Install tritonbench as a library

.gitignore

@@ -13,4 +13,4 @@ __pycache__/
 .DS_Store
 .ipynb_checkpoints/
 .idea
-torchbench.egg-info/
+*.egg-info/

README.md

@@ -26,12 +26,26 @@ By default, it will install the latest PyTorch nightly release and use the Trito
 
 ## Basic Usage
 
-To benchmark an operator, use the following command:
+To benchmark an operator, run the following command:
 
 ```
 $ python run.py --op gemm
 ```
 
+## Install as a library
+
+To install as a library:
+
+```
+$ pip install -e .
+# in your own benchmark script
+import tritonbench
+from tritonbench.utils import parser
+op_args = parser.parse_args()
+addmm_bench = tritonbench.load_opbench_by_name("addmm")(op_args)
+addmm_bench.run()
+```
+
 ## Submodules
 
 We depend on the following projects as a source of customized Triton or CUTLASS kernels:

pyproject.toml

@@ -1,3 +1,18 @@
+[build-system]
+requires = ["setuptools>=40.8.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "tritonbench"
+version = "0.0.1"
+dependencies = [
+    "torch",
+    "triton",
+]
+
+[tool.setuptools.packages.find]
+include = ["tritonbench*"]
+
 [tool.ufmt]
 formatter = "ruff-api"
 excludes = ["submodules/"]

tritonbench/__init__.py

@@ -0,0 +1,5 @@
+from .operators import list_operators, load_opbench_by_name
+from .operators_collection import (
+    list_operator_collections,
+    list_operators_by_collection,
+)

tritonbench/operators/__init__.py

@@ -1,78 +1 @@
-import importlib
-import os
-import pathlib
-from typing import List
-
-OPBENCH_DIR = "operators"
-INTERNAL_OPBENCH_DIR = "fb"
-
-
-def _dir_contains_file(dir, file_name) -> bool:
-    names = map(lambda x: x.name, filter(lambda x: x.is_file(), dir.iterdir()))
-    return file_name in names
-
-
-def _is_internal_operator(op_name: str) -> bool:
-    p = (
-        pathlib.Path(__file__)
-        .parent.parent.joinpath(OPBENCH_DIR)
-        .joinpath(INTERNAL_OPBENCH_DIR)
-        .joinpath(op_name)
-    )
-    if p.exists() and p.joinpath("__init__.py").exists():
-        return True
-    return False
-
-
-def _list_opbench_paths() -> List[str]:
-    p = pathlib.Path(__file__).parent.parent.joinpath(OPBENCH_DIR)
-    # Only load the model directories that contain a "__init.py__" file
-    opbench = sorted(
-        str(child.absolute())
-        for child in p.iterdir()
-        if child.is_dir() and _dir_contains_file(child, "__init__.py")
-    )
-    p = p.joinpath(INTERNAL_OPBENCH_DIR)
-    if p.exists():
-        o = sorted(
-            str(child.absolute())
-            for child in p.iterdir()
-            if child.is_dir() and _dir_contains_file(child, "__init__.py")
-        )
-        opbench.extend(o)
-    return opbench
-
-
-def list_operators() -> List[str]:
-    operators = list(map(lambda y: os.path.basename(y), _list_opbench_paths()))
-    if INTERNAL_OPBENCH_DIR in operators:
-        operators.remove(INTERNAL_OPBENCH_DIR)
-    return operators
-
-
-def load_opbench_by_name(op_name: str):
-    opbench_list = filter(
-        lambda x: op_name.lower() == x.lower(),
-        map(lambda y: os.path.basename(y), _list_opbench_paths()),
-    )
-    opbench_list = list(opbench_list)
-    if not opbench_list:
-        raise RuntimeError(f"{op_name} is not found in the Tritonbench operator list.")
-    assert (
-        len(opbench_list) == 1
-    ), f"Found more than one operators {opbench_list} matching the required name: {op_name}"
-    op_name = opbench_list[0]
-    op_pkg = (
-        op_name
-        if not _is_internal_operator(op_name)
-        else f"{INTERNAL_OPBENCH_DIR}.{op_name}"
-    )
-    module = importlib.import_module(f".{op_pkg}", package=__name__)
-
-    Operator = getattr(module, "Operator", None)
-    if Operator is None:
-        print(f"Warning: {module} does not define attribute Operator, skip it")
-        return None
-    if not hasattr(Operator, "name"):
-        Operator.name = op_name
-    return Operator
+from .op import list_operators, load_opbench_by_name

tritonbench/operators/addmm/hstu.py

@@ -4,6 +4,7 @@
 
 import torch
 import triton
+
 from tritonbench.utils.path_utils import add_path, SUBMODULE_PATH
 
 with add_path(str(SUBMODULE_PATH.joinpath("generative-recommenders"))):

tritonbench/operators/fp8_gemm_rowwise/operator.py

@@ -6,6 +6,7 @@
 
 import torch
 import triton
+
 from tritonbench.utils.data_utils import get_production_shapes
 
 from tritonbench.utils.triton_op import (

tritonbench/operators/geglu/operator.py

@@ -5,6 +5,7 @@
 
 from transformers.models.llama.configuration_llama import LlamaConfig
 from transformers.models.llama.modeling_llama import LlamaMLP
+
 from tritonbench.utils.triton_op import (
     BenchmarkOperator,
     register_benchmark,

tritonbench/operators/gemm/operator.py

@@ -6,6 +6,7 @@
 import torch
 import torch._inductor.config as inductor_config
 import triton
+
 from tritonbench.utils.data_utils import get_production_shapes
 
