[not4land] Local torchao benchmark

manual_cron.sh

@@ -0,0 +1,13 @@
+target_hour=22
+target_min=00
+while true
+do
+    current_hour=$(date +%H)
+    current_min=$(date +%M)
+    if [ $current_hour -eq $target_hour ] && [ $current_min -eq $target_min ] ; then
+        echo "Cron job started at $(date)"
+        sh cron_script.sh > local_cron_log 2>local_cron_err
+        echo "Cron job executed at $(date)"
+    fi
+    sleep 60
+done

requirements.txt

@@ -9,7 +9,7 @@ pytest
 pytest-benchmark
 requests
 tabulate
-git+https://github.com/huggingface/pytorch-image-models.git@730b907
+# git+https://github.com/huggingface/pytorch-image-models.git@730b907
 # this version of transformers is required by linger-kernel
 # https://github.com/linkedin/Liger-Kernel/blob/main/pyproject.toml#L23
 transformers==4.44.2

upload_to_s3.py

@@ -0,0 +1,57 @@
+import os
+import io
+import json
+from functools import lru_cache
+import boto3
+from typing import Any
+import gzip
+
+@lru_cache
+def get_s3_resource() -> Any:
+    return boto3.resource("s3")
+
+def upload_to_s3(
+    bucket_name: str,
+    key: str,
+    json_path: str,
+) -> None:
+    print(f"Writing {json_path} documents to S3")
+    data = []
+    with open(f"{os.path.splitext(json_path)[0]}.json", "r") as f:
+        for l in f.readlines():
+            data.append(json.loads(l))
+
+    body = io.StringIO()
+    for benchmark_entry in data:
+        json.dump(benchmark_entry, body)
+        body.write("\n")
+
+    try:
+        get_s3_resource().Object(
+            f"{bucket_name}",
+            f"{key}",
+        ).put(
+            Body=body.getvalue(),
+            ContentType="application/json",
+        )
+    except e:
+        print("fail to upload to s3:", e)
+        return
+    print("Done!")
+
+if __name__ == "__main__":
+    import argparse
+    import datetime
+    parser = argparse.ArgumentParser(description="Upload benchmark result json file to clickhouse")
+    parser.add_argument("--json-path", type=str, help="json file path to upload to click house", required=True)
+    args = parser.parse_args()
+    today = datetime.date.today()
+    today = datetime.datetime.combine(today, datetime.time.min)
+    today_timestamp = str(int(today.timestamp()))
+    print("Today timestamp:", today_timestamp)
+    import subprocess
+    # Execute the command and capture the output
+    output = subprocess.check_output(['hostname', '-s'])
+    # Decode the output from bytes to string
+    hostname = output.decode('utf-8').strip()
+    upload_to_s3("ossci-benchmarks", f"v3/pytorch/ao/{hostname}/torchbenchmark-torchbench-" + today_timestamp + ".json", args.json_path)

userbenchmark/dynamo/dynamobench/common.py

@@ -62,6 +62,7 @@
     same,
 )
 from torch._logging.scribe import open_source_signpost
+from userbenchmark.dynamo.dynamobench.utils import benchmark_and_write_json_result
 
 
 try:
@@ -555,8 +556,17 @@ def output_signpost(data, args, suite, error=None):
     )
 
 
-def nothing(f):
-    return f
+def nothing(model_iter_fn):
+    def _apply(module: torch.nn.Module, example_inputs: Any):
+        if isinstance(example_inputs, dict):
+            args = ()
+            kwargs = example_inputs
+        else:
+            args = example_inputs
+            kwargs = {}
+        benchmark_and_write_json_result(module, args, kwargs, "noquant", "cuda", compile=False)
+        model_iter_fn(module, example_inputs)
+    return _apply
 
 
 @functools.lru_cache(None)
@@ -4147,8 +4157,9 @@ def get_example_inputs(self):
             "int8dynamic",
             "int8weightonly",
             "int4weightonly",
-            "autoquant",
             "noquant",
+            "autoquant",
+            "autoquant-all",
         ],
         default=None,
         help="Measure speedup of torchao quantization with TorchInductor baseline",

userbenchmark/dynamo/dynamobench/torchao_backend.py

@@ -1,7 +1,7 @@
 from typing import Any, Callable
 
 import torch
-
+from userbenchmark.dynamo.dynamobench.utils import benchmark_and_write_json_result
 
 def setup_baseline():
     from torchao.quantization.utils import recommended_inductor_config_setter
@@ -20,10 +20,21 @@ def torchao_optimize_ctx(quantization: str):
         quantize_,
     )
     from torchao.utils import unwrap_tensor_subclass
+    import torchao
 
     def inner(model_iter_fn: Callable):
         def _torchao_apply(module: torch.nn.Module, example_inputs: Any):
             if getattr(module, "_quantized", None) is None:
+                if quantization == "noquant":
+                    if isinstance(example_inputs, dict):
+                        args = ()
+                        kwargs = example_inputs
+                    else:
+                        args = example_inputs
+                        kwargs = {}
+
+                    benchmark_and_write_json_result(module, args, kwargs, "noquant", "cuda")
+
                 if quantization == "int8dynamic":
                     quantize_(
                         module,
@@ -34,7 +45,30 @@ def _torchao_apply(module: torch.nn.Module, example_inputs: Any):
                     quantize_(module, int8_weight_only(), set_inductor_config=False)
                 elif quantization == "int4weightonly":
                     quantize_(module, int4_weight_only(), set_inductor_config=False)
-                if quantization == "autoquant":
+                if quantization == "autoquant-all":
+                    autoquant(module, error_on_unseen=False, set_inductor_config=False, qtensor_class_list=torchao.quantization.ALL_AUTOQUANT_CLASS_LIST)
+                    if isinstance(example_inputs, dict):
+                        module(**example_inputs)
+                    else:
+                        module(*example_inputs)
+                    from torchao.quantization.autoquant import AUTOQUANT_CACHE
+
+                    if len(AUTOQUANT_CACHE) == 0:
+                        raise Exception(  # noqa: TRY002`
+                            "NotAutoquantizable"
+                            f"Found no autoquantizable layers in model {type(module)}, stopping autoquantized run"
+                        )
+
+                    if isinstance(example_inputs, dict):
+                        args = ()
+                        kwargs = example_inputs
+                    else:
+                        args = example_inputs
+                        kwargs = {}
+
+                    torchao.quantization.utils.recommended_inductor_config_setter()
+                    benchmark_and_write_json_result(module, args, kwargs, quantization, "cuda")
+                elif quantization == "autoquant":
                     autoquant(module, error_on_unseen=False, set_inductor_config=False)
                     if isinstance(example_inputs, dict):
                         module(**example_inputs)
@@ -47,6 +81,16 @@ def _torchao_apply(module: torch.nn.Module, example_inputs: Any):
                             "NotAutoquantizable"
                             f"Found no autoquantizable layers in model {type(module)}, stopping autoquantized run"
                         )
+
+                    if isinstance(example_inputs, dict):
+                        args = ()
+                        kwargs = example_inputs
+                    else:
+                        args = example_inputs
+                        kwargs = {}
+
+                    torchao.quantization.utils.recommended_inductor_config_setter()
+                    benchmark_and_write_json_result(module, args, kwargs, quantization, "cuda")
                 else:
                     unwrap_tensor_subclass(module)
                 setattr(module, "_quantized", True)  # noqa: B010

userbenchmark/dynamo/dynamobench/utils.py

@@ -0,0 +1,86 @@
+import json
+import torch
+import platform
+import os
+import time
+import datetime
+import hashlib
+
+def get_arch_name() -> str:
+    if torch.cuda.is_available():
+        return torch.cuda.get_device_name()
+    else:
+        # This returns x86_64 or arm64 (for aarch64)
+        return platform.machine()
+
+
+def write_json_result(output_json_path, headers, row):
+    """
+    Write the result into JSON format, so that it can be uploaded to the benchmark database
+    to be displayed on OSS dashboard. The JSON format is defined at
+    https://github.com/pytorch/pytorch/wiki/How-to-integrate-with-PyTorch-OSS-benchmark-database
+    """
+    mapping_headers = {headers[i]: v for i, v in enumerate(row)}
+    today = datetime.date.today()
+    sha_hash = hashlib.sha256(str(today).encode("utf-8")).hexdigest()
+    first_second = datetime.datetime.combine(today, datetime.time.min)
+    workflow_id = int(first_second.timestamp())
+    job_id = workflow_id + 1
+    record = {
+        "timestamp": int(time.time()),
+        "schema_version": "v3",
+        "name": "devvm local benchmark",
+        "repo": "pytorch/ao",
+        "head_branch": "main",
+        "head_sha": sha_hash,
+        "workflow_id": workflow_id,
+        "run_attempt": 1,
+        "job_id": job_id,
+        "benchmark": {
+            "name": "TorchAO benchmark",
+            "mode": "inference",
+            "dtype": mapping_headers["dtype"],
+            "extra_info": {
+                "device": mapping_headers["device"],
+                "arch": mapping_headers["arch"],
+                "min_sqnr": None,
+                "compile": mapping_headers["compile"],
+            },
+        },
+        "model": {
+            "name": mapping_headers["name"],
+            "type": "model",
+            # TODO: make this configurable
+            "origins": ["torchbench"],
+        },
+        "metric": {
+            "name": mapping_headers["metric"],
+            "benchmark_values": [mapping_headers["actual"]],
+            "target_value": mapping_headers["target"],
+        },
+    }
+
+    with open(f"{os.path.splitext(output_json_path)[0]}.json", "a") as f:
+        print(json.dumps(record), file=f)
+
+def benchmark_and_write_json_result(model, args, kwargs, quantization, device, compile=True):
+    print(quantization + " run")
+    from torchao.utils import benchmark_model, profiler_runner
+    if compile:
+        model = torch.compile(model, mode="max-autotune")
+    benchmark_model(model, 20, args, kwargs)
+    elapsed_time = benchmark_model(model, 100, args, kwargs)
+    print("elapsed_time: ", elapsed_time, " milliseconds")
+
+    if hasattr(model, "_orig_mod"):
+        name = model._orig_mod.__class__.__name__
+    else:
+        # eager
+        name = model.__class__.__name__
+
+    headers = ["name", "dtype", "compile", "device", "arch", "metric", "actual", "target"]
+    arch = get_arch_name()
+    dtype = quantization
+    performance_result = [name, dtype, compile, device, arch, "time_ms(avg)", elapsed_time, None]
+    _OUTPUT_JSON_PATH = "benchmark_results"
+    write_json_result(_OUTPUT_JSON_PATH, headers, performance_result)

