Revert "[Feature] Support computing entropy with fastdeploy runner (#7954)" (#7994)

This reverts commit 529ec9e.

Author: rain7996

fastdeploy/model_executor/entropy_utils.py

@@ -14,14 +14,13 @@
 # limitations under the License.
 """
 
-import os
-
 import paddle
 
 from fastdeploy.utils import data_processor_logger
 
 
 def get_entropy(logits):
+    # Check for -inf values in logits
     if paddle.any(paddle.isinf(logits) & (logits < 0)):
         data_processor_logger.debug("Detected -inf values in logits, clipping to minimum value")
         logits = paddle.clip(logits, min=1e-9)
@@ -33,36 +32,7 @@ def get_entropy(logits):
     return paddle.sum(p0 * (paddle.log(z0) - a0), axis=-1)
 
 
-def _log_entropy(share_inputs, i):
-    elist = share_inputs["entropy_list"][i]
-    data_processor_logger.info(
-        f"req_id: {share_inputs['req_ids'][i]}, entropy: {sum(elist)/len(elist)}, steps: {len(elist)}, all_values: {elist}"
-    )
-    share_inputs["entropy_list"][i] = []
-
-
-# ==============================================================================
-# ernie5_model_runner path (original logic from commit 361a310)
-# ==============================================================================
-
-
 def calculate_logits_entropy(logits, share_inputs, temperature):
-    use_fd_runner = os.environ.get("EB5_ENABLE_FD_RUNNER", "0") == "1"
-    if use_fd_runner:
-        calculate_logits_entropy_fd(logits, share_inputs, temperature)
-    else:
-        calculate_logits_entropy_ori(logits, share_inputs, temperature)
-
-
-def speculate_calculate_logits_entropy(logits, share_inputs, temperature):
-    use_fd_runner = os.environ.get("EB5_ENABLE_FD_RUNNER", "0") == "1"
-    if use_fd_runner:
-        speculate_calculate_logits_entropy_fd(logits, share_inputs, temperature)
-    else:
-        speculate_calculate_logits_entropy_ori(logits, share_inputs, temperature)
-
-
-def calculate_logits_entropy_ori(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,
@@ -87,10 +57,13 @@ def calculate_logits_entropy_ori(logits, share_inputs, temperature):
             and share_inputs["seq_lens_decoder"][i] != 0
             and len(share_inputs["entropy_list"][i]) != 0
         ):
-            _log_entropy(share_inputs, i)
+            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_ori(logits, share_inputs, temperature):
+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"])
@@ -127,128 +100,7 @@ def speculate_calculate_logits_entropy_ori(logits, share_inputs, temperature):
             and share_inputs["seq_lens_decoder"][i] != 0
             and len(share_inputs["entropy_list"][i]) != 0
         ):
-            _log_entropy(share_inputs, i)
-
-
-# ==============================================================================
-# gpu_model_runner (FD runner) path
-# ==============================================================================
-
-
-def calculate_logits_entropy_fd(logits, share_inputs, temperature):
-    real_bsz = share_inputs["seq_lens_this_time"].shape[0]
-    seq_lens_encoder = share_inputs["seq_lens_encoder"][:real_bsz]
-    seq_lens_this_time = share_inputs["seq_lens_this_time"]
-    if seq_lens_encoder.ndim == 2:
-        seq_lens_encoder = seq_lens_encoder.squeeze(1)
-    if seq_lens_this_time.ndim == 2:
-        seq_lens_this_time = seq_lens_this_time.squeeze(1)
-    real_seq_lens = paddle.where(
-        seq_lens_encoder != 0,
-        paddle.ones([1], dtype="int32"),
-        seq_lens_this_time,
-    )
-
-    for i in range(real_bsz):
-        if int(real_seq_lens[i]) == 0:
-            continue
-        t = temperature[i]
-        if t > 0 and t != 1.0:
-            logits[i] = logits[i].scale_(1 / t)
-
-    entropy_tensor = get_entropy(logits[:real_bsz])
-
-    for i in range(real_bsz):
-        if int(real_seq_lens[i]) == 0:
-            continue
-        entropy_val = float(entropy_tensor[i])
-        share_inputs["entropy_list"][i].append(entropy_val)
-        if (
-            share_inputs["stop_flags"][i]
-            and share_inputs["seq_lens_decoder"][i] != 0
-            and len(share_inputs["entropy_list"][i]) != 0
-        ):
-            _log_entropy(share_inputs, i)
-
-
-def speculate_calculate_logits_entropy_fd(logits, share_inputs, temperature):
-    real_bsz = share_inputs["seq_lens_this_time"].shape[0]
-    total_accepted_num = int(paddle.sum(share_inputs["accept_num"][:real_bsz]))
-
-    if total_accepted_num == 0:
-        for i in range(real_bsz):
-            if (
-                share_inputs["stop_flags"][i]
-                and share_inputs["seq_lens_decoder"][i] != 0
-                and len(share_inputs["entropy_list"][i]) != 0
-            ):
-                _log_entropy(share_inputs, i)
-        return
-
-    seq_lens_encoder = share_inputs["seq_lens_encoder"][:real_bsz]
-    seq_lens_this_time = share_inputs["seq_lens_this_time"]
-    if seq_lens_encoder.ndim == 2:
-        seq_lens_encoder = seq_lens_encoder.squeeze(1)
-    if seq_lens_this_time.ndim == 2:
-        seq_lens_this_time = seq_lens_this_time.squeeze(1)
-    real_seq_lens = paddle.where(
-        seq_lens_encoder != 0,
-        paddle.ones([1], dtype="int32"),
-        seq_lens_this_time,
-    )
-    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.index_select(logits, accepted_idx, axis=0)
-
-    batch_indices = paddle.arange(real_bsz, dtype="int32")
-    batch_id_per_token = paddle.repeat_interleave(batch_indices, share_inputs["accept_num"][:real_bsz])
-    temp_per_token = temperature[batch_id_per_token].flatten()
-    scale = paddle.where(
-        temp_per_token > 0,
-        1.0 / temp_per_token,
-        paddle.ones_like(temp_per_token),
-    )
-    accepted_logits = accepted_logits * scale.unsqueeze(-1)
-
-    entropy_tensor = get_entropy(accepted_logits)
-    entropy = entropy_tensor.tolist()
-
-    idx = 0
-    for i in range(real_bsz):
-        accept_count = int(share_inputs["accept_num"][i])
-        if accept_count > 0:
-            for _ in range(accept_count):
-                e_val = entropy[idx]
-                share_inputs["entropy_list"][i].append(e_val)
-                idx += 1
-        if (
-            share_inputs["stop_flags"][i]
-            and share_inputs["seq_lens_decoder"][i] != 0
-            and len(share_inputs["entropy_list"][i]) != 0
-        ):
-            _log_entropy(share_inputs, i)
-
-
-# ==============================================================================
-# Common utility
-# ==============================================================================
-
-
-def flush_entropy_on_stop(share_inputs):
-    """
-    Flush entropy for requests whose stop_flags became True after entropy calculation.
-    Called after unified_update_model_status which sets stop_flags for max_dec_len.
-    """
-    real_bsz = share_inputs["seq_lens_this_time"].shape[0]
-    for i in range(real_bsz):
-        if (
-            share_inputs["stop_flags"][i]
-            and share_inputs["seq_lens_decoder"][i] != 0
-            and len(share_inputs["entropy_list"][i]) != 0
-        ):
-            _log_entropy(share_inputs, i)
+            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] = []

