[pipeline] add pipeline support for all T5 models (#4310)

* complete policy for T5Model & T5ForConditionalGeneration

* modify function signature in forwards

* add forward for T5model

* add forward for T5ForConditionalGeneration

* fix a bug

* fix hidden_states transporting in decoder

* fix the passing of encoder_outputs

colossalai/shardformer/modeling/t5.py

@@ -1,11 +1,15 @@
-from functools import partial
-from types import MethodType
-from typing import Callable, Dict, List, Optional, Tuple, Union
+import warnings
+from typing import Dict, List, Optional, Tuple, Union
 
 import torch
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from torch.nn import CrossEntropyLoss
 from torch.utils.checkpoint import checkpoint
-from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions
+from transformers.modeling_outputs import (
+    BaseModelOutput,
+    BaseModelOutputWithPastAndCrossAttentions,
+    Seq2SeqLMOutput,
+    Seq2SeqModelOutput,
+)
 from transformers.models.t5.modeling_t5 import T5EncoderModel, T5ForConditionalGeneration, T5Model, T5Stack
 from transformers.utils import logging
 
@@ -198,14 +202,13 @@ class T5PipelineForwards:
             if use_cache is False or use_cache is None:
                 layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:]
             hidden_states, present_key_value_state = layer_outputs[:2]
-            # print(stage, len(layer_outputs), present_key_value_state.shape)
 
             # We share the position biases between the layers - the first layer store them
             # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights),
             # (cross-attention position bias), (cross-attention weights)
             position_bias = layer_outputs[2]
 
-            if self.is_decoder and encoder_hidden_states is not None:
+            if in_decoder and encoder_hidden_states is not None:
                 encoder_decoder_position_bias = layer_outputs[4 if output_attentions else 3]
             # append next layer key value states
             if use_cache:
@@ -238,6 +241,313 @@ class T5PipelineForwards:
                 'encoder_decoder_position_bias': encoder_decoder_position_bias
             }
 
