ -6,7 +6,7 @@
 
 import pytest
 
-from torchao.utils import TORCH_VERSION_AT_LEAST_2_5
+from torchao.utils import TORCH_VERSION_AT_LEAST_2_5, is_sm_at_least_89
 
 if not TORCH_VERSION_AT_LEAST_2_5:
     pytest.skip("Unsupported PyTorch version", allow_module_level=True)
@@ -40,8 +40,7 @@
 from torchao.float8.fsdp_utils import WeightWithDynamicFloat8CastTensor
 from torchao.testing.float8.fsdp2_utils import check_parity_bf16_mp, check_parity_no_mp
 
-is_cuda_8_9 = torch.cuda.is_available() and torch.cuda.get_device_capability() >= (8, 9)
-if not is_cuda_8_9:
+if not is_sm_at_least_89():
     pytest.skip("Unsupported CUDA device capability version", allow_module_level=True)