comments

llms/mlx_lm/evaluate.py

@@ -10,7 +10,7 @@
 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
@@ -43,13 +43,13 @@ def _rstrip_until(s, untils):
 
 
 def _pad_inputs(inputs):
-    lengths = mx.array([len(x) for x in inputs])
+    lengths = np.array([len(x) for x in inputs])
     maxlen = lengths.max()
-    padded = mx.stack(
-        [mx.pad(mx.array(x), (0, maxlen - len(x))) for x in inputs],
+    padded = np.stack(
+        [np.pad(x, (0, maxlen - len(x))) for x in inputs],
         axis=0,
     )
-    return padded, lengths
+    return mx.array(padded), mx.array(lengths)
 
 
 @register_model("mlxlm")
@@ -65,26 +65,24 @@ def __init__(
         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, step_size=64):
+    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)
 
-        # TODO: come up with a better way to get the dtype
-        dtype = self._model.model.embed_tokens(inputs).dtype
-
         scores, is_greedy = [], []
         for i in range(0, inputs.shape[1], step_size):
             inp = inputs[:, i : i + step_size]
             T = inp.shape[1]
 
             offset = cache[0].offset
-            mask = create_causal_mask(T, offset, lengths=lengths).astype(dtype)
+            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))
@@ -107,24 +105,29 @@ def _score_fn(self, inputs, step_size=64):
         return scores, lengths, is_greedy
 
     def _loglikelihood(self, texts, score_spans=None):
-        results = []
+        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, length, is_greedy = self._score_fn(batch)
-            for j in range(len(batch)):
-                if score_spans is None:  # full sequence score
-                    l = length[j].item()
-                    score = scores[j][:l].astype(mx.float32).sum()
-                    ig = is_greedy[j][:l].astype(mx.int32).sum()
-                else:  # subsequence score
-                    start, end = score_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
-
-                results.append((score.item(), ig.item(), length))
+            scores, lengths, is_greedy = self._score_fn(batch)
+
+            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]
 
-        return results
+            scores = (masks * scores).sum(axis=-1)
+            is_greedy = (masks * is_greedy).sum(axis=-1)
+
+            all_scores[i : i + self._batch_size] = scores
+            all_is_greedy[i : i + self._batch_size] = is_greedy == lengths
+
+        return all_scores, all_is_greedy
 
     def _tokenize(self, texts):
         return [
@@ -203,23 +206,20 @@ def loglikelihood(self, requests) -> list[tuple[float, bool]]:
         shortened = [shortened[i] for i in sorted_indices]
         completion_spans = [completion_spans[i] for i in sorted_indices]
 
-        group = mx.distributed.init() if mx.distributed.is_available() else None
-        if group is not None:
-            # 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()]
+        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,
         )
 
-        scores = mx.array([r[0] for r in results])
-        is_greedy = mx.array([r[1] == r[2] for r in results])
-
         # all gather the results across groups
-        if group is not None:
+        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))))
@@ -237,7 +237,15 @@ def loglikelihood(self, requests) -> list[tuple[float, bool]]:
         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
@@ -275,7 +283,8 @@ def loglikelihood_rolling(self, requests) -> list[float]:
             "Estimating loglikelihood rolling for %d sequences." % len(requests)
         )
         inputs = self._tokenize([req.args[0] for req in requests])
-        return [t[0] for t in self._loglikelihood(inputs)]
+        scores, _ = self._loglikelihood(inputs)
+        return scores.tolist()
 
     def generate_until(self, requests) -> list[str]:
         """Generate greedily until a stopping sequence
@@ -338,7 +347,7 @@ def main():
     )
     parser.add_argument(
         "--limit",
-        default=1.0,
+        default=None,
         help="Limit the number of examples per task.",
         type=float,
     )
@@ -352,11 +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()