+    @staticmethod
+    def t5_model_forward(
+        self: T5Model,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        decoder_input_ids: Optional[torch.LongTensor] = None,
+        decoder_attention_mask: Optional[torch.BoolTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        decoder_head_mask: Optional[torch.FloatTensor] = None,
+        cross_attn_head_mask: Optional[torch.Tensor] = None,
+        encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        decoder_inputs_embeds: Optional[torch.Tensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        stage_manager: Optional[PipelineStageManager] = None,
+        hidden_states: Optional[torch.FloatTensor] = None,
+        position_bias: Optional[torch.Tensor] = None,
+        encoder_decoder_position_bias: Optional[torch.Tensor] = None,
+        stage_index: Optional[List[int]] = None,
+        decoder_starting_stage: Optional[int] = None,
+    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]:
+
+        # This function is modified on the basis of transformers.models.t5.modeling_t5.T5Model.forward.
+        # Please refer to original code of transformers for more details.
+
+        __HEAD_MASK_WARNING_MSG = """
+        The input argument `head_mask` was split into two arguments `head_mask` and `decoder_head_mask`. Currently,
+        `decoder_head_mask` is set to copy `head_mask`, but this feature is deprecated and will be removed in future versions.
+        If you do not want to use any `decoder_head_mask` now, please set `decoder_head_mask = torch.ones(num_layers,
+        num_heads)`.
+        """
+
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        logger = logging.get_logger(__name__)
+
+        # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
+        if past_key_values:
+            logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.')
+            past_key_values = None
+        if output_attentions:
+            logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.')
+            output_attentions = False
+        if output_hidden_states:
+            logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.')
+            output_hidden_states = False
+        if use_cache:
+            logger.warning_once('use_cache=True is not supported for pipeline models at the moment.')
+            use_cache = False
+
+        # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
+        if head_mask is not None and decoder_head_mask is None:
+            if self.config.num_layers == self.config.num_decoder_layers:
+                warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
+                decoder_head_mask = head_mask
+
+        in_decoder = stage_manager.stage >= decoder_starting_stage
+
+        # Stage is in encoder, directly return the output of t5_stack_forward
+        if not in_decoder:
+            encoder_outputs = T5PipelineForwards.t5_stack_forward(
+                self.encoder,
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                inputs_embeds=inputs_embeds,
+                head_mask=head_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+                stage_manager=stage_manager,
+                hidden_states=hidden_states,
+                position_bias=position_bias,
+                encoder_decoder_position_bias=encoder_decoder_position_bias,
+                stage_index=stage_index,
+                decoder_starting_stage=decoder_starting_stage)
+            if stage_manager.stage == decoder_starting_stage - 1:
+                # last stage of encoder
+                return {'encoder_outputs': encoder_outputs}
+            else:
+                return encoder_outputs
+
+        at_last_decoder_stage = stage_manager.is_last_stage()
+        at_first_decoder_stage = stage_manager.stage == decoder_starting_stage
+
+        if encoder_outputs is None:
+            raise ValueError("Non-empty encoder_outputs should be passed in at decoder stages.")
+
+        encoder_hidden_states = encoder_outputs[0]
+        if return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+            encoder_outputs = BaseModelOutput(
+                last_hidden_state=encoder_outputs[0],
+                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+            )
+
+        # Stage is in decoder, we assume that the outputs of last stage of encoder will be passed in.
+        if not at_first_decoder_stage and hidden_states is None:
+            raise ValueError("If not at the first layer of decoder, non-empty hidden_states must be provided.")
+
+        # Decode
+        decoder_outputs = T5PipelineForwards.t5_stack_forward(
+            self.decoder,
+            input_ids=decoder_input_ids,
+            attention_mask=decoder_attention_mask,
+            inputs_embeds=decoder_inputs_embeds,
+            past_key_values=past_key_values,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=attention_mask,
+            head_mask=decoder_head_mask,
+            cross_attn_head_mask=cross_attn_head_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            hidden_states=hidden_states,
+            position_bias=position_bias,
+            encoder_decoder_position_bias=encoder_decoder_position_bias,
+            stage_index=stage_index,
+            decoder_starting_stage=decoder_starting_stage)
+
+        # Directly return outputs of overloaded T5Stack forward if not at last stage.
+        if not at_last_decoder_stage:
+            decoder_outputs['encoder_outputs'] = encoder_outputs    # encoder_outputs should be passed to the next stage
+            return decoder_outputs
+
+        if not return_dict:
+            return decoder_outputs + encoder_outputs
+
+        return Seq2SeqModelOutput(
+            last_hidden_state=decoder_outputs.last_hidden_state,
+            past_key_values=decoder_outputs.past_key_values,
+            decoder_hidden_states=decoder_outputs.hidden_states,
+            decoder_attentions=decoder_outputs.attentions,
+            cross_attentions=decoder_outputs.cross_attentions,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+            encoder_hidden_states=encoder_outputs.hidden_states,
+            encoder_attentions=encoder_outputs.attentions,
+        )
+
+    @staticmethod
+    def t5_for_conditional_generation_forward(
+        self: T5ForConditionalGeneration,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        decoder_input_ids: Optional[torch.LongTensor] = None,
+        decoder_attention_mask: Optional[torch.BoolTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        decoder_head_mask: Optional[torch.FloatTensor] = None,
+        cross_attn_head_mask: Optional[torch.Tensor] = None,
+        encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        stage_manager: Optional[PipelineStageManager] = None,
+        hidden_states: Optional[torch.FloatTensor] = None,
+        position_bias: Optional[torch.Tensor] = None,
+        encoder_decoder_position_bias: Optional[torch.Tensor] = None,
+        stage_index: Optional[List[int]] = None,
+        decoder_starting_stage: Optional[int] = None,
+    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
+
+        # This function is modified on the basis of transformers.models.t5.modeling_t5.T5ForConditionalGeneration.forward.
+        # Please refer to original code of transformers for more details.
+
+        __HEAD_MASK_WARNING_MSG = """
+        The input argument `head_mask` was split into two arguments `head_mask` and `decoder_head_mask`. Currently,
+        `decoder_head_mask` is set to copy `head_mask`, but this feature is deprecated and will be removed in future versions.
+        If you do not want to use any `decoder_head_mask` now, please set `decoder_head_mask = torch.ones(num_layers,
+        num_heads)`.
+        """
+
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        logger = logging.get_logger(__name__)
+
+        # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
+        if past_key_values:
+            logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.')
+            past_key_values = None
+        if output_attentions:
+            logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.')
+            output_attentions = False
+        if output_hidden_states:
+            logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.')
+            output_hidden_states = False
+        if use_cache:
+            logger.warning_once('use_cache=True is not supported for pipeline models at the moment.')
+            use_cache = False
+
+        # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
+        if head_mask is not None and decoder_head_mask is None:
+            if self.config.num_layers == self.config.num_decoder_layers:
+                warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
+                decoder_head_mask = head_mask
+
+        in_decoder = stage_manager.stage >= decoder_starting_stage
+
+        # Stage is in encoder, directly return the output of t5_stack_forward
+        if not in_decoder:
+            encoder_outputs = T5PipelineForwards.t5_stack_forward(
+                self.encoder,
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                inputs_embeds=inputs_embeds,
+                head_mask=head_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+                stage_manager=stage_manager,
+                hidden_states=hidden_states,
+                position_bias=position_bias,
+                encoder_decoder_position_bias=encoder_decoder_position_bias,
+                stage_index=stage_index,
+                decoder_starting_stage=decoder_starting_stage)
+            if stage_manager.stage == decoder_starting_stage - 1:
+                # last stage of encoder
+                return {'encoder_outputs': encoder_outputs}
+            else:
+                return encoder_outputs
+
+        at_last_decoder_stage = stage_manager.is_last_stage()
+        at_first_decoder_stage = stage_manager.stage == decoder_starting_stage
+
+        if encoder_outputs is None:
+            raise ValueError("Non-empty encoder_outputs should be passed in at decoder stages.")
+
+        encoder_hidden_states = encoder_outputs[0]
+        if return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+            encoder_outputs = BaseModelOutput(
+                last_hidden_state=encoder_outputs[0],
+                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+            )
+
+        # Stage is in decoder, we assume that the outputs of last stage of encoder will be passed in.
+        if not at_first_decoder_stage and hidden_states is None:
+            raise ValueError("If not at the first layer of decoder, non-empty hidden_states must be provided.")
+
+        # Decode
+        decoder_outputs = T5PipelineForwards.t5_stack_forward(
+            self.decoder,
+            input_ids=decoder_input_ids,
+            attention_mask=decoder_attention_mask,
+            inputs_embeds=decoder_inputs_embeds,
+            past_key_values=past_key_values,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=attention_mask,
+            head_mask=decoder_head_mask,
+            cross_attn_head_mask=cross_attn_head_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            hidden_states=hidden_states,
+            position_bias=position_bias,
+            encoder_decoder_position_bias=encoder_decoder_position_bias,
+            stage_index=stage_index,
+            decoder_starting_stage=decoder_starting_stage)
+
+        # Directly return outputs of overloaded T5Stack forward if not at last stage.
+        if not at_last_decoder_stage:
+            decoder_outputs['encoder_outputs'] = encoder_outputs    # encoder_outputs should be passed to the next stage
+            return decoder_outputs
+
+        sequence_output = decoder_outputs[0]
+
+        if self.config.tie_word_embeddings:
+            # Rescale output before projecting on vocab
+            # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
+            sequence_output = sequence_output * (self.model_dim**-0.5)
+
+        lm_logits = self.lm_head(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss(ignore_index=-100)
+            # move labels to correct device to enable PP
+            labels = labels.to(lm_logits.device)
+            loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
+
+        if not return_dict:
+            output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
+            return ((loss,) + output) if loss is not None else output
+
+        return Seq2SeqLMOutput(
+            loss=loss,
+            logits=lm_logits,
+            past_key_values=decoder_outputs.past_key_values,
+            decoder_hidden_states=decoder_outputs.hidden_states,
+            decoder_attentions=decoder_outputs.attentions,
+            cross_attentions=decoder_outputs.cross_attentions,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+            encoder_hidden_states=encoder_outputs.hidden_states,
+            encoder_attentions=encoder_outputs.attentions,
+        )
+
     @staticmethod
     def t5_encoder_model_forward(
         self: T5EncoderModel,

