add preliminary working HF-auto LM

lm_eval/models/__init__.py

@@ -3,5 +3,6 @@ from . import openai_completions
 from . import textsynth
 from . import dummy
 from . import seq2seq
+from . import hf_merged
 
 # TODO: implement __all__

lm_eval/models/hf_merged.py

+import torch
+import transformers
+from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
+
+import copy
+from tqdm import tqdm
+
+import torch.nn.functional as F
+
+from lm_eval import utils
+from lm_eval.logger import eval_logger
+from lm_eval.api.model import LM
+from lm_eval.api.registry import register_model
+
+from lm_eval.utils import MultiTokenEOSCriteria, stop_sequences_criteria
+
+from accelerate import Accelerator
+
+
+@register_model("hf-auto")
+class HFLM(LM):
+    AUTO_MODEL_CLASS = None
+    def __init__(
+        self,
+        device="cuda",
+        pretrained="gpt2",
+        revision="main",
+        low_cpu_mem_usage=None,
+        subfolder=None,
+        tokenizer=None,
+        batch_size=1,
+    ):
+        super().__init__()
+
+        assert isinstance(device, str)
+        assert isinstance(pretrained, str)
+        assert isinstance(batch_size, int)
+
+        gpus = torch.cuda.device_count()
+        
+        if gpus <= 1:
+            if device:
+                if device not in ["cuda", "cpu"]:
+                    device = int(device)
+                self._device = torch.device(device)
+                eval_logger.info(f"Using device '{device}'")
+            else:
+                eval_logger.info("Device not specified")
+                eval_logger.info(f"Cuda Available? {torch.cuda.is_available()}")
+                self._device = (
+                    torch.device("cuda")
+                    if torch.cuda.is_available()
+                    else torch.device("cpu")
+                )
+            self._rank = 0
+            self._world_size = 1
+
+        else:
+            self._device = "cpu"
+
+        # TODO: update this to be less of a hack once subfolder is fixed in HF
+        revision = revision + ("/" + subfolder if subfolder is not None else "")
+
+        # get config 
+        self._config = transformers.AutoConfig.from_pretrained(
+            pretrained,
+            revision=revision,
+        )
+
+        if getattr(self._config, "model_type") in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
+            self.AUTO_MODEL_CLASS = transformers.AutoModelForCausalLM
+        else:
+            self.AUTO_MODEL_CLASS = transformers.AutoModelForSeq2SeqLM 
+
+        assert self.AUTO_MODEL_CLASS in [transformers.AutoModelForCausalLM, transformers.AutoModelForSeq2SeqLM]
+
+        self.model = self.AUTO_MODEL_CLASS.from_pretrained(
+            pretrained, revision=revision, low_cpu_mem_usage=low_cpu_mem_usage
+        ).to(self.device)
+        self.model.eval()
+
+        self.tokenizer = transformers.AutoTokenizer.from_pretrained(
+            pretrained if tokenizer is None else tokenizer,
+            revision=revision,
+        )
+
+        self.vocab_size = self.tokenizer.vocab_size
+
+        # multithreading and batching
+        self.batch_size_per_gpu = batch_size  # todo: adaptive batch size
+
+        # multigpu support with accelerate
+        if gpus > 1:
+            accelerator = Accelerator()
+            if gpus > accelerator.num_processes:
+                eval_logger.warning(
+                    "WARNING: The number of total system GPUs does not match the number of spawned processes. "
+                    "If you would like to use data parallelism, please launch the script "
+                    "with 'accelerate launch *script*'. "
+                    f"Current run will proceed with {accelerator.num_processes} devices."