 from tritonbench.utils.path_utils import REPO_PATH

tritonbench/operators/jagged_layer_norm/operator.py

@@ -7,6 +7,7 @@
 
 import torch
 import triton
+
 from tritonbench.utils.jagged_utils import (
     ABSOLUTE_TOLERANCE,
     EPSILON,

tritonbench/operators/jagged_mean/operator.py

@@ -7,6 +7,7 @@
 
 import torch
 import triton
+
 from tritonbench.utils.jagged_utils import (
     ABSOLUTE_TOLERANCE,
     generate_input_vals,

tritonbench/operators/jagged_softmax/operator.py

@@ -7,6 +7,7 @@
 
 import torch
 import triton
+
 from tritonbench.utils.jagged_utils import (
     ABSOLUTE_TOLERANCE,
     generate_input_vals,

tritonbench/operators/jagged_sum/operator.py

@@ -7,6 +7,7 @@
 
 import torch
 import triton
+
 from tritonbench.utils.jagged_utils import (
     ABSOLUTE_TOLERANCE,
     generate_input_vals,

tritonbench/operators/layer_norm/operator.py

@@ -3,6 +3,7 @@
 import torch
 import torch.nn.functional as F
 import triton
+
 from tritonbench.utils.triton_op import (
     BenchmarkOperator,
     BenchmarkOperatorMetrics,

tritonbench/operators/op.py

@@ -0,0 +1,78 @@
+import importlib
+import os
+import pathlib
+from typing import List
+
+OPBENCH_DIR = "operators"
+INTERNAL_OPBENCH_DIR = "fb"
+
+
+def _dir_contains_file(dir, file_name) -> bool:
+    names = map(lambda x: x.name, filter(lambda x: x.is_file(), dir.iterdir()))
+    return file_name in names
+
+
+def _is_internal_operator(op_name: str) -> bool:
+    p = (
+        pathlib.Path(__file__)
+        .parent.parent.joinpath(OPBENCH_DIR)
+        .joinpath(INTERNAL_OPBENCH_DIR)
+        .joinpath(op_name)
+    )
+    if p.exists() and p.joinpath("__init__.py").exists():
+        return True
+    return False
+
+
+def _list_opbench_paths() -> List[str]:
+    p = pathlib.Path(__file__).parent.parent.joinpath(OPBENCH_DIR)
+    # Only load the model directories that contain a "__init.py__" file
+    opbench = sorted(
+        str(child.absolute())
+        for child in p.iterdir()
+        if child.is_dir() and _dir_contains_file(child, "__init__.py")
+    )
+    p = p.joinpath(INTERNAL_OPBENCH_DIR)
+    if p.exists():
+        o = sorted(
+            str(child.absolute())
+            for child in p.iterdir()
+            if child.is_dir() and _dir_contains_file(child, "__init__.py")
+        )
+        opbench.extend(o)
+    return opbench
+
+
+def list_operators() -> List[str]:
+    operators = list(map(lambda y: os.path.basename(y), _list_opbench_paths()))
+    if INTERNAL_OPBENCH_DIR in operators:
+        operators.remove(INTERNAL_OPBENCH_DIR)
+    return operators
+
+
+def load_opbench_by_name(op_name: str):
+    opbench_list = filter(
+        lambda x: op_name.lower() == x.lower(),
+        map(lambda y: os.path.basename(y), _list_opbench_paths()),
+    )
+    opbench_list = list(opbench_list)
+    if not opbench_list:
+        raise RuntimeError(f"{op_name} is not found in the Tritonbench operator list.")
+    assert (
+        len(opbench_list) == 1
+    ), f"Found more than one operators {opbench_list} matching the required name: {op_name}"
+    op_name = opbench_list[0]
+    op_pkg = (
+        op_name
+        if not _is_internal_operator(op_name)
+        else f"{INTERNAL_OPBENCH_DIR}.{op_name}"
+    )
+    module = importlib.import_module(f"..{op_pkg}", package=__name__)
+
+    Operator = getattr(module, "Operator", None)
+    if Operator is None:
+        print(f"Warning: {module} does not define attribute Operator, skip it")
+        return None
+    if not hasattr(Operator, "name"):
+        Operator.name = op_name
+    return Operator

tritonbench/operators/op_task.py

@@ -7,6 +7,7 @@
 from typing import Any, Dict, List, Optional
 
 import torch
+
 from tritonbench.components.tasks import base as base_task
 from tritonbench.components.workers import subprocess_worker
 

tritonbench/operators/ragged_attention/hstu.py

@@ -1,5 +1,6 @@
 import torch
 import triton
+
 from tritonbench.utils.path_utils import add_path, SUBMODULE_PATH
 from tritonbench.utils.triton_op import IS_FBCODE
 

tritonbench/operators/ragged_attention/operator.py

@@ -3,6 +3,7 @@
 from typing import Any, Callable, List, Optional
 
 import torch
+
 from tritonbench.utils.input import input_filter
 
 from tritonbench.utils.triton_op import (

tritonbench/operators/softmax/operator.py

@@ -3,6 +3,7 @@
 import torch
 import triton
 import triton.language as tl
+
 from tritonbench.utils.data_utils import get_production_shapes
 
 from tritonbench.utils.triton_op import (

tritonbench/operators/sum/operator.py

@@ -7,6 +7,7 @@
 import torch
 import triton
 import triton.language as tl
+
 from tritonbench.utils.triton_op import (
     BenchmarkOperator,
     BenchmarkOperatorMetrics,