Add general support for system metric measurement (#12)
Browse files- support mmlu (2088911598dc03c566aab4569f805c447276c689)
requirements.txt
CHANGED
@@ -18,7 +18,7 @@ tqdm
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wandb
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transformers>=4.36.0
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tokenizers>=0.15.0
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-
lm_eval[ifeval] @ git+https://github.com/EleutherAI/lm-evaluation-harness.git
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accelerate
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sentencepiece
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langdetect
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wandb
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transformers>=4.36.0
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tokenizers>=0.15.0
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lm_eval[ifeval] @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@0.4.2
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accelerate
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sentencepiece
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langdetect
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src/backend/hflm_with_measurement.py
CHANGED
@@ -68,6 +68,226 @@ class HFLMWithMeasurement(HFLM):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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def _model_generate(self, context, max_length, stop, **generation_kwargs):
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# temperature = 0.0 if not set
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# if do_sample is false and temp==0.0:
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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def _loglikelihood_tokens(
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self,
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requests: List[Tuple[Tuple[str, str], List[int], List[int]]],
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disable_tqdm: bool = False,
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override_bs: int = None,
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) -> List[Tuple[float, bool]]:
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# TODO: implement some kind of efficient-request-middleware that lumps together requests with the same context
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res = []
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def _collate(req: Tuple[Tuple[str, str], List[int], List[int]]):
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"""Defines the key for the sorted method"""
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# the negative sign on len(toks) sorts descending - this has a few advantages:
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# - time estimates will always be over not underestimates, which is more useful for planning
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# - to know the size of a batch when going through the list, you know the first one is always the batch
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# padded context length. this is useful to simplify the batching logic and more importantly to make
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# automatic adaptive batches much much easier to implement
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# - any OOMs will happen right away rather than near the end
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toks = req[1] + req[2]
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return -len(toks), tuple(toks)
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def _lookup_one_token_cont(req: Tuple[Tuple[str, str], List[int], List[int]]):
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"""Defines the key to group and lookup one-token continuations"""
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# Use with group_by="contexts" (optional)"
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# allows for the creation of a lookup, so we can reuse logits in case of one-token continuations.
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# speeds up some multiple-choice tasks proportionally to the number of choices.
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# groups requests by context+continuation[:-1] and infer on one request/group.
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return req[-2] + req[-1][:-1]
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re_ord = Collator(
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requests,
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sort_fn=_collate,
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group_by="contexts"
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if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM
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and self.logits_cache
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else None,
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group_fn=_lookup_one_token_cont,
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)
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# automatic (variable) batch size detection for vectorization
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# pull longest context sample from request
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n_reordered_requests = len(re_ord)
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batch_size = (
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self.batch_size
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if self.batch_size != "auto"
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else override_bs
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if override_bs is not None
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else 0
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)
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batch_fn = (
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self._batch_scheduler
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if self.batch_size == "auto"
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and n_reordered_requests > 0
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and not override_bs
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else None
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)
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chunks = re_ord.get_batched(n=batch_size, batch_fn=batch_fn)
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pbar = tqdm(
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total=len(requests),
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disable=(disable_tqdm or (self.rank != 0)),
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desc="Running loglikelihood requests",
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)
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for chunk in chunks:
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inps = []
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cont_toks_list = []
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inplens = []
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conts = []
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encoder_attns = []
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padding_len_inp = None
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padding_len_cont = None
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# because vectorizing is annoying, we first convert each (context, continuation) pair to padded
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# tensors, then we pack them together into a batch, call the model, and then pick it all apart
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# again because vectorizing is annoying
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for _, context_enc, continuation_enc in chunk:
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# sanity check
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assert len(context_enc) > 0
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assert len(continuation_enc) > 0
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assert len(continuation_enc) <= self.max_length
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# how this all works (illustrated on a causal decoder-only setup):
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# CTX CONT
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# inp 0 1 2 3|4 5 6 7 8 9 <- last token is deleted by inp[:, :-1]
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# model \ \
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# logits 1 2 3|4 5 6 7 8 9 <- the ctx half gets tossed out by the
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# cont_toks 4 5 6 7 8 9 [:, -len(continuation_enc):, :self.vocab_size] slice
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# when too long to fit in context, truncate from the left
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if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
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inp = torch.tensor(
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(context_enc + continuation_enc)[-(self.max_length + 1) :][:-1],
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dtype=torch.long,
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device=self.device,
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)
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(inplen,) = inp.shape
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elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
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inp = torch.tensor(
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(context_enc)[-self.max_length :],
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dtype=torch.long,
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device=self.device,
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)
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(inplen,) = inp.shape
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# build encoder attn masks
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encoder_attns.append(torch.ones_like(inp))
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cont = torch.tensor(
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(continuation_enc)[-self.max_length :],
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# TODO: left-shift these?
