make_sampler creates sampler chain with all sampling parameters (#1330)

* top_p refactor

* top_k and min_p refactor

* Create sampler chain

* Remove unnecessary mx.where

* Use mx.allclose

llms/mlx_lm/sample_utils.py

@@ -35,14 +35,25 @@ def make_sampler(
     """
     if temp == 0:
         return lambda x: mx.argmax(x, axis=-1)
-    elif top_p > 0 and top_p < 1.0:
-        return lambda x: top_p_sampling(x, top_p, temp)
-    elif min_p != 0.0:
-        return lambda x: min_p_sampling(x, min_p, min_tokens_to_keep, temp)
-    elif top_k > 0:
-        return lambda x: top_k_sampling(x, top_k, temp)
-    else:
-        return lambda x: categorical_sampling(x, temp)
+
+    # Create sampler chain
+    sampling_methods = []
+    if top_k > 0:
+        sampling_methods.append(lambda x: apply_top_k(x, top_k))
+    if top_p > 0 and top_p < 1.0:
+        sampling_methods.append(lambda x: apply_top_p(x, top_p))
+    if min_p != 0.0:
+        sampling_methods.append(lambda x: apply_min_p(x, min_p, min_tokens_to_keep))
+
+    # Apply the sampling methods
+    def sampler(logits):
+        for method in sampling_methods:
+            logits = method(logits)
+
+        # Return the sampled token
+        return categorical_sampling(logits, temp)
+
+    return sampler
 
 
 def make_logits_processors(
@@ -85,10 +96,9 @@ def make_logits_processors(
 
 
 @partial(mx.compile, inputs=mx.random.state, outputs=mx.random.state)
-def top_k_sampling(
+def apply_top_k(
     logprobs: mx.array,
     top_k: int,
-    temperature=1.0,
 ) -> mx.array:
     """
     Sample from only the top K tokens ranked by probability.
@@ -103,20 +113,18 @@ def top_k_sampling(
             f"`top_k` has to be an integer in the (0, {vocab_size}] interval,"
             f" but is {top_k}."
         )
-    logprobs = logprobs * (1 / temperature)
     mask_idx = mx.argpartition(-logprobs, kth=top_k - 1, axis=-1)[..., top_k:]
     masked_logprobs = mx.put_along_axis(
         logprobs, mask_idx, mx.array(-float("inf"), logprobs.dtype), axis=-1
     )
-    return mx.random.categorical(masked_logprobs, axis=-1)
+    return masked_logprobs
 
 
 @partial(mx.compile, inputs=mx.random.state, outputs=mx.random.state)
-def min_p_sampling(
+def apply_min_p(
     logprobs: mx.array,
     min_p: float,
     min_tokens_to_keep: int = 1,
-    temperature=1.0,
 ) -> mx.array:
     """
     Apply min-p sampling to the logprobs.
@@ -144,8 +152,6 @@ def min_p_sampling(
         )
     # reference implementation: https://github.com/huggingface/transformers/blob/main/src/transformers/generation/logits_process.py#L531-L605
 
-    logprobs = logprobs * (1 / temperature)
-
     # Indices sorted in decreasing order
     sorted_indices = mx.argsort(-logprobs, axis=-1)
     sorted_logprobs = mx.take_along_axis(logprobs, sorted_indices, axis=-1)
@@ -163,25 +169,31 @@ def min_p_sampling(
     # Create pool of tokens with probability less than scaled min_p
     selected_logprobs = mx.where(tokens_to_remove, -float("inf"), sorted_logprobs)
 
-    # Return sampled tokens
-    sorted_tokens = mx.random.categorical(selected_logprobs, axis=-1)[:, None]
-    return mx.take_along_axis(sorted_indices, sorted_tokens, axis=-1).squeeze(1)
+    # Create a mapping to rearrange back to original indices
+    # Use argsort of sorted_indices to get the inverse permutation
+    inverse_indices = mx.argsort(sorted_indices, axis=-1)
+
+    # Rearrange selected_logprobs back to original order
+    original_order_logprobs = mx.take_along_axis(
+        selected_logprobs, inverse_indices, axis=-1
+    )
+
+    return original_order_logprobs
 
 
 @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 apply_top_p(logits: mx.array, top_p: float) -> 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.
     Returns:
         token 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)
+    probs = mx.softmax(logits, axis=-1)
 
     # sort probs in ascending order
     sorted_indices = mx.argsort(probs, axis=-1)
@@ -196,8 +208,15 @@ def top_p_sampling(logits: mx.array, top_p: float, temperature: float) -> mx.arr
         0,
     )
 
-    sorted_tokens = mx.random.categorical(mx.log(top_probs), axis=-1)[:, None]
-    return mx.take_along_axis(sorted_indices, sorted_tokens, axis=-1).squeeze(1)
+    # Create a mapping to rearrange back to original indices
+    # Use argsort of sorted_indices to get the inverse permutation
+    inverse_indices = mx.argsort(sorted_indices, axis=-1)
+
+    # Rearrange top_probs back to original order
+    original_order_probs = mx.take_along_axis(top_probs, inverse_indices, axis=-1)
+
+    # Convert back to logits and return
+    return mx.log(original_order_probs)
 
 
 @partial(mx.compile, inputs=mx.random.state, outputs=mx.random.state)