userbenchmark/group_bench/configs/torch_ao.yaml

@@ -10,7 +10,5 @@ metrics:
 test_group:
   test_batch_size_default:
     subgroup:
-      - extra_args:
-      - extra_args: --quantization int8dynamic
-      - extra_args: --quantization int8weightonly
-      - extra_args: --quantization int4weightonly
+      - extra_args: --quantization noquant
+      - extra_args: --quantization autoquant

userbenchmark/torchao/run.py

@@ -12,18 +12,18 @@
 
 
 def _get_ci_args(
-    backend: str, modelset: str, dtype, mode: str, device: str, experiment: str
+    quantization: str, modelset: str, dtype, mode: str, device: str, experiment: str
 ) -> List[List[str]]:
     if modelset == "timm":
         modelset_full_name = "timm_models"
     else:
         modelset_full_name = modelset
-    output_file_name = f"torchao_{backend}_{modelset_full_name}_{dtype}_{mode}_{device}_{experiment}.csv"
+    output_file_name = f"torchao_{quantization}_{modelset_full_name}_{dtype}_{mode}_{device}_{experiment}.csv"
     ci_args = [
         "--progress",
         f"--{modelset}",
         "--quantization",
-        f"{backend}",
+        f"{quantization}",
         f"--{mode}",
         f"--{dtype}",
         f"--{experiment}",
@@ -32,16 +32,33 @@ def _get_ci_args(
     ]
     return ci_args
 
+def _get_eager_baseline_args(quantization: str, modelset: str, dtype, mode: str, device: str, experiment: str):
+    if modelset == "timm":
+        modelset_full_name = "timm_models"
+    else:
+        modelset_full_name = modelset
+    output_file_name = f"torchao_{quantization}_{modelset_full_name}_{dtype}_{mode}_{device}_{experiment}_eager.csv"
+    ci_args = [
+        "--progress",
+        f"--{modelset}",
+        f"--{mode}",
+        f"--{dtype}",
+        f"--{experiment}",
+        "--nothing",
+        "--output",
+        f"{str(OUTPUT_DIR.joinpath(output_file_name).resolve())}",
+    ]
+    return ci_args
 
 def _get_full_ci_args(modelset: str) -> List[List[str]]:
-    backends = ["autoquant", "int8dynamic", "int8weightonly", "noquant"]
+    quantizations = ["autoquant-all", "autoquant", "noquant"]
     modelset = [modelset]
     dtype = ["bfloat16"]
     mode = ["inference"]
     device = ["cuda"]
-    experiment = ["performance", "accuracy"]
-    cfgs = itertools.product(*[backends, modelset, dtype, mode, device, experiment])
-    return [_get_ci_args(*cfg) for cfg in cfgs]
+    experiment = ["performance"]
+    cfgs = itertools.product(*[quantizations, modelset, dtype, mode, device, experiment])
+    return [_get_ci_args(*cfg) for cfg in cfgs] + [_get_eager_baseline_args("noquant", modelset[0], dtype[0], mode[0], device[0], experiment[0])]
 
 
 def _get_output(pt2_args):