[RL] logprob compute use the same method

paddlenlp/rl/models/ppo_model_utils.py

@@ -485,16 +485,14 @@
                 kl_loss_coeff=self.kl_loss_coeff,
                 loop_chunk_size=1024,
                 response_start=response_start,
-                use_actor_fused_loss=self.entropy_coeff <= 0,  # currently only support kunbo's fused head loss
+                use_actor_fused_loss=True,  # currently only support kunbo's fused head loss
                 temperature=self.temperature,
             )
             with paddle.no_grad():
                 self.info_buffer["kl_loss"] = (
                     kl_loss.detach() / self.kl_loss_coeff if self.kl_loss_coeff > 0 else paddle.to_tensor([0.0])
                 )
-                self.info_buffer["entropy_loss"] = (
-                    entropy_loss.detach() / self.entropy_coeff if self.entropy_coeff > 0 else paddle.to_tensor([0.0])
-                )
+                self.info_buffer["entropy_loss"] = entropy_loss.detach()
                 self.info_buffer["pure_policy_loss"] = (
                     pg_loss.detach() / self.pg_loss_coeff if self.pg_loss_coeff > 0 else paddle.to_tensor([0.0])
                 )
@@ -716,6 +714,7 @@
         clip_range_score: float,
         kl_loss_coeff: float,  # KL loss coefficient
         temperature: float,
+        print_entropy_loss: bool = True,
     ):
         """
         forward function of ActorFusedLoss
@@ -813,11 +812,11 @@
             token_end_idx = min(i + loop_chunk_size, n_tokens)
             hidden_states_chunk = hidden_states[token_start_idx:token_end_idx]
             labels_chunk = labels[token_start_idx:token_end_idx]
-            old_log_probs_chunk = old_log_probs[token_start_idx:token_end_idx]
+            mask_chunk = loss_mask[token_start_idx:token_end_idx]
+            old_log_probs_chunk = old_log_probs[token_start_idx:token_end_idx] * mask_chunk
             if kl_loss_coeff > 0:
-                ref_log_chunk = ref_log_probs[token_start_idx:token_end_idx]
+                ref_log_chunk = ref_log_probs[token_start_idx:token_end_idx] * mask_chunk
             advantages_chunk = advantages[token_start_idx:token_end_idx]
-            mask_chunk = loss_mask[token_start_idx:token_end_idx]
 
             # Calculate the current logits_chunk,  not fused linear
             logits_chunk_cast = paddle.matmul(hidden_states_chunk, lm_head_weight_cast, transpose_y=transpose_y)
@@ -841,13 +840,14 @@
                 token_loss_chunk = F.cross_entropy(logits_chunk, labels_chunk, reduction="none")
                 softmax_output_chunk = F.softmax(logits_chunk, axis=-1)
 
-            log_probs_chunk = -token_loss_chunk.squeeze(axis=-1)
+            log_probs_chunk = -token_loss_chunk.squeeze(axis=-1) * mask_chunk
             # calculate gradient, note sign
             grad_logits_chunk = labels_one_hot.astype("float32") - softmax_output_chunk
             grad_logits_chunk = grad_logits_chunk.astype(dtype)
 
             # ratio
             ratio_chunk = paddle.exp(log_probs_chunk - old_log_probs_chunk)
+
             clipped_ratio_chunk = paddle.clip(
                 ratio_chunk, min=1.0 - clip_range_ratio_low, max=1.0 + clip_range_ratio_high
             )
@@ -892,6 +892,7 @@
             if kl_loss_coeff > 0:
                 # [3] kl loss
                 delta_chunk = ref_log_chunk - log_probs_chunk
+
                 exp_delta_chunk = paddle.exp(delta_chunk)
                 kl_loss_estimate_chunk = exp_delta_chunk - delta_chunk - 1
                 kl_loss_clipped_chunk = (
@@ -912,6 +913,17 @@
                 )
                 d_loss_d_logits_chunk += d_kl_log_probs_chunk.unsqueeze(-1) * d_log_probs_d_logits_chunk
 
+            if print_entropy_loss:
+                # [2] entropy loss
+                log_prob_chunk = paddle.log(paddle.clip(softmax_output_chunk, min=1e-12))
+                entropy_loss_chunk = -(softmax_output_chunk * log_prob_chunk).sum(axis=-1) * mask_chunk
+                # entropy_loss_chunk shape is [bs, seqlen, vocab_size // tensor_parallel_degree], do all_reduce sum here
+                if tensor_parallel_degree > 1 and tensor_parallel_output:
+                    paddle.distributed.all_reduce(
+                        entropy_loss_chunk, op=paddle.distributed.ReduceOp.SUM, group=model_parallel_group
+                    )
+                total_entropy_loss += entropy_loss_chunk.sum() / divisor
+
             # grads
             if grad_hidden_states is not None:
                 grad_hidden_states[token_start_idx:token_end_idx] = paddle.matmul(

