buckify eval_llama (#5437)

Summary:
Pull Request resolved: #5437

This PR buckify `eval_llama`. This is useful when we need to run eval using buck.

Reviewed By: mergennachin

Differential Revision: D62897016

fbshipit-source-id: 59cc64eaa3b29f707b9aa7d3ac2568a16c2743c9

Author: Lunwen He

examples/models/llama2/TARGETS

@@ -104,5 +104,45 @@ runtime.python_library(
         "//executorch/util:python_profiler",
         "fbsource//third-party/pypi/coremltools:coremltools",
         "fbsource//third-party/pypi/sentencepiece:sentencepiece",
+        "//pytorch/ao:torchao",
+    ],
+)
+
+runtime.python_binary(
+    name = "eval_llama",
+    main_function = "executorch.examples.models.llama2.eval_llama.main",
+    preload_deps = [
+        "//executorch/extension/llm/custom_ops:custom_ops_aot_lib",
+        "//executorch/kernels/quantized:aot_lib",
+    ],
+    deps = [
+        ":eval_library",
+        "//caffe2:torch",
+    ],
+)
+
+runtime.python_library(
+    name = "eval_library",
+    srcs = [
+        "eval_llama.py",
+        "eval_llama_lib.py",
+        "evaluate/eager_eval.py",
+    ],
+    _is_external_target = True,
+    base_module = "executorch.examples.models.llama2",
+    visibility = [
+        "//bento/...",
+        "//bento_kernels/...",
+        "//executorch/examples/...",
+        "@EXECUTORCH_CLIENTS",
+    ],
+    deps = [
+        "fbsource//third-party/pypi/lm-eval:lm-eval",
+        "fbsource//third-party/pypi/tiktoken:tiktoken",
+        ":export_library",
+        "//executorch/examples/models/llama2/tokenizer:tiktoken_py",
+        "//executorch/extension/llm/export:export_lib",
+        "//executorch/extension/llm/tokenizer:tokenizer_py_lib",
+        "//executorch/extension/pybindings:portable_lib",
     ],
 )

examples/models/llama2/eval_llama.py

@@ -24,7 +24,7 @@ def main() -> None:
     args = parser.parse_args()
     # Overrides this arg, because evaluation requires full logits.
     args.generate_full_logits = True
-    eval_llama(modelname, args)
+    eval_llama(modelname, args)  # pyre-ignore
 
 
 if __name__ == "__main__":

examples/models/llama2/eval_llama_lib.py

@@ -10,19 +10,20 @@
 from typing import Optional, Union
 
 import torch
-from executorch.examples.models.llama2.evaluate import EagerEvalWrapper, evaluate_model
 from executorch.examples.models.llama2.export_llama_lib import (
     get_quantizer_and_quant_params,
 )
 from executorch.examples.models.llama2.tokenizer.tiktoken import Tokenizer as Tiktoken
 
-from executorch.extension.llm.export import LLMEdgeManager
+from executorch.extension.llm.export.builder import LLMEdgeManager
 from executorch.extension.llm.tokenizer.tokenizer import (
     Tokenizer as SentencePieceTokenizer,
 )
 from executorch.extension.llm.tokenizer.utils import get_tokenizer
 from lm_eval.api.model import LM
 
+from .evaluate.eager_eval import EagerEvalWrapper, evaluate_model
+
 from .export_llama_lib import (
     _prepare_for_llama_export,
     build_args_parser as _build_args_parser,
@@ -91,7 +92,7 @@ def __init__(
         tokenizer: Union[SentencePieceTokenizer, Tiktoken],
         max_seq_length: Optional[int] = None,
     ):
-        super().__init__(None, tokenizer, max_seq_length)
+        super().__init__(None, tokenizer, max_seq_length)  # pyre-ignore
         self._model = model  # Expects model to be path to a .pte file
 
         from executorch.extension.pybindings.portable_lib import _load_for_executorch
@@ -106,7 +107,7 @@ def __init__(
         from executorch.kernels import quantized  # noqa
 
         self._et_model = _load_for_executorch(self._model)
-        self._use_kv_cache = self._et_model.run_method("use_kv_cache")[0]
+        self._use_kv_cache = self._et_model.run_method("use_kv_cache")[0]  # pyre-ignore
 
     def _model_call(self, inps):
         # Given inps (tokens), return the logits from a single forward call
@@ -140,7 +141,7 @@ def __init__(
         tokenizer_bin: str,
         max_seq_length: Optional[int] = None,
     ):
-        super().__init__(None, tokenizer, max_seq_length)
+        super().__init__(None, tokenizer, max_seq_length)  # pyre-ignore
         self._model = model
         self._tokenizer_bin = tokenizer_bin
 
