[CI] Add e2e CI

.github/workflows/compiler-and-runtime-build.yml

@@ -15,6 +15,14 @@ on:
       - "scripts/frontends/**"
   workflow_dispatch:
 
+# Ensure that only a single job or workflow using the same
+# concurrency group will run at a time. This would cancel
+# any in-progress jobs in the same github workflow and github
+# ref (e.g. refs/heads/main or refs/pull/<pr_number>/merge).
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   clear_workspace:
     name: Clear workspace

.github/workflows/e2e_test.yaml

@@ -0,0 +1,31 @@
+name: e2e Numerical test CI
+
+on: 
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+  workflow_dispatch:
+
+# Ensure that only a single job or workflow using the same
+# concurrency group will run at a time. This would cancel
+# any in-progress jobs in the same github workflow and github
+# ref (e.g. refs/heads/main or refs/pull/<pr_number>/merge).
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  numerical_e2e_test:
+    name: e2e CI
+    runs-on: self-hosted
+    steps:
+      - name: clear workspace
+        run: rm -rf $GITHUB_WORKSPACE && mkdir $GITHUB_WORKSPACE
+      - name: Checkout byteir repo
+        uses: actions/checkout@v3
+      - name: Build and test e2e
+        run: ./scripts/e2e/build_and_test_e2e.sh ${{ secrets.LLVM_INSTALL_DIR }} ${{ secrets.TORCH_FRONTEND_LLVM_INSTALL_DIR }}
+        shell: bash

.github/workflows/torch-frontend-ci.yml

@@ -15,6 +15,14 @@ on:
       - "scripts/frontends/torch-frontend/**"
   workflow_dispatch:
 
+# Ensure that only a single job or workflow using the same
+# concurrency group will run at a time. This would cancel
+# any in-progress jobs in the same github workflow and github
+# ref (e.g. refs/heads/main or refs/pull/<pr_number>/merge).
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   torch_frontend_build_and_test:
     name: torch-frontend CI

compiler/python/byteir/compile.py

@@ -24,7 +24,7 @@ def _detect_cuda_with_nvidia_smi():
         sm_names = {
             "sm_70": ["V100"],
             "sm_75": ["T4", "Quadro T2000"],
-            "sm_80": ["PG509", "A100", "A10", "RTX 30", "A30", "RTX 40"],
+            "sm_80": ["PG509", "A100", "A10", "RTX 30", "A30", "RTX 40", "A16"],
             "sm_90": ["H100"],
         }
         for sm, names in sm_names.items():
@@ -222,6 +222,7 @@ def compile_cuda_with_ait(
         if verbose:
             _print_verbose(device_module, "// IR Dump After NVVM Codegen:")
         # write to output device ptx
+        assert _detect_cuda_with_nvidia_smi() != None
         byteir.translate_to_ptx(device_module.operation, output_file_dir + "/" + output_file_name, _detect_cuda_with_nvidia_smi())
 
     with context:

scripts/e2e/build_and_test_e2e.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+
+set -e
+set -x
+
+CUR_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
+# path to byteir root
+ROOT_PROJ_DIR="$CUR_DIR/../.."
+
+LLVM_INSTALL_DIR="$1"
+
+TORCH_FRONTEND_LLVM_INSTALL_DIR="$2"
+
+pushd $ROOT_PROJ_DIR
+# build compiler
+bash scripts/compiler/build_and_lit_test.sh $LLVM_INSTALL_DIR
+# build runtime
+bash scripts/runtime/build_and_test.sh --cuda --python --no-test $LLVM_INSTALL_DIR
+# build torch_frontend
+bash scripts/frontends/torch-frontend/build_and_test.sh $TORCH_FRONTEND_LLVM_INSTALL_DIR
+
+pip3 install $ROOT_PROJ_DIR/external/AITemplate/python/dist/*.whl --force-reinstall
+pip3 install $ROOT_PROJ_DIR/compiler/build/python/dist/*.whl --force-reinstall
+pip3 install $ROOT_PROJ_DIR/runtime/python/dist/*.whl --force-reinstall
+pip3 install $ROOT_PROJ_DIR/frontends/torch-frontend/build/torch-frontend/python/dist/*.whl --force-reinstall
+pip3 install -r $ROOT_PROJ_DIR/frontends/torch-frontend/torch-requirements.txt
+
+python3 tests/numerical_test/main.py
+rm -rf ./local_test
+popd

scripts/runtime/build_and_test.sh

@@ -71,6 +71,13 @@ cmake -GNinja \
 
 cmake --build "$BUILD_DIR" --target all --target install
 
+if [[ $BRT_ENABLE_PYTHON_BINDINGS == "ON" ]]; then
+  pushd $PROJ_DIR/python
+  # note: python packing depend on `--target install`
+  python3 setup.py bdist_wheel
+  popd
+fi
+
 if [[ $BRT_USE_CUDA == "ON" ]] && [[ $BRT_ENABLE_ASAN == "ON" ]]; then
   export ASAN_OPTIONS=protect_shadow_gap=0
 fi

tests/numerical_test/execute.py

@@ -86,6 +86,7 @@ def get_entry_func_name(interp):
 
 
 def compile_and_run_mlir(mhlo_file, target):
+    np.random.seed(0)
     try:
         interp = Interpreter.load_from_file(mhlo_file)
         np_inputs = generate_np_inputs(interp)

tests/numerical_test/main.py

@@ -40,10 +40,10 @@
 
 SM80_PLUS_TESTS = [
     "dot_f32.mlir",
-    "MatmulF32Module_basic",
-    "bmm_rrr_add_f32.mlir",
-    "bmm_rrr_f32.mlir",
     "bmm_rrr_permute_f32.mlir",
+    "MatmulF32Module_basic",
+    "BatchMatmulAddF32Module_basic",
+    "BatchMatmulF32Module",
 ]
 