paddlenlp/rl/trainer/actor_trainer.py

@@ -16,6 +16,9 @@
 
 import numpy as np
 import paddle
+import paddle.nn.functional as F
+from paddle.distributed import fleet
+from paddle.distributed.fleet.layers.mpu import mp_ops
 from paddle.distributed.fleet.meta_parallel import ParallelCrossEntropy
 
 from ..models.ppo_model_utils import (
@@ -57,6 +60,13 @@
         Raises:
             None.
         """
+        if self.args.use_fused_head_and_loss_fn:
+            return self.compute_fused_logprob(
+                input_ids=input_ids,
+                position_ids=position_ids,
+                **kwargs,
+            )
+
         log_probs_list = []
         batch_size, sequence_length = input_ids.shape
         per_device_logprob_batch_size = self.args.per_device_logprob_batch_size
@@ -147,6 +157,153 @@
 
         return paddle.concat(log_probs_list, axis=0)
 
+    def compute_fused_logprob(
+        self, input_ids: paddle.Tensor, position_ids: paddle.Tensor = None, loop_chunk_size=1024, **kwargs
+    ):
+        log_probs_list = []
+        batch_size, sequence_length = input_ids.shape
+        per_device_logprob_batch_size = self.args.per_device_logprob_batch_size
+        num_batches = (batch_size + per_device_logprob_batch_size - 1) // per_device_logprob_batch_size
+
+        # Pipe model outputs a logits tensor with LMHead, while non-pipe model
+        # outputs a tuple with logits tensor as the only one element.
+        startend_row_indices = create_startend_row_indices(input_ids, self.tokenizer.pad_token_id)
+        response_start = kwargs["prompt"].shape[-1] - 1 if "prompt" in kwargs else 0
+
+        num_embeddings = self.model.config.vocab_size
+        tensor_parallel_degree = self.model.config.tensor_parallel_degree
+        tensor_parallel_output = self.model.config.tensor_parallel_output
+
+        for i in range(num_batches):
+            # Calculate the start and end indices for the current batch
+            start_index = i * per_device_logprob_batch_size
+            end_index = min(start_index + per_device_logprob_batch_size, batch_size)
+
+            # Extract the current batch
+            current_input_ids = input_ids[start_index:end_index]
+            current_startend_row_indices = (
+                startend_row_indices[start_index:end_index] if startend_row_indices is not None else None
+            )
+            current_position_ids = position_ids[start_index:end_index] if position_ids is not None else None
+            current_labels = current_input_ids[:, response_start + 1 :]
+
+            if self.args.use_remove_padding:
+                from ..utils.bert_padding import prepare_flashmask_inputs
+
+                update_inputs = prepare_flashmask_inputs(
+                    current_input_ids,
+                    current_position_ids,
+                    self.tokenizer.pad_token_id,
+                    self.model.config.sequence_parallel,
+                    self.model.config.tensor_parallel_degree,
+                )
+                current_input_ids = update_inputs["input_ids"]
+                current_position_ids = update_inputs["position_ids"]
+                current_startend_row_indices = update_inputs["attn_mask_startend_row_indices"]
+                indices = update_inputs["indices"]
+                raw_input_shape = update_inputs["raw_input_shape"]
+                pad_size = update_inputs["pad_size"]
+
+            # NOTE: for use_fused_head_and_loss_fn
+            self.model.training = True
+            hidden_states, lm_head_weight, lm_head_bias, transpose_y = self.model(
+                current_input_ids,
+                position_ids=current_position_ids,
+                attn_mask_startend_row_indices=current_startend_row_indices,
+            )
+            self.model.training = False
+
+            if self.args.use_remove_padding:
+                if pad_size > 0:
+                    hidden_states = hidden_states[:, :-pad_size]
+
+                from ..utils.bert_padding import pad_input
+
+                hidden_states = pad_input(
+                    hidden_states.squeeze(0), indices, batch=raw_input_shape[0], seqlen=raw_input_shape[1]
+                ).contiguous()
+
+            if self.args.use_fp32_compute and hidden_states.dtype != paddle.float32:
+                hidden_states = hidden_states.cast(paddle.float32)
+                lm_head_weight = lm_head_weight.cast(paddle.float32)
+                if lm_head_bias is not None:
+                    lm_head_bias = lm_head_bias.cast(paddle.float32)
+
+            # Recover
+            hidden_states = hidden_states[:, response_start:-1, :]
+            dtype = hidden_states.dtype
+            original_shape = hidden_states.shape
+            if tensor_parallel_degree > 1:
+                assert tensor_parallel_output, (
+                    "When tensor_parallel_degree > 1 and use_fused_head_and_loss_fn, "
+                    "tensor_parallel_output needs to be set to True."
+                )
+            # Parallel Configuration
+            if tensor_parallel_degree > 1 and tensor_parallel_output:
+                hcg = fleet.get_hybrid_communicate_group()
+                model_parallel_group = hcg.get_model_parallel_group()
+                tensor_parallel_degree = hcg.get_model_parallel_world_size()
+
+            # reshape
+            hidden_states = hidden_states.reshape([-1, original_shape[-1]])
+            labels = current_labels.reshape([-1])
+
+            n_tokens = hidden_states.shape[0]
+            n_classes = lm_head_weight.shape[0] if transpose_y else lm_head_weight.shape[1]
+
+            # convert dtype of weights and biases of lm_head
+            lm_head_weight_cast = lm_head_weight.astype(dtype)
+            if lm_head_bias is not None:
+                lm_head_bias_cast = lm_head_bias.astype(dtype)
+
+            # use indices to distinguish the devices.
+            if tensor_parallel_degree > 1 and tensor_parallel_output:
+                rank = hcg.get_model_parallel_rank()
+                per_part_size = num_embeddings // tensor_parallel_degree
+                indices = paddle.arange(
+                    rank * per_part_size,
+                    rank * per_part_size + n_classes,
+                    dtype=labels.dtype,
+                ).unsqueeze(0)
+            else:
+                indices = paddle.arange(num_embeddings, dtype=labels.dtype).unsqueeze(0)
+
+            log_prob_chunks = []
+            for ci in range(0, n_tokens, loop_chunk_size):
+                token_start_idx = ci
+                token_end_idx = min(ci + loop_chunk_size, n_tokens)
+                hidden_states_chunk = hidden_states[token_start_idx:token_end_idx]
+                labels_chunk = labels[token_start_idx:token_end_idx]
+
+                # Calculate the current logits_chunk,  not fused linear
+                logits_chunk_cast = paddle.matmul(hidden_states_chunk, lm_head_weight_cast, transpose_y=transpose_y)
+                if lm_head_bias is not None:
+                    logits_chunk_cast += lm_head_bias_cast
+
+                logits_chunk = logits_chunk_cast.astype("float32")
+                logits_chunk = logits_chunk / self.args.temperature
+
+                # rewritten as cross entropy
+                if tensor_parallel_degree > 1 and tensor_parallel_output:
+                    token_loss_chunk = mp_ops._c_softmax_with_cross_entropy(
+                        logits_chunk,
+                        labels_chunk,
+                        group=model_parallel_group,
+                        return_softmax=False,
+                    )
+                else:
+                    token_loss_chunk = F.cross_entropy(logits_chunk, labels_chunk, reduction="none")
+                log_prob_chunk = -token_loss_chunk.squeeze(axis=-1)
+                log_prob_chunks.append(log_prob_chunk)
+
+            log_probs = paddle.concat(log_prob_chunks, axis=-1).reshape(original_shape[:-1])
+            log_probs_list.append(log_probs)
+
+            log_prob_chunks = None
+            paddle.device.cuda.empty_cache()
+
+        return paddle.concat(log_probs_list, axis=0)
+
     def update_actor(self, rl_batch: Dict[str, paddle.Tensor]) -> Dict[str, Any]:
         # inputs shared by policy and value trainer
         input_ids = rl_batch["input_ids"].contiguous()  # length: src+tgt

