comments

llms/mlx_lm/evaluate.py

@@ -10,7 +10,7 @@ import logging
 import os
 from importlib.metadata import version
 from pathlib import Path
-from typing import Optional, Union
+from typing import Optional
 
 import lm_eval
 import mlx.core as mx
@@ -20,11 +20,10 @@ from lm_eval.api.model import LM
 from lm_eval.api.registry import register_model
 from tqdm import tqdm
 
+from .models.base import create_causal_mask
 from .models.cache import make_prompt_cache
 from .utils import load, stream_generate
 
-PAD = 0
-
 
 def _len_longest_common_prefix(a, b):
     l = 0
@@ -43,31 +42,14 @@ def _rstrip_until(s, untils):
     return s[: min(f)]
 
 
-def _pad_inputs(
-    inputs,
-    maxlen,
-    genlen=0,
-    pad_left=False,
-    pad_multiple=32,
-    truncate=False,
-):
-    # pad the prompts to the left with at least genlen tokens.
-    actual_maxlen = max(len(p) for p in inputs) + genlen
-    if actual_maxlen > maxlen:
-        if not truncate:
-            raise ValueError("Inputs are too long.")
-        else:  # drop begining
-            actual_maxlen = maxlen
-            inputs = [p[max(0, len(p) - maxlen) :] for p in inputs]
-    if pad_multiple > 0:
-        maxlen = (actual_maxlen + pad_multiple - 1) // pad_multiple
-        maxlen *= pad_multiple
-    assert PAD == 0
-    lr = np.array((1, 0) if pad_left else (0, 1))
-    return np.stack(
-        [np.pad(np.array(x, np.int32), lr * (maxlen - len(x))) for x in inputs],
+def _pad_inputs(inputs):
+    lengths = np.array([len(x) for x in inputs])
+    maxlen = lengths.max()
+    padded = np.stack(
+        [np.pad(x, (0, maxlen - len(x))) for x in inputs],
         axis=0,
     )
+    return mx.array(padded), mx.array(lengths)
 
 
 @register_model("mlxlm")
@@ -83,32 +65,33 @@ class MLXLM(LM):
         self._batch_size = batch_size
         self._model, self.tokenizer = load(path_or_hf_repo)
         self._max_tokens = max_tokens or self.tokenizer.model_max_length
-        self.use_chat_template = use_chat_template or (
+        self.use_chat_template = use_chat_template and (
             self.tokenizer.chat_template is not None
         )
 
-    def _score_fn(self, inputs, tokenize=True, step_size=32):
-        if tokenize:
-            inputs = self._tokenize(inputs)
-        inputs = _pad_inputs(inputs, self._max_tokens, truncate=False)
-        inputs = mx.array(inputs)
+    def _score_fn(self, inputs, step_size: int = 64):
+        inputs, lengths = _pad_inputs(inputs)
         inputs, targets = inputs[..., :-1], inputs[..., 1:]
 
         cache = make_prompt_cache(self._model)
 
-        mask = targets != PAD
-
         scores, is_greedy = [], []
         for i in range(0, inputs.shape[1], step_size):
-            logits = self._model(inputs[:, i : i + step_size], cache=cache)
+            inp = inputs[:, i : i + step_size]
+            T = inp.shape[1]
 
+            offset = cache[0].offset
+            mask = create_causal_mask(T, offset, lengths=lengths)
+            mask = mask == 0
+
+            logits = self._model(inp, cache=cache, mask=mask)
             log_probs = nn.log_softmax(logits.astype(mx.float32))
+
             score = mx.take_along_axis(
                 log_probs, targets[:, i : i + step_size, mx.newaxis], axis=-1
             )[..., 0]
-            ig = mask[:, i : i + step_size] * (
-                targets[:, i : i + step_size] == mx.argmax(logits, axis=-1)
-            )
+            ig = targets[:, i : i + step_size] == mx.argmax(logits, axis=-1)
+            ig = mx.where(mx.arange(T) + offset < lengths[:, None], ig, False)
 
             mx.eval(score, ig)
             mx.metal.clear_cache()
@@ -119,38 +102,32 @@ class MLXLM(LM):
         scores = mx.concatenate(scores, axis=1)
         is_greedy = mx.concatenate(is_greedy, axis=1)
 
-        return scores, mask.sum(axis=-1), is_greedy
+        return scores, lengths, is_greedy
 
-    def _loglikelihood(self, texts, score_spans=None, tokenize=True):
-        # sort by length to get batches with little padding.
-        sorted_indices = sorted(range(len(texts)), key=lambda i: -len(texts[i]))
-        sorted_inputs = [texts[sorted_indices[i]] for i in range(len(texts))]
-        sorted_spans = None
-        if score_spans is not None:
-            sorted_spans = [score_spans[sorted_indices[i]] for i in range(len(texts))]
+    def _loglikelihood(self, texts, score_spans=None):
+        all_scores = mx.zeros(len(texts))
+        all_is_greedy = mx.zeros(len(texts), dtype=mx.bool_)
+        for i in tqdm(range(0, len(texts), self._batch_size)):
+            batch = texts[i : i + self._batch_size]
+            scores, lengths, is_greedy = self._score_fn(batch)
 
