Fix Llama Benchmarking 8b f16 tests to run on presubmit (#363)

Modify all the changes merged into the sharktank repo to make Llama
Benchmarking test work.

---------

Signed-off-by: aviator19941 <avinash.sharma@amd.com>

.github/workflows/ci-llama.yaml

@@ -8,6 +8,7 @@ name: Llama Benchmarking Tests
 
 on:
   workflow_dispatch:
+  pull_request:
   schedule:
     # Weekdays at 5:00 AM UTC = 10:00 PM PST.
     - cron: "0 5 * * 1-5"
@@ -75,7 +76,7 @@ jobs:
             "numpy<2.0"
 
       - name: Run llama test
-        run: pytest sharktank/tests/models/llama/benchmark_amdgpu_tests.py -v -s --longrun
+        run: pytest sharktank/tests/models/llama/benchmark_amdgpu_test.py -v -s --longrun --iree-hip-target=gfx942
 
       - name: Upload llama executable files
         uses: actions/upload-artifact@v4

sharktank/conftest.py

@@ -201,13 +201,6 @@ def caching(request: FixtureRequest) -> Optional[bool]:
     return set_fixture_from_cli_option(request, "caching")
 
 
-@pytest.fixture(scope="class")
-def iree_hip_target_type(request: FixtureRequest) -> Optional[str]:
-    return set_fixture_from_cli_option(
-        request, "iree_hip_target", "iree_hip_target_type"
-    )
-
-
 @pytest.fixture(scope="class")
 def tensor_parallelism_size(request: FixtureRequest) -> Optional[str]:
     return set_fixture_from_cli_option(

sharktank/sharktank/examples/export_paged_llm_v1.py

@@ -57,7 +57,7 @@ def main():
         "--attention-kernel",
         type=str,
         default="decomposed",
-        choices=["decomposed", "torch_sdpa"],
+        choices=["decomposed", "torch"],
     )
 
     args = cli.parse(parser)

sharktank/sharktank/models/llama/tools/shard_llama.py

@@ -33,12 +33,10 @@ def main():
     dataset = cli.get_input_dataset(args)
 
     if args.output_file is None:
-        print(f"Need file destination for IRPA file")
-        return
+        raise RuntimeError(f"Need file destination for IRPA file")
 
     if args.shard_count < 2:
-        print(f"Expect sharding greater than 1 found {args.shard_count}")
-        return
+        raise RuntimeError(f"Expect sharding greater than 1 found {args.shard_count}")
 
     hp = configs.LlamaHParams.from_gguf_props(dataset.properties)
     llama_config = LlamaModelConfig(hp)

sharktank/sharktank/utils/export_artifacts.py

@@ -5,11 +5,13 @@
 # SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 
 import os
+import sys
 import subprocess
 import logging
 import time
 from pathlib import Path
 from datetime import timedelta
+from typing import List
 
 import iree.compiler as ireec
 
@@ -25,6 +27,7 @@
 class ExportArtifacts:
     def __init__(
         self,
+        *,
         irpa_path: str,
         batch_size: int,
         iree_hip_target: str,
@@ -59,9 +62,44 @@ def wrapper(*args, **kwargs):
 
