eval script for llama (#374)

* eval script for llama

Summary: previously we were only doing this in the tests but now we have
an eval script to along with generate.py

Test Plan:

python eval.py -q "int4wo-64-gptq"

Reviewers:

Subscribers:

Tasks:

Tags:

* new api

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

torchao/_models/llama/eval.py

@@ -0,0 +1,134 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+import torch
+import torchao
+from pathlib import Path
+from typing import List, Optional
+from generate import (
+    _load_model,
+    device_sync,
+
+)
+from torchao.quantization.quant_api import (
+    quantize, int4wo, int8wo, int8da_int8w
+
+)
+from torchao._models._eval import TransformerEvalWrapper, InputRecorder
+
+from tokenizer import get_tokenizer
+import time
+from torchao.quantization.GPTQ import Int4WeightOnlyGPTQQuantizer
+from model import prepare_inputs_for_model
+
+torch._inductor.config.fx_graph_cache = True
+torch._inductor.config.force_fuse_int_mm_with_mul = True
+
+def run_evaluation(
+    checkpoint_path: Path,
+    tasks: List[str],
+    limit: Optional[int] = None,
+    device = "cuda",
+    precision = torch.bfloat16,
+    quantization: Optional[str] = None,
+    compile=False,
+    max_length=None,
+    calibration_tasks: Optional[List[str]] = None,
+    calibration_limit: Optional[int] = None,
+    calibration_seq_length: Optional[int] = None,
+    pad_calibration_inputs: Optional[bool] = False,
+):
+    """Runs the evaluation of a model using LM Eval."""
+    assert checkpoint_path.is_file(), checkpoint_path
+    tokenizer_path = checkpoint_path.parent / "tokenizer.model"
+    assert tokenizer_path.is_file(), str(tokenizer_path)
+    # Load Model and Tokenizer
+
+    print("Loading model ...")
+    t0 = time.time()
+    model = _load_model(checkpoint_path, "cpu", precision)
+
+    if max_length is None:
+        max_length = model.config.block_size
+
+    device_sync(device=device) # MKG
+    print(f"Time to load model: {time.time() - t0:.02f} seconds")
+    tokenizer = get_tokenizer(tokenizer_path, checkpoint_path)
+
+
+    if quantization:
+        if "int8wo" in quantization:
+            quantize(model, int8wo())
+        if "int8dq" in quantization:
+            quantize(model, int8da_int8w())
+        if "int4wo" in quantization and not "gptq" in quantization:
+            groupsize=int(quantization.split("-")[-1])
+            assert groupsize in [32,64,128,256], f"int4wo groupsize needs to be one of [32,64,128,256] but got {groupsize}"
+            quantize(model, int4wo(groupsize=groupsize))
+        if "int4wo" in quantization and "gptq" in quantization:
+            groupsize=int(quantization.split("-")[-2])
+            assert groupsize in [32,64,128,256], f"int4wo groupsize needs to be one of [32,64,128,256] but got {groupsize}"
+
+            inputs = InputRecorder(
+                tokenizer,
+                calibration_seq_length,
+                prepare_inputs_for_model,
+                pad_calibration_inputs,
+                model.config.vocab_size,
+                device="cpu"
+            ).record_inputs(
+                calibration_tasks,
+                calibration_limit,
+            ).get_inputs()
+
+            quantizer = Int4WeightOnlyGPTQQuantizer(groupsize=groupsize, precision=precision)
+            model.setup_caches(max_batch_size=1, max_seq_length=calibration_seq_length)
+            model = quantizer.quantize(model, inputs).to(device)
+
+    if compile:
+        model = torch.compile(model, mode="max-autotune", fullgraph=True)
+    with torch.no_grad():
+        TransformerEvalWrapper(
+            model=model.to(device), 
+            tokenizer=tokenizer, 
+            max_seq_length=max_length,
+            input_prep_func=prepare_inputs_for_model,
+            device=device,
+        ).run_eval(
+            tasks=tasks,
+            limit=limit,
+        )
+
+if __name__ == '__main__':
+    import argparse
+    parser = argparse.ArgumentParser(description='Run HF Model Evaluation')
+    parser.add_argument('--checkpoint_path', type=Path, default=Path("../../../checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth"), help='Model checkpoint path.')
+    parser.add_argument('--tasks', nargs='+', type=str, default=["wikitext"], help='List of lm-eluther tasks to evaluate usage: --tasks task1 task2')
+    parser.add_argument('--limit', type=int, default=None, help='Number of eval samples to evaluate')
+    parser.add_argument('--precision', type=lambda x: getattr(torch, x.split(".")[-1]), default=torch.bfloat16, help='dtype precision to use')
+    parser.add_argument('--device', type=str, default="cuda", help='Device to use for evaluation')
+    parser.add_argument("-q", "--quantization", type=str, help="Which quantization techniques to apply: int8dq, int8wo, int4wo-<groupsize>, int4wo-<groupsize>-gptq")
+    parser.add_argument('--compile', action='store_true', help='Whether to compile the model.')
+    parser.add_argument('--max_length', type=int, default=None, help='Length of text to process at one time')
+    parser.add_argument('--calibration_tasks', type=str, nargs='+', default=['wikitext'], help='tasks to do gptq calibration on, if doing gptq')
+    parser.add_argument('--calibration_limit', type=int, default=1000, help='number of samples to use for gptq calibration')
+    parser.add_argument('--calibration_seq_length', type=int, default=100, help='length of sequences to use for gptq calibration')
+    parser.add_argument('--pad_calibration_inputs', type=bool, default=False, help='pads sequences shorter than calibration_seq_length to that length, yielding more calibration inputs but running much slower')
+
+    args = parser.parse_args()
+    run_evaluation(
+        args.checkpoint_path, 
+        args.tasks, 
+        args.limit, 
+        args.device, 
+        args.precision, 
+        args.quantization, 
+        args.compile, 
+        args.max_length, 
+        args.calibration_tasks, 
+        args.calibration_limit, 
+        args.calibration_seq_length, 
+        args.pad_calibration_inputs, 
+    )

torchao/quantization/GPTQ.py

@@ -1018,9 +1018,9 @@ def quantize(
 class Int8DynActInt4WeightGPTQQuantizer(GPTQQuantizer):
     def __init__(
         self,
-        blocksize,
-        percdamp,
-        groupsize,
+        blocksize=128,
+        percdamp=.01,
+        groupsize=64,
         inner_k_tiles=8,
         padding_allowed=True,
         precision=torch.float32,