fastdeploy/model_executor/pre_and_post_process.py

@@ -107,7 +107,6 @@
 
 from fastdeploy.model_executor.entropy_utils import (
     calculate_logits_entropy,
-    flush_entropy_on_stop,
     speculate_calculate_logits_entropy,
 )
 from fastdeploy.model_executor.layers.moe.routing_indices_cache import (
@@ -521,9 +520,6 @@ def post_process_speculate(
         model_output.max_dec_len,  # max_dec_len
     )
 
-    if enable_entropy:
-        flush_entropy_on_stop(share_inputs)
-
 
 def save_output_speculate(
     sampler_output: SamplerOutput,

fastdeploy/worker/gpu_model_runner.py

@@ -1254,7 +1254,7 @@ def _dummy_prefill_inputs(self, input_length_list: List[int], max_dec_len_list:
             self.share_inputs["block_tables"][idx : idx + 1, :block_num] = np.arange(
                 idx * block_num, (idx + 1) * block_num, 1
             )
-        self.share_inputs["seq_lens_this_time"] = self.share_inputs["seq_lens_this_time_buffer"][:batch_size]
+        self.share_inputs["seq_lens_this_time"] = self.share_inputs["seq_lens_this_time_buffer"]
 
     def _prepare_inputs(self, cached_token_num=-1, cached_real_bsz=-1, is_dummy_or_profile_run=False) -> None:
         """Prepare the model inputs"""
@@ -2416,7 +2416,6 @@ def execute_model_overlap(
         model_inputs, p_done_idxs, token_num_event = self._preprocess(
             model_forward_batch, num_running_requests, self._cached_launch_token_num, self._cached_real_bsz
         )
-
         model_output = self._execute(model_inputs)
         # save output (last batch)
         if self._cached_model_output_data is not None: