add includes for gptq, eval.py

Author: Michael Gschwind

eval.py

@@ -0,0 +1,270 @@
+# 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 sys
+import time
+from pathlib import Path
+from typing import Optional
+
+import torch
+import torch._dynamo.config
+import torch._inductor.config
+
+torch._dynamo.config.automatic_dynamic_shapes = True
+torch._inductor.config.triton.unique_kernel_names = True
+torch._inductor.config.epilogue_fusion = False
+torch._inductor.config.triton.cudagraphs = True
+torch._dynamo.config.cache_size_limit = 100000
+
+from sentencepiece import SentencePieceProcessor
+
+from model import Transformer
+
+try:
+    import lm_eval
+    lm_eval_available = True
+except:
+    lm_eval_available = False
+
+from generate import _load_model, encode_tokens, model_forward
+
+if lm_eval_available:
+    try: # lm_eval version 0.4
+        from lm_eval.models.huggingface import HFLM as eval_wrapper
+        from lm_eval.tasks import get_task_dict
+        from lm_eval.evaluator import evaluate
+    except: #lm_eval version 0.3
+        from lm_eval import base
+        from lm_eval import tasks
+        from lm_eval import evaluator
+        eval_wrapper=base.BaseLM
+        get_task_dict=tasks.get_task_dict
+        evaluate=evaluator.evaluate
+
+
+def setup_cache_padded_seq_input_pos_max_seq_length_for_prefill(
+    model: Transformer,
+    prompt: torch.Tensor,
+    max_new_tokens: int,
+    max_seq_length: Optional[int] = None,
+):
+    """
+    Sets up model cache and does some bookkeeping calculations for prompt, input_pos and max_seq_length
+    that are needed for prefill or model_forward
+
+    Args:
+        model (LLaMA): The model whose cache gets set up
+        prompt (torch.Tensor): Tensor of shape (T) with indices of the prompt sequence.
+        max_new_tokens (int): The desired maximum number of new tokens that can be generated.
+        max_seq_length (Optional[int], optional): The maximum sequence length allowed.
+
+    Returns:
+        seq (torch.Tensor): prompt but padded with zeros to size max_seq_length
+        input_pos (torch.Tensor): tensor of integers in increasing order
+        max_seq_length (int): The maximum sequence length allowed, updated based on other numbers
+    """
+    T = prompt.size(0)
+    T_new = T + max_new_tokens
+    if max_seq_length is None:
+        max_seq_length = min(T_new, model.config.block_size)
+
+    device, dtype = prompt.device, prompt.dtype
+    # create an empty tensor of the expected final shape and fill in the current tokens
+    empty = torch.empty(T_new, dtype=dtype, device=device)
+    empty[:T] = prompt
+    seq = empty
+    input_pos = torch.arange(0, T, device=device)
+
+    with torch.device(device):
+        model.setup_caches(max_batch_size=1, max_seq_length=max_seq_length)
+
+    return seq, input_pos, max_seq_length
+
+class GPTFastEvalWrapper(eval_wrapper):
+    """
+    A wrapper class for GPTFast, providing integration with the lm-evaluation-harness library.
+    """
+    def __init__(
+        self,
+        model: Transformer,
+        tokenizer,
+        max_seq_length: Optional[int]=None,
+    ):
+        super().__init__()
+        self._model = model
+        self._tokenizer = tokenizer
+        self._device = torch.device('cuda')
+        self._max_seq_length = 2048 if max_seq_length is None else max_seq_length
+
+    @property
+    def eot_token_id(self):
+        return self._tokenizer.eos_id()
+
+    @property
+    def max_length(self):
+        return self._max_seq_length
+
+    @property
+    def max_gen_toks(self):
+        return 50
+
+    @property
+    def batch_size(self):
+        return 1
+
+    @property
+    def device(self):
+        return self._device
+
+    def tok_encode(self, string: str, **kwargs):
+        encoded = encode_tokens(self._tokenizer,
+            string, bos=True, device=self._device)
+        # encoded is a pytorch tensor, but some internal logic in the
+        # eval harness expects it to be a list instead
+        # TODO: verify this for multi-batch as well
+        encoded = encoded.tolist()
+        return encoded
+
+    def tok_decode(self, tokens):
+        decoded = self._tokenizer.decode(tokens)
+        return decoded
+
+    def _model_call(self, inps):
+        # TODO: make batches work
+        inps = inps.squeeze(0)
+
+        max_new_tokens = 1
+        seq, input_pos, max_seq_length = \
+            setup_cache_padded_seq_input_pos_max_seq_length_for_prefill(
+                self._model,
+                inps,
+                max_new_tokens,
+                self.max_length,
+            )
+        x = seq.index_select(0, input_pos).view(1, -1)
+        logits = model_forward(self._model, x, input_pos)
+        return logits
+
+    def _model_generate(self, context, max_length, eos_token_id):
+        raise Exception('unimplemented')
+
+
+@torch.no_grad()