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# TODO: our code assumes we never end up truncating conts for either model type
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dtype=torch.long,
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device=self.device,
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)
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(contlen,) = cont.shape
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conts.append(cont)
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padding_len_cont = (
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max(padding_len_cont, contlen)
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if padding_len_cont is not None
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else contlen
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)
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padding_len_inp = (
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max(padding_len_inp, inplen)
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if padding_len_inp is not None
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else inplen
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)
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inps.append(inp) # [1, inp_length]
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cont_toks_list.append(continuation_enc)
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inplens.append(inplen)
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# create encoder attn mask and batched conts, if seq2seq
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call_kwargs = {}
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if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
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batched_inps = pad_and_concat(
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padding_len_inp, inps, padding_side="right"
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) # [batch, padding_len_inp]
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elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
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# TODO: left-pad encoder inps and mask?
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batched_inps = pad_and_concat(
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padding_len_inp, inps
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) # [batch, padding_len_inp]
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batched_conts = pad_and_concat(
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padding_len_cont, conts
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) # [batch, padding_len_cont]
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batched_encoder_mask = pad_and_concat(
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padding_len_inp, encoder_attns
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) # [batch, padding_len_inp]
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call_kwargs = {
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"attn_mask": batched_encoder_mask,
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"labels": batched_conts,
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}
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start = time()
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intermediate_res = self._model_call(batched_inps, **call_kwargs)
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end = time()
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multi_logits = F.log_softmax(
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intermediate_res , dim=-1
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) # [batch, padding_length (inp or cont), vocab]
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per_sample_time = (end - start) / len(multi_logits)
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for (request_str, ctx_tokens, _), logits, inplen, cont_toks in zip(
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chunk, multi_logits, inplens, cont_toks_list
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):
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# Slice to original seq length
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contlen = len(cont_toks)
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# take only logits in the continuation
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# (discard context toks if decoder-only ; discard right-padding)
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# also discards + checks for "virtual tokens" in the causal LM's input window
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# from prompt/prefix tuning tokens, if applicable
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ctx_len = (
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inplen + (logits.shape[0] - padding_len_inp)
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if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM
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else None
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)
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logits = self._select_cont_toks(logits, contlen=contlen, inplen=ctx_len)
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logits = logits.unsqueeze(0) # [1, seq, vocab]
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# Check if per-token argmax is exactly equal to continuation
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greedy_tokens = logits.argmax(dim=-1)
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# check for one-token continuation cache hits.
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# noop in case group_by != "contexts" or no cache hit and returns the
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# original args. Otherwise, expands the logits batch dimension and yields each
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# batch along with matching continuation tokens and prompt strings.