+                )
+                self._rank = accelerator.local_process_index
+                self._world_size = accelerator.num_processes
+                # manually set model to use gpu, for case where many GPUs available but
+                # only seek to use one
+                self._device = (
+                    torch.device(f"cuda:{accelerator.local_process_index}")
+                    if torch.cuda.is_available()
+                    else torch.device("cpu")
+                )
+                self.model.to(self.device)
+            else:
+                self.model = accelerator.prepare(self.model)
+                self._device = torch.device(f"cuda:{accelerator.local_process_index}")
+                self.accelerator = accelerator
+
+                if self.accelerator.is_local_main_process:
+                    eval_logger.info(f"Using {gpus} devices with data parallelism")
+
+                self._rank = self.accelerator.local_process_index
+                self._world_size = self.accelerator.num_processes
+        
+    @property
+    def eot_token_id(self):
+        # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence*
+        return self.tokenizer.eos_token_id
+
+    # TODO: add a self.config property
+    # TODO: make model at self._model, have self.model property unwrap accelerator if needed under hood?
+    @property
+    def max_length(self):
+        try:
+            if hasattr(self, "accelerator"):
+                return self.accelerator.unwrap_model(self.model).config.n_ctx
+            else:
+                return self.model.config.n_ctx
+        except AttributeError:
+            # gptneoconfig doesn't have n_ctx apparently
+            if hasattr(self, "accelerator"):
+                return self.accelerator.unwrap_model(
+                    self.model
+                ).config.max_position_embeddings
+            else:
+                return self.model.config.max_position_embeddings
+    
+    @property
+    def max_gen_toks(self):
+        return 256
+
+    @property
+    def batch_size(self):
+        return self.batch_size_per_gpu
+
+    @property
+    def device(self):
+        return self._device
+
+    @property
+    def rank(self):
+        return self._rank
+
+    @property
+    def world_size(self):
+        return self._world_size
+
+    def tok_encode(self, string: str, left_truncate_len=None):
+        """
+
+        """
+        if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
+            add_special_tokens = False
+        elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
+            add_special_tokens = True
+
+        encoding = self.tokenizer.encode(string, add_special_tokens=add_special_tokens)
+        
+        # left-truncate the encoded context to be at most `left_truncate_len` tokens long
+        if left_truncate_len:
+            encoding = encoding[-left_truncate_len:]
+        
+        return encoding
+
+    def tok_decode(self, tokens):
+        if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
+            return self.tokenizer.decode(tokens)
+        elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
+            return self.tokenizer.decode(tokens, skip_special_tokens=True)
+
+    def _model_call(self, inps, attn_mask = None ,labels = None):
+        """
+        inps: a torch tensor of shape [batch, sequence_ctx]
+        the size of sequence may vary from call to call
+
+        labels: a torch tensor of shape [batch, sequence_cont]
+        the size of sequence may vary from call to call
+
+        returns: a torch tensor of shape [batch, sequence, vocab] with the
+        logits returned from the model
+        """
+        with torch.no_grad():
+            return self.model(input_ids = inps, attention_mask = attn_mask, labels = labels).logits
+        
+    def _model_call(self, inps, attn_mask=None, labels=None):
+        """
+        inps: torch.Tensor
+            A torch tensor of shape [batch, (sequence_ctx + sequence_cont)] or of shape
+            [batch, sequence_ctx]. the size of sequence may vary from call to call
+        :param attn_mask: torch.Tensor, optional
+            A torch tensor of shape [batch, (sequence_ctx + sequence_cont)]. Only passed
+            (and must be passed) if self.AUTO_MODEL_CLASS is transformers.AutoModelForSeq2SeqLM
+        :param labels: torch.Tensor, optional
+            A torch tensor of shape [batch, (sequence_ctx + sequence_cont)]. Only passed
+            (and must be passed) if self.AUTO_MODEL_CLASS is transformers.AutoModelForSeq2SeqLM
+        :return
+            A torch tensor of shape [batch, sequence, vocab] with the
+        logits returned from the model's decoder
+        """
+        with torch.no_grad():
+            if attn_mask or labels:
+                assert self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM
+                return self.model(input_ids = inps, attention_mask = attn_mask, labels = labels).logits
+            else:
+                assert self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM
+                return self.model(inps).logits
+
+    def _model_generate(self, context, max_length, stop, **generation_kwargs):
+        # we require users to pass do_sample=True explicitly
+        # for non-greedy gen. This should be reevaluated when considering beam search.