colossalai/shardformer/policies/t5.py

@@ -293,21 +293,42 @@ class T5BasePolicy(Policy):
 
 class T5ModelPolicy(T5BasePolicy):
 
+    def __init__(self) -> None:
+        super().__init__()
+
     def module_policy(self):
         from transformers import T5Model
-        base_policy = super().module_policy()
+        policy = super().module_policy()
 
         if self.shard_config.enable_tensor_parallelism:
             self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
                 suffix="shared",
                 target_module=VocabParallelEmbedding1D,
             ),
-                                                        policy=base_policy,
+                                                        policy=policy,
                                                         target_key=T5Model)
-        return base_policy
+        if self.pipeline_stage_manager is not None:
+            self.set_pipeline_forward(model_cls=T5Model, new_forward=T5PipelineForwards.t5_model_forward, policy=policy)
+
+        return policy
+
+    def get_held_layers(self) -> List[nn.Module]:
+        return super().get_held_layers()
+
+    def get_shared_params(self) -> List[Dict[int, Tensor]]:
+        module = self.model
+        stage_manager = self.pipeline_stage_manager
+        if stage_manager is not None and stage_manager.num_stages > 1:
+            _, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(len(module.encoder.block),
+                                                                          len(module.decoder.block),
+                                                                          stage_manager.num_stages)
+
+            if id(module.decoder.embed_tokens.weight) == id(module.shared.weight):
+                return [{0: module.shared.weight, decoder_starting_stage: module.decoder.embed_tokens.weight}]
+        return []
 
     def postprocess(self):
-        if self.shard_config.enable_tensor_parallelism:
+        if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None:
             binding_map = {"shared.weight": ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]}
             for k, v in binding_map.items():
                 src = getattr_(self.model, k)
@@ -318,6 +339,9 @@ class T5ModelPolicy(T5BasePolicy):
 
 class T5ForConditionalGenerationPolicy(T5BasePolicy):
 