         return wrapper
 
+    @timeit
+    def shard_irpa_file(
+        self,
+        *,
+        output_file: str,
+    ):
+        shard_irpa_args = [
+            "python3",
+            "-m",
+            "sharktank.models.llama.tools.shard_llama",
+            "--irpa-file",
+            self.irpa_path,
+            "--output-file",
+            output_file,
+            "--shard_count",
+            str(self.tensor_parallelism_size),
+        ]
+
+        cwd = self.sharktank_dir
+        cmd = subprocess.list2cmdline(shard_irpa_args)
+
+        logger.info(f"Sharding irpa file:\n" f"cd {cwd} && {cmd}")
+
+        proc = subprocess.run(cmd, shell=True, capture_output=True, cwd=cwd)
+        if proc.returncode != 0:
+            logger.error(
+                f"Error sharding irpa file with shard_llama.py\n"
+                f"{proc.stdout+proc.stderr}"
+            )
+        else:
+            logger.info(f"Sharded irpa file successfully:\n" f"{proc.stdout}")
+
+        return proc.returncode
+
     @timeit
     def export_to_mlir(
         self,
+        *,
         mlir_path: str,
         json_path: str,
     ):
@@ -78,18 +116,16 @@ def export_to_mlir(
             "--bs",
             str(self.batch_size),
         ]
-        if self.attention_kernel == "decomposed":
+        if self.attention_kernel in ["decomposed", "torch"]:
             export_args.append("--attention-kernel")
             export_args.append(self.attention_kernel)
-        elif self.attention_kernel == "torch_sdpa":
-            raise NotImplementedError("attention_kernel torch_sdpa not implemented yet")
 
         cwd = self.sharktank_dir
         cmd = subprocess.list2cmdline(export_args)
 
         logger.info(f"Exporting mlir:\n" f"cd {cwd} && {cmd}")
 
-        proc = subprocess.run(export_args, capture_output=True, cwd=cwd, text=True)
+        proc = subprocess.run(cmd, shell=True, capture_output=True, cwd=cwd)
         if proc.returncode != 0:
             logger.error(
                 f"Error exporting mlir with export_paged_llm_v1.py\n"
@@ -103,12 +139,14 @@ def export_to_mlir(
     @timeit
     def compile_to_vmfb(
         self,
+        *,
         mlir_path,
         vmfb_path,
+        hal_dump_path,
     ):
         # TODO: Control flag to enable multiple backends
         compile_flags = ["--iree-hip-target=" + self.iree_hip_target]
-
+        compile_flags += [f"--iree-hal-dump-executable-files-to={hal_dump_path}/files"]
         try:
             ireec.compile_file(
                 input_file=mlir_path,
@@ -121,7 +159,43 @@ def compile_to_vmfb(
         else:
             logger.info(f"Compiled to vmfb successfully:\n" f"{vmfb_path}")
 
-    def create_file(self, suffix, prefix):
+    def iree_benchmark_vmfb(
+        self,
+        *,
+        hip_device_id: str,
+        vmfb_name: str,
+        irpa_path: str,
+        args: List[str],
+        cwd: str | Path,
+    ):
+        """Runs a compiled program with the given args using `iree-benchmark-module`.
+        This assumes that the `iree-benchmark-module` command is available (usually via PATH).
+        Args:
+            vmfb_name: Name of the .vmfb file (relative to `cwd`).
+            args: List of arguments to pass to `iree-benchmark-module`.
+            cwd: Working directory to run the command within. (either string or Path works)
+            compile_cmd: Command used to compile the program, for inclusion in error messages.
+        Raises Exception if running fails for some reason.
+        """
+        benchmark_args = [
+            f"ROCR_VISIBLE_DEVICES={hip_device_id}",
+            "iree-benchmark-module",
+            f"--device=hip://{hip_device_id}",
+            "--hip_use_streams=true",
+            "--hip_allow_inline_execution=true",
+            "--device_allocator=caching",
+            f"--module={vmfb_name}",
+            f"--parameters=model={irpa_path}",
+        ]
+        benchmark_args += args
+        cmd = subprocess.list2cmdline(benchmark_args)
+        logging.getLogger().info(f"Launching run command:\n" f"cd {cwd} && {cmd}")
+        proc = subprocess.run(cmd, shell=True, stdout=sys.stdout, cwd=cwd)
+        return_code = proc.returncode
+        if return_code != 0:
+            raise RuntimeError(f"Error running benchmark {cmd} in cwd {cwd}")
+
+    def create_file(self, *, suffix, prefix):
         file_path = Path(prefix).with_suffix(suffix)
         f = open(file_path, "w")
         return file_path