+        if "do_sample" not in generation_kwargs.keys():
+            generation_kwargs["do_sample"] = False
+        # build stopping criteria
+        stopping_criteria = stop_sequences_criteria(
+            self.tokenizer, stop, 1, context.shape[0]
+        )
+        if hasattr(self, "accelerator"):
+            return self.accelerator.unwrap_model(self.model).generate(
+                context,
+                max_length=max_length,
+                stopping_criteria=stopping_criteria,
+                pad_token_id=self.eot_token_id,
+                use_cache=True,
+                **generation_kwargs,
+            )
+        else:
+            return self.model.generate(
+                context,
+                max_length=max_length,
+                stopping_criteria=stopping_criteria,
+                pad_token_id=self.eot_token_id,
+                use_cache=True,
+                **generation_kwargs,
+            )
+
+    def _select_cont_toks(self, logits, contlen=None, inplen=None):
+        if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
+            assert (contlen and inplen), "Must pass input len and cont. len to select scored logits for causal LM" 
+            # discard right-padding.
+            # also discard the input/context tokens. we'll only score continuations.
+            logits = logits[inplen - contlen : inplen]
+        elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
+            assert (contlen and not inplen), "Selecting scored logits for Seq2SeqLM requires only cont. len"
+            # only discard right-padding. 
+            # the logits input to this fn only contain decoder-side tokens.
+            logits = logits[: contlen]
+        
+        return logits
+
+    def loglikelihood(self, requests):
+        new_reqs = []
+        for context, continuation in [req.args for req in requests]:
+            if context == "":
+                # end of text as context
+                context_enc = [self.eot_token_id]
+            else:
+                context_enc = self.tok_encode(context)
+
+            continuation_enc = self.tok_encode(continuation)
+
+            new_reqs.append(((context, continuation), context_enc, continuation_enc))
+
+        return self._loglikelihood_tokens(new_reqs)
+
+    def loglikelihood_rolling(self, requests):
+        loglikelihoods = []
+        for (string,) in tqdm([req.args for req in requests], disable=(self.rank != 0)):
+            rolling_token_windows = list(
+                map(
+                    utils.make_disjoint_window,
+                    utils.get_rolling_token_windows(
+                        token_list=self.tok_encode(string),
+                        prefix_token=self.eot_token_id, 
+                        max_seq_len=self.max_length,
+                        context_len=1,
+                    ),
+                )
+            )
+            
+            #TODO: Right now, we pass single EOT token to the Encoder and the full context to the decoder
+            rolling_token_windows = [(None,) + x for x in rolling_token_windows]
+
+            pad_amnt = 0
+            if self.world_size > 1:
+                # We pad out the external document-level iterator so the inner iterator doesn't hang
+                mytensor = torch.tensor(len(rolling_token_windows), device=self.device)
+                gathered = (
+                    self.accelerator.gather(mytensor).cpu().detach().numpy().tolist()
+                )
+
+                pad_amnt = max(gathered) - gathered[self.rank]
+                if pad_amnt > 0:
+                    rolling_token_windows += pad_amnt * [rolling_token_windows[0]]
+
+            string_nll = self._loglikelihood_tokens(
+                rolling_token_windows, disable_tqdm=True
+            )
+
+            if (self.world_size > 1) and (pad_amnt > 0):
+                string_nll = [x[0] for x in string_nll[:-pad_amnt]]
+            else:
+                # discard is_greedy
+                string_nll = [x[0] for x in string_nll]
+
+            string_nll = sum(string_nll)
+            loglikelihoods.append(string_nll)
+
+        return loglikelihoods
+
+    def _loglikelihood_tokens(self, requests, disable_tqdm=False):
+        # TODO: implement some kind of efficient-request-middleware that lumps together requests with the same context
+        res = []
+
+        def _collate(x):
+            # the negative sign on len(toks) sorts descending - this has a few advantages:
+            # - time estimates will always be over not underestimates, which is more useful for planning
+            # - to know the size of a batch when going through the list, you know the first one is always the batch
+            #   padded context length. this is useful to simplify the batching logic and more importantly to make
+            #   automatic adaptive batches much much easier to implement
+            # - any OOMs will happen right away rather than near the end
+
+            toks = x[1] + x[2]
+            return -len(toks), tuple(toks)
+
+        # TODO: automatic (variable) batch size detection for vectorization
+        re_ord = utils.Reorderer(requests, _collate)
+        for chunk in utils.chunks(
+            tqdm(re_ord.get_reordered(), disable=(disable_tqdm or (self.rank != 0))),
+            self.batch_size,
+        ):
+
+            inps = []
+            cont_toks_list = []
+            inplens = []
+
+            conts = []
+            encoder_attns = []
+
+            padding_len_inp = None
+            padding_len_cont = None
+            # because vectorizing is annoying, we first convert each (context, continuation) pair to padded
+            # tensors, then we pack them together into a batch, call the model, and then pick it all apart
+            # again because vectorizing is annoying
+
+            for _, context_enc, continuation_enc in chunk:
+                # sanity check
+                assert len(context_enc) > 0
+                assert len(continuation_enc) > 0
+                assert len(continuation_enc) <= self.max_length
+
+                # how this all works:
+                #          CTX      CONT
+                # inp    0 1 2 3|4 5 6 7 8 9   <- last token is deleted by inp[:, :-1]
+                # model  \               \
+                # logits   1 2 3|4 5 6 7 8 9   <- the ctx half gets tossed out by the
+                # cont_toks      4 5 6 7 8 9      [:, -len(continuation_enc):, :self.vocab_size] slice
+
+                # when too long to fit in context, truncate from the left
+                if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
+                    inp = torch.tensor(
+                        (context_enc + continuation_enc)[-(self.max_length + 1) :][:-1],
+                        dtype=torch.long,
+                    ).to(self.device)
+                    (inplen,) = inp.shape
+                elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
+                    inp = torch.tensor(
+                        (context_enc)[-self.max_length :],
+                        dtype=torch.long,
+                    ).to(self.device)
+                    (inplen,) = inp.shape
+                    cont = torch.tensor(
+                        (continuation_enc)[-self.max_length :],
+                        dtype=torch.long,
+                    ).to(self.device)
+                    (contlen,) = cont.shape
+
+                    padding_len_cont = max(padding_len_cont, contlen) if padding_len_cont is not None else contlen
+
+                padding_len_inp = max(padding_len_inp, inplen) if padding_len_inp is not None else inplen
+
+
+                # # pad length from seq to padding_length
+                # inp = torch.cat(
+                #     [
+                #         inp,  # [seq]
+                #         torch.zeros(padding_length - inplen, dtype=torch.long).to(
+                #             inp.device
+                #         ),  # [padding_length - seq]
+                #     ],
+                #     dim=0,
+                # )
+
+                inps.append(inp)  # [1, inp_length]
+                cont_toks_list.append(continuation_enc)
+                inplens.append(inplen)
+            
+            # create encoder attn mask and batched conts, if seq2seq
+            call_kwargs = {}
+            if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
+                # batched_inps = torch.cat(inps, dim=0)  # [batch, padding_length]
+                batched_inps = utils.pad_and_concat(padding_len_inp, inps, padding_side="right")
+            elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
+                # TODO: left-pad encoder inps and mask?
+                batched_inps = utils.pad_and_concat(padding_len_inp, inps) # [batch, enc_padding_length]
+                batched_conts = utils.pad_and_concat(padding_len_cont, conts) # [batch, padding_length]
+                batched_encoder_mask = utils.pad_and_concat(padding_len_inp, encoder_attns) # size???