+def eval(
+    model: Transformer,
+    tokenizer,
+    tasks: list = ["hellaswag"],
+    limit: Optional[int] = None,
+    max_seq_length: Optional[int] = None,
+) -> dict:
+    """
+    Evaluates a language model on a specified task using the lm-evaluation-harness library.
+
+    Args:
+        model (Transformer): The pre-trained language model to evaluate.
+        tokenizer: The tokenizer to use for encoding/decoding text.
+        task (str): The name of the evaluation task to perform.
+        limit (Optional[int]): The maximum number of samples to evaluate (None for all available).
+        max_seq_length (Optional[int]): The maximum sequence length allowed for input text.
+
+    Returns:
+        eval_results (dict): A dictionary of evaluation results for the specified task(s).
+    """
+    model_eval_wrapper = GPTFastEvalWrapper(
+        model,
+        tokenizer,
+        max_seq_length,
+    )
+
+    try:
+        lm_eval.tasks.initialize_tasks()
+    except:
+        pass
+
+    if 'hendrycks_test' in tasks:
+        tasks.remove('hendrycks_test')
+        tasks += [x for x in lm_eval.tasks.hendrycks_test.create_all_tasks().keys()]
+    task_dict = get_task_dict(tasks)
+
+    eval_results = evaluate(
+        model_eval_wrapper,
+        task_dict,
+        limit=limit,
+    )
+    return eval_results
+
+
+def main(
+    checkpoint_path: Path = Path("checkpoints/meta-llama/Llama-2-7b-chat-hf/lit_model.pth"),
+    compile: bool = False,
+    tasks: list = ["hellaswag"],
+    limit: Optional[int] = None,
+    max_seq_length: Optional[int] = None,
+) -> None:
+    """Evaluates model on a task from the `lm-evaluation-harness` library.
+
+    Args:
+        checkpoint_path (Path): The path to the model checkpoint file to load.
+        compile (bool): Whether or not to compile the model for optimization.
+        task (Optional[str]): The name of the evaluation task or a list of tasks to perform.
+        limit (Optional[int]): The maximum number of samples to evaluate (None for all available).
+        max_seq_length (Optional[int]): The maximum sequence length allowed for input text.
+
+    """
+
+    assert checkpoint_path.is_file(), checkpoint_path
+
+    tokenizer_path = checkpoint_path.parent / "tokenizer.model"
+    assert tokenizer_path.is_file(), tokenizer_path
+
+    device = 'cuda'
+    precision = torch.bfloat16
+
+    print("Loading model ...")
+    t0 = time.time()
+    model = _load_model(checkpoint_path, device, precision, False)
+
+    torch.cuda.synchronize()
+    print(f"Time to load model: {time.time() - t0:.02f} seconds.")
+
+    model.eval()
+
+    tokenizer = SentencePieceProcessor(model_file=str(tokenizer_path))
+
+    torch.manual_seed(1234)
+
+    if compile:
+        global model_forward
+        model_forward = torch.compile(model_forward,  mode="reduce-overhead", dynamic=True, fullgraph=True)
+        torch._inductor.config.coordinate_descent_tuning = True
+
+    t1 = time.time()
+    result = eval(
+        model,
+        tokenizer,
+        tasks,
+        limit,
+        max_seq_length,
+    )
+    print(f"Time to run eval: {time.time() - t1:.02f} seconds.")
+    print(f"For model {checkpoint_path}")
+    for task, res in result["results"].items():
+        print(f"{task}: {res}")
+
+
+if __name__ == '__main__':
+    import argparse
+    parser = argparse.ArgumentParser(description='Your CLI description.')
+
+    parser.add_argument('--checkpoint_path', type=Path, default=Path("checkpoints/meta-llama/Llama-2-7b-chat-hf/lit_model.pth"), help='Model checkpoint path.')
+    parser.add_argument('--compile', action='store_true', help='Whether to compile the model.')
+    parser.add_argument('--tasks', nargs='+', type=str, default=["hellaswag"], help='list of lm-eluther tasks to evaluate usage: --tasks task1 task2')
+    parser.add_argument('--limit', type=int, default=None, help='number of samples to evalulate')
+    parser.add_argument('--max_seq_length', type=int, default=None, help='maximum length sequence to evaluate')
+
+    args = parser.parse_args()
+    main(
+        Path(args.checkpoint_path), args.compile, args.tasks, args.limit, args.max_seq_length,
+    )

quantize.py

@@ -13,6 +13,13 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
+
+try:
+    from GPTQ import GenericGPTQRunner, InputRecorder
+    from eval import get_task_dict, evaluate, lm_eval
+except:
+    pass
+
 # from .ops.quantized_ops import *  # noqa
 #
 # from torchao.quantization.quant_primitives import (
@@ -68,7 +75,7 @@ def quantize_model(model: nn.Module, quantize_options):
             ).quantized_model()
         elif quantizer == "linear:gptq":
             linears_quantized = True
-            model = Int8DynActInt4WeightQuantHandler(
+            model = WeightOnlyInt4GPTQQuantHandler(
                 model,
                 **q_kwargs
             ).quantized_model()