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# logits -> [1, seq, vocab]
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for request_str, cont_toks, logits in re_ord.get_cache(
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req_str=request_str,
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cxt_toks=ctx_tokens,
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cont_toks=cont_toks,
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logits=logits,
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):
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cont_toks = torch.tensor(
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cont_toks, dtype=torch.long, device=self.device
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).unsqueeze(0) # [1, seq]
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max_equal = (greedy_tokens == cont_toks).all()
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# Obtain log-probs at the corresponding continuation token indices
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# last_token_slice = logits[:, -1, :].squeeze(0).tolist()
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logits = torch.gather(logits, 2, cont_toks.unsqueeze(-1)).squeeze(
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-1
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) # [1, seq]
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# Answer: (log prob, is-exact-match)
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answer = (float(logits.sum()), bool(max_equal))
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res.append((answer, per_sample_time, 0, 0))
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self.cache_hook.add_partial("loglikelihood", request_str, answer)
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pbar.update(1)
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pbar.close()
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return re_ord.get_original(res)
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def _model_generate(self, context, max_length, stop, **generation_kwargs):
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# temperature = 0.0 if not set
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# if do_sample is false and temp==0.0:
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src/backend/run_eval_suite.py
CHANGED
@@ -1,13 +1,57 @@
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from lm_eval import evaluator
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from lm_eval.tasks import TaskManager
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from src.backend.manage_requests import EvalRequest
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from src.backend.tasks.xsum.task import XSum
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from src.backend.tasks.xsum.task_v2 import XSumv2
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-
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-
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from src.backend.tasks.selfcheckgpt.task import SelfCheckGPT
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from lm_eval import evaluator
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from lm_eval.tasks import TaskManager
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from lm_eval.api.metrics import mean
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from lm_eval.api.task import ConfigurableTask
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from src.backend.manage_requests import EvalRequest
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orig_process_results = ConfigurableTask.process_results
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orig_aggregation = ConfigurableTask.aggregation
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orig_higher_is_better = ConfigurableTask.higher_is_better
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def process_results_decorator(func):
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def wrapper(self, doc, results, *args, **kwargs):
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processed_results = [r[0] for r in results]
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end_to_end_time = sum([r[1] for r in results]) / len(results)
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prefilling_time = sum([r[2] for r in results]) / len(results)
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decoding_throughput = sum([r[3] for r in results]) / len(results)
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# print(f"end_to_end_time: {end_to_end_time}, prefilling_time: {prefilling_time}, decoding_throughput: {decoding_throughput}")
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result_dict = func(self, doc, processed_results, *args, **kwargs)
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result_dict["end_to_end_time"] = end_to_end_time
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result_dict["prefilling_time"] = prefilling_time
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result_dict["decoding_throughput"] = decoding_throughput
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return result_dict
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return wrapper
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ConfigurableTask.process_results = process_results_decorator(orig_process_results)
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def aggregation_decorator(func):
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def wrapper(self, *args, **kwargs):
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aggregation_list = func(self, *args, **kwargs)
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aggregation_list["end_to_end_time"] = mean
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aggregation_list["prefilling_time"] = mean
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aggregation_list["decoding_throughput"] = mean
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return aggregation_list
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return wrapper
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ConfigurableTask.aggregation = aggregation_decorator(orig_aggregation)
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def higher_is_better_decorator(func):
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41 |
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def wrapper(self, *args, **kwargs):
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higher_is_better_dict = func(self, *args, **kwargs)
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higher_is_better_dict["end_to_end_time"] = False
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higher_is_better_dict["prefilling_time"] = False
|
45 |
+
higher_is_better_dict["decoding_throughput"] = True
|
46 |
+
return higher_is_better_dict
|
47 |
+
return wrapper
|
48 |
+
ConfigurableTask.higher_is_better = higher_is_better_decorator(orig_higher_is_better)
|
49 |
+
|
50 |
+
# from src.backend.tasks.xsum.task import XSum
|
51 |
+
# from src.backend.tasks.xsum.task_v2 import XSumv2
|
52 |
+
|
53 |
+
# from src.backend.tasks.cnndm.task import CNNDM
|
54 |
+
# from src.backend.tasks.cnndm.task_v2 import CNNDMv2
|
55 |
|
56 |
from src.backend.tasks.selfcheckgpt.task import SelfCheckGPT
|
57 |
|
src/backend/tasks/measurement_task_utils.py
CHANGED
@@ -12,7 +12,7 @@ def process_results_decorator(func):
|
|
12 |
end_to_end_time = sum([r[1] for r in results]) / len(results)
|
13 |
prefilling_time = sum([r[2] for r in results]) / len(results)
|
14 |
decoding_throughput = sum([r[3] for r in results]) / len(results)
|
15 |
-
print(f"end_to_end_time: {end_to_end_time}, prefilling_time: {prefilling_time}, decoding_throughput: {decoding_throughput}")
|
16 |
|
17 |
# Now call the original process_results with the processed results
|
18 |
result_dict = func(self, doc, processed_results, *args, **kwargs)
|
|
|
12 |
end_to_end_time = sum([r[1] for r in results]) / len(results)
|
13 |
prefilling_time = sum([r[2] for r in results]) / len(results)
|
14 |
decoding_throughput = sum([r[3] for r in results]) / len(results)
|
15 |
+
# print(f"end_to_end_time: {end_to_end_time}, prefilling_time: {prefilling_time}, decoding_throughput: {decoding_throughput}")
|
16 |
|
17 |
# Now call the original process_results with the processed results
|
18 |
result_dict = func(self, doc, processed_results, *args, **kwargs)
|