+                call_kwargs = {"attn_mask": batched_encoder_mask, "labels": batched_conts}
+
+            multi_logits = F.log_softmax(
+                self._model_call(batched_inps, **call_kwargs), dim=-1
+            ).cpu()  # [batch, padding_length, vocab]
+
+            for (cache_key, _, _), logits, inplen, cont_toks in zip(
+                chunk, multi_logits, inplens, cont_toks_list
+            ):
+
+                # Slice to original seq length
+                contlen = len(cont_toks)
+                # take only logits in the continuation 
+                # (discard context toks if decoder-only ; discard right-padding)
+                logits = self._select_cont_toks(logits, contlen=contlen, inplen=inplen)
+                logits = logits.unsqueeze(
+                    0
+                )  # [1, seq, vocab]
+
+                # Check if per-token argmax is exactly equal to continuation
+                greedy_tokens = logits.argmax(dim=-1)
+                cont_toks = torch.tensor(cont_toks, dtype=torch.long).unsqueeze(
+                    0
+                )  # [1, seq]
+                max_equal = (greedy_tokens == cont_toks).all()
+
+                # Obtain log-probs at the corresponding continuation token indices
+                # last_token_slice = logits[:, -1, :].squeeze(0).tolist()
+                logits = torch.gather(logits, 2, cont_toks.unsqueeze(-1)).squeeze(
+                    -1
+                )  # [1, seq]
+
+                # Answer: (log prob, is-exact-match)
+                answer = (float(logits.sum()), bool(max_equal))
+
+                res.append(answer)
+
+        return re_ord.get_original(res)
+
+    def greedy_until(self, requests):
+        res = []
+
+        def _collate(x):
+            toks = self.tok_encode(x[0])
+            return len(toks), x[0]
+
+        re_ord = utils.Reorderer([req.args for req in requests], _collate)
+
+        for context, gen_kwargs in tqdm(re_ord.get_reordered()):
+            until = None
+            if isinstance(gen_kwargs, dict):
+                gen_kwargs = copy.deepcopy(gen_kwargs)  # edge case for repeats > 1
+                print(gen_kwargs)
+                if "until" in gen_kwargs.keys():
+                    until = gen_kwargs.pop("until")
+                    if isinstance(until, str):
+                        until = [gen_kwargs]
+                    elif not isinstance(until, list):
+                        raise ValueError(
+                            f"Expected `gen_kwargs['until']` to be of type Union[str,list] but got {until}"
+                        )
+            else:
+                raise ValueError(
+                    f"Expected `gen_kwargs` to be of type `dict` but got {gen_kwargs}"
+                )
+            if not until:
+                until = [self.tok_decode(self.eot_token_id)]
+            if "max_gen_toks" in gen_kwargs.keys():
+                max_gen_toks = gen_kwargs.pop("max_gen_toks")
+            else:
+                max_gen_toks = self.max_gen_toks
+
+            # first stop sequence is used to halt generation upon encountering
+            (primary_until) = until[0]
+
+            # set the max length in tokens of inputs ("context_enc")
+            if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
+                # max len for inputs = max length, minus room to generate the max new tokens
+                max_ctx_len = self.max_length - max_gen_toks
+            elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
+                # max len for inputs = encoder's whole max_length
+                max_ctx_len = self.max_length
+
+            context_enc = torch.tensor(
+                [self.tok_encode(context, left_truncate_len=max_ctx_len)]
+            ).to(self.device)
+
+            cont = self._model_generate(
+                context=context_enc, 
+                max_length=context_enc.shape[1] + max_gen_toks, 
+                stop=primary_until,
+                **gen_kwargs,
+            )
+            s = self.tok_decode(cont[0].tolist())
+
+            # use secondary stop seqs to cut off should-have-been-stopped content post-hoc
+            for term in until:
+                s = s.split(term)[0]
+
+            res.append(s)
+
+        return re_ord.get_original(res)
\ No newline at end of file