paddlenlp/rl/trainer/ppo_trainer.py

@@ -1473,15 +1473,16 @@
                 if self.args.balance_batch:
                     batch = self._balance_batch(batch)
 
-                # step 2-3: compute logprob for rollout data
-                with TimerScope(self.timers, RolloutStages.ROLLOUT_LOGPROB):
-                    with reload_and_offload_scope(self, self.reference_model):
-                        with TimerScope(self.timers, RolloutStages.ROLLOUT_REF_LOGPROB):
-                            batch["ref_log_probs"] = self.reference_trainer.compute_logprob(**batch)
-
-                    with reload_and_offload_scope(self, self.actor_model):
-                        with TimerScope(self.timers, RolloutStages.ROLLOUT_OLD_LOGPROB):
-                            batch["log_probs"] = self.actor_trainer.compute_logprob(**batch)
+                with self.autocast_smart_context_manager():
+                    # step 2-3: compute logprob for rollout data
+                    with TimerScope(self.timers, RolloutStages.ROLLOUT_LOGPROB):
+                        with reload_and_offload_scope(self, self.reference_model):
+                            with TimerScope(self.timers, RolloutStages.ROLLOUT_REF_LOGPROB):
+                                batch["ref_log_probs"] = self.reference_trainer.compute_logprob(**batch)
+
+                        with reload_and_offload_scope(self, self.actor_model):
+                            with TimerScope(self.timers, RolloutStages.ROLLOUT_OLD_LOGPROB):
+                                batch["log_probs"] = self.actor_trainer.compute_logprob(**batch)
 
                 # step 2-2: compute reward for rollout data
                 with TimerScope(