Merge remote-tracking branch 'origin/main' into bench_structure

.github/workflows/regression_test_rocm.yml

@@ -0,0 +1,49 @@
+name: Run Regression Tests on ROCm
+
+on:
+  push:
+    branches:
+      - main
+    tags:
+      - ciflow/rocm/*
+
+concurrency:
+  group: regression_test-${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_number || github.ref }}
+  cancel-in-progress: true
+
+env:
+  HF_TOKEN: ${{ secrets.HF_TOKEN }}
+
+jobs:
+  test-nightly:
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - name: ROCM Nightly
+            runs-on: linux.rocm.gpu.torchao
+            torch-spec: '--pre torch==2.7.0.dev20250122 --index-url https://download.pytorch.org/whl/nightly/rocm6.3'
+            gpu-arch-type: "rocm"
+            gpu-arch-version: "6.3"
+
+    permissions:
+      id-token: write
+      contents: read
+    uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main
+    with:
+      timeout: 120
+      no-sudo: ${{ matrix.gpu-arch-type == 'rocm' }}
+      runner: ${{ matrix.runs-on }}
+      gpu-arch-type: ${{ matrix.gpu-arch-type }}
+      gpu-arch-version: ${{ matrix.gpu-arch-version }}
+      submodules: recursive
+      script: |
+        conda create -n venv python=3.9 -y
+        conda activate venv
+        python -m pip install --upgrade pip
+        pip install ${{ matrix.torch-spec }}
+        pip install -r dev-requirements.txt
+        pip install .
+        export CONDA=$(dirname $(dirname $(which conda)))
+        export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH
+        pytest test --verbose -s

benchmarks/float8/bench_linear_float8.py

@@ -23,10 +23,6 @@
     ScalingType,
 )
 from torchao.float8.float8_linear import Float8Linear
-from torchao.float8.float8_linear_utils import (
-    linear_requires_sync,
-    sync_float8_amax_and_scale_history,
-)
 from torchao.float8.float8_tensor import ScaledMMConfig
 
 # estimating TOPs for matmuls in fp32, fp16, fp8
@@ -122,39 +118,18 @@ def main(
     scaling_type_grad_output = ScalingType(scaling_type_grad_output)
     scaling_granularity = ScalingGranularity(scaling_granularity)
 
-    if scaling_type_input is ScalingType.STATIC:
-        cast_config_input = CastConfig(
-            scaling_type=scaling_type_input,
-            static_scale=torch.tensor([1.0], device="cuda"),
-            scaling_granularity=scaling_granularity,
-        )
-    else:
-        cast_config_input = CastConfig(
-            scaling_type=scaling_type_input,
-            scaling_granularity=scaling_granularity,
-        )
-    if scaling_type_weight is ScalingType.STATIC:
-        cast_config_weight = CastConfig(
-            scaling_type=scaling_type_weight,
-            static_scale=torch.tensor([1.0], device="cuda"),
-            scaling_granularity=scaling_granularity,
-        )
-    else:
-        cast_config_weight = CastConfig(
-            scaling_type=scaling_type_weight,
-            scaling_granularity=scaling_granularity,
-        )
-    if scaling_type_grad_output is ScalingType.STATIC:
-        cast_config_grad_output = CastConfig(
-            scaling_type=scaling_type_grad_output,
-            static_scale=torch.tensor([1.0], device="cuda"),
-            scaling_granularity=scaling_granularity,
-        )
-    else:
-        cast_config_grad_output = CastConfig(
-            scaling_type=scaling_type_grad_output,
-            scaling_granularity=scaling_granularity,
-        )
+    cast_config_input = CastConfig(
+        scaling_type=scaling_type_input,
+        scaling_granularity=scaling_granularity,
+    )
+    cast_config_weight = CastConfig(
+        scaling_type=scaling_type_weight,
+        scaling_granularity=scaling_granularity,
+    )
+    cast_config_grad_output = CastConfig(
+        scaling_type=scaling_type_grad_output,
+        scaling_granularity=scaling_granularity,
+    )
 
     config = Float8LinearConfig(
         cast_config_input=cast_config_input,
@@ -185,7 +160,7 @@ def main(
             copy.deepcopy(linear_ref),
             config=config,
         )
-        scaling_repr = f"{linear_float8.scaling_type_repr()},{linear_float8.scaling_granularity_repr()}"
+        scaling_repr = linear_float8.extra_repr()
 
         if fast_accum:
             linear_float8.forward_config = ScaledMMConfig(False, True, False)
@@ -196,8 +171,6 @@ def main(
         ref_forw_backward = lambda: linear_ref(input_tensor).sum().backward()
 
         def float8_forw_backward():
-            if linear_requires_sync(config):
-                sync_float8_amax_and_scale_history(linear_float8)
             linear_float8(input_tensor).sum().backward()
 
         def n_times(n, fn, *args, **kwargs):