@@ -165,17 +166,17 @@ def gen_eval_wrapper(
     Returns:
         eval_wrapper (LM): A wrapper interface for the lm-evaluation-harness library.
     """
-    tokenizer = get_tokenizer(args.tokenizer_path)
+    tokenizer = get_tokenizer(args.tokenizer_path)  # pyre-ignore
 
     # ExecuTorch Binary Evaluation
-    if (model := args.pte) is not None:
-        if (tokenizer_bin := args.tokenizer_bin) is not None:
+    if (model := args.pte) is not None:  # pyre-ignore
+        if (tokenizer_bin := args.tokenizer_bin) is not None:  # pyre-ignore
             # ETRunnerEvalWrapper: Create a wrapper around an ExecuTorch model, evaluated at runtime
             return ETRunnerEvalWrapper(
                 model=model,
                 tokenizer=tokenizer,
                 tokenizer_bin=tokenizer_bin,
-                max_seq_length=args.max_seq_length,
+                max_seq_length=args.max_seq_length,  # pyre-ignore
             )
 
         # ETPybindEvalWrapper: Create a wrapper around an ExecuTorch model, evaluated with pybindings
@@ -194,16 +195,16 @@ def gen_eval_wrapper(
     if len(quantizers) != 0:
         manager = manager.capture_pre_autograd_graph().pt2e_quantize(quantizers)
         model = (
-            manager.pre_autograd_graph_module.to(device="cuda")
+            manager.pre_autograd_graph_module.to(device="cuda")  # pyre-ignore
             if torch.cuda.is_available()
             else manager.pre_autograd_graph_module.to(device="cpu")
         )
         return GraphModuleEvalWrapper(
             model=model,
             tokenizer=tokenizer,
             max_seq_length=args.max_seq_length,
-            use_kv_cache=args.use_kv_cache,
-            enable_dynamic_shape=args.enable_dynamic_shape,
+            use_kv_cache=args.use_kv_cache,  # pyre-ignore
+            enable_dynamic_shape=args.enable_dynamic_shape,  # pyre-ignore
         )
     else:
         # TODO: use manager.pre_autograd_graph_module for the eval to remove the if-else branch
@@ -221,7 +222,7 @@ def gen_eval_wrapper(
         # that is not available in this eval_llama. We save the checkpoint
         # here for consistency with eval_llama. The accuracy results we
         # get from eval_llama can be used as a reference to other evaluations.
-        if args.output_eager_checkpoint_file is not None:
+        if args.output_eager_checkpoint_file is not None:  # pyre-ignore
             torch.save(model, args.output_eager_checkpoint_file)
 
         return EagerEvalWrapper(
@@ -282,8 +283,8 @@ def eval_llama(
     # Evaluate the model
     eval_results = evaluate_model(
         eval_wrapper,
-        args.tasks,
-        args.limit,
+        args.tasks,  # pyre-ignore
+        args.limit,  # pyre-ignore
     )
 
     for task, res in eval_results["results"].items():

examples/models/llama2/evaluate/eager_eval.py

@@ -62,7 +62,7 @@ def batch_size(self):
     def device(self):
         return self._device
 
-    def tok_encode(self, string: str, **kwargs):
+    def tok_encode(self, string: str, **kwargs):  # pyre-ignore
         tokens = self._tokenizer.encode(string, bos=True, eos=False)
         encoded = torch.tensor(tokens, dtype=torch.int, device=self.device)
         # encoded is a pytorch tensor, but some internal logic in the
@@ -111,7 +111,9 @@ def evaluate_model(
 
     if "hendrycks_test" in tasks:
         tasks.remove("hendrycks_test")
-        tasks += list(lm_eval.tasks.hendrycks_test.create_all_tasks().keys())
+        tasks += list(
+            lm_eval.tasks.hendrycks_test.create_all_tasks().keys()  # pyre-ignore
+        )
     task_dict = get_task_dict(tasks)
 
     eval_results = evaluate(

extension/llm/export/builder.py

@@ -209,7 +209,9 @@ def pt2e_calibrate(
             from executorch.examples.models.llama2.eval_llama_lib import (
                 GraphModuleEvalWrapper,
             )
-            from executorch.examples.models.llama2.evaluate import evaluate_model
+            from executorch.examples.models.llama2.evaluate import (  # pyre-ignore[21]
+                evaluate_model,
+            )
         except ImportError:
             raise ImportError(
                 "Please install the llm eval dependency via examples/models/llama2/install_requirements.sh"