feat: support batch input in generate()

The `prompt` argument can now be either a `str` or `list[str]`.

The change to `generate()` is backwards-compatible.

The changes to `generate_step()`, `top_p_sampling()`, and
`min_p_sampling()` are backwards-incompatible in order to unify shapes;
this could be changed by adding a few if-statements, if preferred.

llms/mlx_lm/sample_utils.py

@@ -26,7 +26,10 @@ def min_p_sampling(
             0.99-0.8 range.
         min_tokens_to_keep (int, optional): Minimum number of tokens that cannot
             be filtered. Default: ``1``.
-
+        temperature: Temperature parameter for softmax distribution reshaping.
+    Returns:
+        token(s) selected based on the min-p criterion.
+        Shape: same as logits, but with the last dimension having size 1.
     """
     if not (0 <= min_p <= 1.0):
         raise ValueError(
@@ -39,14 +42,14 @@ def min_p_sampling(
     # reference implementation: https://github.com/huggingface/transformers/blob/main/src/transformers/generation/logits_process.py#L531-L605
 
     # Softmax probabilities
-    probs = mx.softmax(logits * (1 / temperature), axis=-1)
+    probs = mx.softmax(logits / temperature, axis=-1)
 
     # Indices sorted in decreasing order
-    sorted_indices = mx.argsort(-logits).squeeze(0)
-    sorted_probs = probs[..., sorted_indices]
+    sorted_indices = mx.argsort(-logits)
+    sorted_probs = mx.take_along_axis(probs, sorted_indices, axis=-1)
 
     # Top probability
-    top_probs = probs[..., sorted_indices[0]]
+    top_probs = mx.expand_dims(sorted_probs[..., 0], axis=-1)
 
     # Calculate the min_p threshold
     scaled_min_p = min_p * top_probs
@@ -58,43 +61,54 @@ def min_p_sampling(
     # Create pool of tokens with probability less than scaled min_p
     selected_probs = mx.where(tokens_to_remove, 0, sorted_probs)
 
-    # Return sampled token
-    sorted_token = mx.random.categorical(mx.log(selected_probs))
-    return sorted_indices[sorted_token]
+    # Return sampled token(s)
+    sampled_indices = mx.random.categorical(mx.log(selected_probs))
+    tokens = mx.take_along_axis(
+        sorted_indices, mx.expand_dims(sampled_indices, axis=-1), axis=-1
+    )
+    return tokens.squeeze(-1)
 
 
 @partial(mx.compile, inputs=mx.random.state, outputs=mx.random.state)
-def top_p_sampling(logits: mx.array, top_p: float, temperature: float) -> mx.array:
+def top_p_sampling(
+    logits: mx.array, top_p: float, temperature: float, axis: int = -1
+) -> mx.array:
     """
     Apply top-p (nucleus) sampling to logits.
 
     Args:
         logits: The logits from the model's output.
         top_p: The cumulative probability threshold for top-p filtering.
         temperature: Temperature parameter for softmax distribution reshaping.
+        axis: The axis along which to apply top-p sampling.
     Returns:
-        token selected based on the top-p criterion.
+        token(s) selected based on the top-p criterion.
     """
-    # referenced implementation from https://github.com/huggingface/transformers/blob/main/src/transformers/generation/logits_process.py#L449-L460
-    probs = mx.softmax(logits * (1 / temperature), axis=-1)
+    # Apply temperature and compute softmax
+    probs = mx.softmax(logits / temperature, axis=axis)
 
-    # sort probs in ascending order
-    sorted_indices = mx.argsort(probs, axis=-1)
-    sorted_probs = probs[..., sorted_indices.squeeze(0)]
+    # Sort probs in descending order
+    sorted_indices = mx.argsort(-probs, axis=axis)
+    sorted_probs = mx.take_along_axis(probs, sorted_indices, axis=axis)
 
-    cumulative_probs = mx.cumsum(sorted_probs, axis=-1)
+    # Compute cumulative probabilities
+    cumulative_probs = mx.cumsum(sorted_probs, axis=axis)
 
-    # select tokens with cumulative probs below threshold
-    top_probs = mx.where(
-        cumulative_probs > 1 - top_p,
-        sorted_probs,
-        0,
-    )
+    # Create a mask for probs above the threshold
+    mask = cumulative_probs <= top_p
+
+    # Apply the mask to the sorted probabilities
+    masked_probs = sorted_probs * mask
 
-    sorted_token = mx.random.categorical(mx.log(top_probs))
-    token = sorted_indices.squeeze(0)[sorted_token]
+    # Sample from the normalized probabilities
+    sampled_indices = mx.random.categorical(mx.log(masked_probs), axis=axis)
+
+    # Gather the original token indices
+    tokens = mx.take_along_axis(
+        sorted_indices, mx.expand_dims(sampled_indices, axis=axis), axis=axis
+    )
 
-    return token
+    return tokens.squeeze(axis)
 
 
 @partial(mx.compile, inputs=mx.random.state, outputs=mx.random.state)

llms/mlx_lm/server.py

@@ -410,7 +410,7 @@ def handle_completion(
         top_tokens = []
         for (token, logprobs), _ in zip(
             generate_step(
-                prompt=prompt,
+                prompts=prompt[None],
                 model=self.model,
                 temp=self.temperature,
                 top_p=self.top_p,
@@ -420,6 +420,8 @@ def handle_completion(
             ),
             range(self.max_tokens),
         ):
+            token = token.item()
+            logprobs = logprobs.squeeze(0)
             detokenizer.add_token(token)
             logging.debug(detokenizer.text)
             tokens.append(token)
@@ -497,7 +499,7 @@ def handle_stream(
 
         for (token, _), _ in zip(
             generate_step(
-                prompt=prompt,
+                prompts=prompt[None],
                 model=self.model,
                 temp=self.temperature,
                 top_p=self.top_p,
@@ -506,6 +508,7 @@ def handle_stream(
             ),
             range(self.max_tokens),
         ):
+            token = token.item()
             detokenizer.add_token(token)
             logging.debug(detokenizer.text)
             tokens.append(token)