 
@@ -59,7 +59,7 @@ def _detect_cuda_with_nvidia_smi():
         sm_names = {
             70: ["V100"],
             75: ["T4", "Quadro T2000"],
-            80: ["PG509", "A100", "A10", "RTX 30", "A30", "RTX 40"],
+            80: ["PG509", "A100", "A10", "RTX 30", "A30", "RTX 40", "A16"],
             90: ["H100"],
         }
         for sm, names in sm_names.items():
@@ -120,8 +120,8 @@ def main():
     elif args.config == 'torch':
         results = run_torch_test(arch)
     failed = report_results(results)
-    # TODO(zzk): use test infra for dynamo tests
-    run_torch_dynamo_tests(arch)
+    # TODO(zzk): disable flash attn test for now
+    # run_torch_dynamo_tests(arch)
     sys.exit(1 if failed else 0)
 
 

tests/numerical_test/mlir_tests/ops/bmm_rrr_permute_f32.mlir

@@ -1,6 +1,6 @@
-func.func @bmm_rrr(%arg0 : tensor<12x256x256xf32>, %arg1 : tensor<12x256x64xf32>) -> tensor<1x256x12x64xf32> {
-    %0 = "mhlo.dot_general"(%arg0, %arg1) {dot_dimension_numbers = #mhlo.dot<lhs_batching_dimensions = [0], rhs_batching_dimensions = [0], lhs_contracting_dimensions = [2], rhs_contracting_dimensions = [1]>} : (tensor<12x256x256xf32>, tensor<12x256x64xf32>) -> tensor<12x256x64xf32>
-    %1 = mhlo.reshape %0 : (tensor<12x256x64xf32>) -> tensor<1x12x256x64xf32>
-    %2 = "mhlo.transpose"(%1) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<1x12x256x64xf32>) -> tensor<1x256x12x64xf32>
-    return %2 : tensor<1x256x12x64xf32>
+func.func @bmm_rrr_permute_f32(%arg0: tensor<4x2x2xf32>, %arg1: tensor<4x2x2xf32>) -> tensor<2x2x2x2xf32> {
+  %0 = "mhlo.dot_general"(%arg0, %arg1) {dot_dimension_numbers = #mhlo.dot<lhs_batching_dimensions = [0], rhs_batching_dimensions = [0], lhs_contracting_dimensions = [2], rhs_contracting_dimensions = [1]>} : (tensor<4x2x2xf32>, tensor<4x2x2xf32>) -> tensor<4x2x2xf32>
+  %1 = mhlo.reshape %0 : (tensor<4x2x2xf32>) -> tensor<2x2x2x2xf32>
+  %2 = "mhlo.transpose"(%1) {permutation = dense<[0, 2, 1, 3]> : tensor<4xi64>} : (tensor<2x2x2x2xf32>) -> tensor<2x2x2x2xf32>
+  return %2 : tensor<2x2x2x2xf32>
 }

tests/numerical_test/mlir_tests/ops/dot_f32.mlir

@@ -1,4 +1,4 @@
-func.func @gemm_rrr_f32(%arg0 : tensor<256x256xf32>, %arg1 : tensor<256x256xf32>) -> tensor<256x256xf32> {
-  %0 = "mhlo.dot"(%arg0, %arg1) : (tensor<256x256xf32>, tensor<256x256xf32>) -> tensor<256x256xf32>
-  return %0 : tensor<256x256xf32>
+func.func @gemm_rrr_f32(%arg0 : tensor<4x4xf32>, %arg1 : tensor<4x4xf32>) -> tensor<4x4xf32> {
+  %0 = "mhlo.dot"(%arg0, %arg1) : (tensor<4x4xf32>, tensor<4x4xf32>) -> tensor<4x4xf32>
+  return %0 : tensor<4x4xf32>
 }

tests/numerical_test/torch_e2e_testing/test_suite/basic.py

@@ -58,3 +58,31 @@ def forward(self, a, b):
 @register_test_case(module_factory=lambda: MatmulF32Module())
 def MatmulF32Module_basic(module, tu: TestUtils):
     module.forward(tu.rand(5, 6), tu.rand(6, 10))
+
+
+class BatchMatmulF32Module(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, a, b):
+        return torch.bmm(a, b)
+
+
+@register_test_case(module_factory=lambda: BatchMatmulF32Module())
+def BatchMatmulF32Module_basic(module, tu: TestUtils):
+    module.forward(tu.rand(2, 5, 6), tu.rand(2, 6, 10))
+
+
+class BatchMatmulAddF32Module(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, a, b, c):
+        return c + torch.bmm(a, b)
+
+
+@register_test_case(module_factory=lambda: BatchMatmulAddF32Module())
+def BatchMatmulAddF32Module_basic(module, tu: TestUtils):
+    module.forward(tu.rand(2, 5, 6), tu.rand(2, 6, 10), tu.rand(2, 5, 10))