-        results = []
-        for i in tqdm(range(0, len(sorted_inputs), self._batch_size)):
-            batch = sorted_inputs[i : i + self._batch_size]
-            scores, length, is_greedy = self._score_fn(batch, tokenize=tokenize)
-            for j in range(len(batch)):
-                if sorted_spans is None:  # full sequence score
-                    mask = mx.arange(scores[j].shape[-1]) < length
-                    score = (scores[j].astype(mx.float32) * mask).sum(axis=-1)
-                    ig = (is_greedy[j].astype(mx.int32) * mask).sum(axis=-1)
-                else:  # subsequence score
-                    start, end = sorted_spans[i + j]
-                    score = scores[j][start:end].astype(mx.float32).sum()
-                    ig = is_greedy[j][start:end].astype(mx.int32).sum()
-                    length = end - start
+            ind = np.arange(scores.shape[-1])
+            if score_spans is not None:
+                spans = score_spans[i : i + self._batch_size]
+                lengths = [end - start for start, end in spans]
+                masks = mx.array(
+                    np.array([(ind >= start) & (ind < end) for start, end in spans])
+                )
+            else:
+                masks = ind[None] < lengths[:, None]
 
-                results.append((score.item(), ig.item(), length))
+            scores = (masks * scores).sum(axis=-1)
+            is_greedy = (masks * is_greedy).sum(axis=-1)
 
-        # reorder the outputs
-        inv_sort = np.argsort(sorted_indices)
-        results = [results[inv_sort[i]] for i in range(len(results))]
+            all_scores[i : i + self._batch_size] = scores
+            all_is_greedy[i : i + self._batch_size] = is_greedy == lengths
 
-        return results
+        return all_scores, all_is_greedy
 
     def _tokenize(self, texts):
         return [
@@ -222,16 +199,53 @@ class MLXLM(LM):
                 + "completion longer than context."
             )
 
+        num_results = len(shortened)
+
+        # sort by length to get batches with little padding.
+        sorted_indices = sorted(range(len(shortened)), key=lambda i: -len(shortened[i]))
+        shortened = [shortened[i] for i in sorted_indices]
+        completion_spans = [completion_spans[i] for i in sorted_indices]
+
+        group = mx.distributed.init()
+
+        # split strided so we have approximately the same lengths on each node
+        shortened = shortened[group.rank() :: group.size()]
+        completion_spans = completion_spans[group.rank() :: group.size()]
+
         # model scoring, returns num_requests x (logp, is_greedy, length).
-        results = self._loglikelihood(
+        scores, is_greedy = self._loglikelihood(
             shortened,
             score_spans=completion_spans,
-            tokenize=False,
         )
-        return [(r[0], r[1] == r[2]) for r in results]
+
+        # all gather the results across groups
+        if group.size() > 1:
+            per_group = int(np.ceil(num_results / group.size()))
+            scores = mx.pad(scores, ((0, per_group - len(scores)),))
+            is_greedy = mx.pad(is_greedy, ((0, per_group - len(is_greedy))))
+            scores = mx.distributed.all_gather(scores[mx.newaxis], stream=mx.cpu)
+            is_greedy = mx.distributed.all_gather(is_greedy[mx.newaxis], stream=mx.cpu)
+            scores = scores.T.reshape(-1)
+            is_greedy = is_greedy.T.reshape(-1)
+
+        scores = np.array(scores[:num_results])
+        is_greedy = np.array(is_greedy[:num_results])
+
+        results = [(score, ig) for score, ig in zip(scores, is_greedy)]
+        inv_sort = np.argsort(sorted_indices)
+        results = [results[inv_sort[i]] for i in range(len(inv_sort))]
+        return results
 
     tokenizer_name = lm_eval.models.huggingface.HFLM.tokenizer_name
-    apply_chat_template = lm_eval.models.huggingface.HFLM.apply_chat_template
+
+    def apply_chat_template(
+        self, chat_history: list[dict[str, str]], add_generation_prompt: bool = True
+    ) -> str:
+        if len(chat_history) == 0:
+            return ""
+        return lm_eval.models.huggingface.HFLM.apply_chat_template(
+            chat_history, add_generation_prompt
+        )
 
     def loglikelihood_rolling(self, requests) -> list[float]:
         """Compute full log-likelihood of a string, with no truncation, for perplexity computation
@@ -268,8 +282,9 @@ class MLXLM(LM):
         logging.info(
             "Estimating loglikelihood rolling for %d sequences." % len(requests)
         )
-        inputs = [req.args[0] for req in requests]
-        return [t[0] for t in self._loglikelihood(inputs)]
+        inputs = self._tokenize([req.args[0] for req in requests])
+        scores, _ = self._loglikelihood(inputs)
+        return scores.tolist()
 
     def generate_until(self, requests) -> list[str]:
         """Generate greedily until a stopping sequence
@@ -332,7 +347,7 @@ def main():
     )
     parser.add_argument(
         "--limit",
-        default=1.0,
+        default=None,
         help="Limit the number of examples per task.",
         type=float,
     )
@@ -346,11 +361,8 @@ def main():
     )
     parser.add_argument(
         "--apply-chat-template",
-        action=argparse.BooleanOptionalAction,
-        help="Specifies whether to apply a chat template to the prompt. If "
-        "the model has a chat template, this defaults to `True`, "
-        "otherwise `False`.",
-        default=None,
+        action="store_true",
+        help="Specifies whether to apply a chat template to the prompt.",
     )
     args = parser.parse_args()