+    def __init__(self) -> None:
+        super().__init__()
+
     def module_policy(self):
         from transformers import T5ForConditionalGeneration
 
@@ -335,8 +359,38 @@ class T5ForConditionalGenerationPolicy(T5BasePolicy):
             ],
                                                         policy=policy,
                                                         target_key=T5ForConditionalGeneration)
+
+        if self.pipeline_stage_manager is not None:
+            self.set_pipeline_forward(model_cls=T5ForConditionalGeneration,
+                                      new_forward=T5PipelineForwards.t5_for_conditional_generation_forward,
+                                      policy=policy)
         return policy
 
+    def get_held_layers(self) -> List[nn.Module]:
+        held_layers = super().get_held_layers()
+        if self.pipeline_stage_manager.is_last_stage():
+            held_layers.append(self.model.lm_head)
+        return held_layers
+
+    def get_shared_params(self) -> List[Dict[int, Tensor]]:
+        module = self.model
+        stage_manager = self.pipeline_stage_manager
+        if stage_manager is not None and stage_manager.num_stages > 1:
+            _, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(len(module.encoder.block),
+                                                                          len(module.decoder.block),
+                                                                          stage_manager.num_stages)
+
+            shared_params = []
+            if id(module.decoder.embed_tokens.weight) == id(module.shared.weight):
+                shared_params.append({
+                    0: module.shared.weight,
+                    decoder_starting_stage: module.decoder.embed_tokens.weight
+                })
+            if id(module.lm_head.weight) == id(module.shared.weight):
+                shared_params.append({0: module.shared.weight, stage_manager.num_stages - 1: module.lm_head.weight})
+            return shared_params
+        return []
+
     def postprocess(self):
         super().postprocess()
         if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None:
@@ -382,7 +436,7 @@ class T5EncoderPolicy(T5BasePolicy):
         return []
 
     def postprocess(self):
-        if self.shard_config.enable_tensor_parallelism:
+        if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None:
             binding_map = {"shared.weight": ["encoder.embed_tokens.weight"]}
             for k, v in binding_map.items():
                 src = getattr_(self.model, k)

tests/test_shardformer/test_model/test_shard_t5_pipeline.py

@@ -28,8 +28,6 @@ def run_t5_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_
 
     sub_model_zoo = model_zoo.get_sub_registry('transformers_t5')
     for name, (model_fn, data_gen_fn, _, _, _) in sub_model_zoo.items():
-        if name != 'transformers_t5_encoder_model':
-            continue
 
         inputs = data_gen_fn()
         inputs = {k: v.cuda() for k, v in inputs.items()}
@@ -52,6 +50,7 @@ def run_t5_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_
         stage = stage_manager.stage
         at_first_stage = (stage == 0) or (stage == decoder_starting_stage)
         at_last_stage = (stage == decoder_starting_stage - 1) or (stage == stage_manager.num_stages - 1)
+        in_decoder = stage >= decoder_starting_stage
 
         if not at_first_stage:
             # change inputs if not the first stage
@@ -62,19 +61,25 @@ def run_t5_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_
             inputs['hidden_states'] = hidden_states
             inputs['position_bias'] = position_bias
             inputs['encoder_decoder_position_bias'] = encoder_decoder_position_bias
+        if in_decoder:
+            encoder_output_states = torch.zeros(*hidden_state_shape).cuda()
+            inputs['encoder_outputs'] = (encoder_output_states,)
 
         sharded_model.train()
         output = sharded_model(**inputs)
         if at_last_stage:
-            if name != 'transformers_t5_for_conditional_generation':
-                assert output[0].shape == hidden_state_shape
-            else:
+            if name == 'transformers_t5_for_conditional_generation' and in_decoder:
                 assert output.loss is not None
+            else:
+                if name != 'transformers_t5_encoder_model' and not in_decoder:
+                    output = output['encoder_outputs']
+                assert output[0].shape == hidden_state_shape
         else:
             assert output['hidden_states'].shape == hidden_state_shape
             # position_bias information should be passed in T5
-            assert 'position_bias' in output
-            assert 'encoder_decoder_position_bias' in output
+            assert output['position_bias'].shape == position_bias_shape
+            if in_decoder:
+                assert output['encoder_decoder_position_bias'].shape == position_bias_shape
 
     torch.cuda.empty_cache()