[Feature] Entropy calculation support (#5692)

* support entropy

* fix bug

---------

Co-authored-by: YuBaoku <49938469+EmmonsCurse@users.noreply.github.com>

fastdeploy/model_executor/entropy_utils.py

@@ -0,0 +1,99 @@
+"""
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+import paddle
+
+from fastdeploy.utils import data_processor_logger
+
+
+def calculate_logits_entropy(logits, share_inputs, temperature):
+    real_bsz = share_inputs["seq_lens_this_time"].shape[0]
+    real_seq_lens = paddle.where(
+        share_inputs["seq_lens_encoder"][:real_bsz].squeeze(1) != 0,
+        paddle.ones([1], dtype="int32"),
+        share_inputs["seq_lens_this_time"].squeeze(1),
+    )
+
+    def get_entropy(logits):
+        a0 = logits - paddle.max(logits, axis=-1, keepdim=True)
+        ea0 = paddle.exp(a0)
+        z0 = paddle.sum(ea0, axis=-1, keepdim=True)
+        p0 = ea0 / z0
+        return paddle.sum(p0 * (paddle.log(z0) - a0), axis=-1)
+
+    batch_indices = paddle.arange(real_bsz, dtype="int32")
+    batch_id_per_token = paddle.repeat_interleave(batch_indices, real_seq_lens)
+    for i in range(logits.shape[0]):
+        if temperature[batch_id_per_token[i]] > 0 and temperature[batch_id_per_token[i]] != 1.0:
+            logits[i] = logits[i].scale_(1 / temperature[batch_id_per_token[i]])
+
+    entropy_tensor = get_entropy(logits)
+    entropy = entropy_tensor.tolist()
+
+    for i in range(real_bsz):
+        for _ in range(real_seq_lens[i]):
+            share_inputs["entropy_list"][i].append(entropy.pop(0))
+        if share_inputs["stop_flags"][i] and len(share_inputs["entropy_list"][i]) != 0:
+            data_processor_logger.info(
+                f"req_id: {share_inputs['req_ids'][i]}, entropy: {sum(share_inputs['entropy_list'][i])/len(share_inputs['entropy_list'][i])}"
+            )
+            share_inputs["entropy_list"][i] = []
+
+
+def speculate_calculate_logits_entropy(logits, share_inputs, temperature):
+    # get accepted logits
+    real_bsz = share_inputs["seq_lens_this_time"].shape[0]
+    total_accepted_num = paddle.sum(share_inputs["accept_num"])
+    real_seq_lens = paddle.where(
+        share_inputs["seq_lens_encoder"][:real_bsz].squeeze(1) != 0,
+        paddle.ones([1], dtype="int32"),
+        share_inputs["seq_lens_this_time"].squeeze(1),
+    )
+    seq_start_idx = paddle.concat([paddle.zeros([1], dtype="int32"), paddle.cumsum(real_seq_lens, dtype="int32")])
+    repeated_starts = paddle.repeat_interleave(seq_start_idx[:-1], share_inputs["accept_num"][:real_bsz])
+    offsets = paddle.concat([paddle.arange(share_inputs["accept_num"][i].item()) for i in range(real_bsz)]).astype(
+        "int32"
+    )
+    accepted_idx = repeated_starts + offsets
+
+    accepted_logits = paddle.empty([total_accepted_num, logits.shape[1]], dtype=logits.dtype)
+    for i in range(total_accepted_num):
+        accepted_logits[i] = logits[accepted_idx[i]]
+
+    def get_entropy(logits):
+        a0 = logits - paddle.max(logits, axis=-1, keepdim=True)
+        ea0 = paddle.exp(a0)
+        z0 = paddle.sum(ea0, axis=-1, keepdim=True)
+        p0 = ea0 / z0
+        return paddle.sum(p0 * (paddle.log(z0) - a0), axis=-1)
+
+    batch_indices = paddle.arange(share_inputs["accept_num"].shape[0], dtype="int32")
+    batch_id_per_token = paddle.repeat_interleave(batch_indices, share_inputs["accept_num"])
+    for i in range(accepted_logits.shape[0]):
+        if temperature[batch_id_per_token[i]] > 0 and temperature[batch_id_per_token[i]] != 1.0:
+            accepted_logits[i] = accepted_logits[i].scale_(1 / temperature[batch_id_per_token[i]])
+
+    entropy_tensor = get_entropy(accepted_logits)
+    entropy = entropy_tensor.tolist()
+
+    for i in range(real_bsz):
+        for _ in range(share_inputs["accept_num"][i]):
+            share_inputs["entropy_list"][i].append(entropy.pop(0))
+        if share_inputs["stop_flags"][i] and len(share_inputs["entropy_list"][i]) != 0:
+            data_processor_logger.info(
+                f"req_id: {share_inputs['req_ids'][i]}, entropy: {sum(share_inputs['entropy_list'][i])/len(share_inputs['entropy_list'][i])}"
+            )
+            share_inputs["entropy_list"][i] = []