[pipeline] Add Pipeline Forward for GPT2Model Shardformer

colossalai/shardformer/layer/linear.py

@@ -129,7 +129,7 @@ def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, Lis
                                  **kwargs)
 
         with torch.no_grad():
-            # the weigh to the linear layer is a transpose
+            # the weight to the linear layer is a transpose
             # thus shard on row is equal to shard on column
             sharded_weight = shard_rowwise(module.weight.data, process_group)
             linear_1d.weight.data.copy_(sharded_weight)

colossalai/shardformer/policies/gpt2.py

@@ -1,6 +1,14 @@
-import torch.nn as nn
+import logging
+from functools import partial
+from types import MethodType
+from typing import Dict, List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+from torch.nn import Module
 
 import colossalai.shardformer.layer as col_nn
+from colossalai.pipeline.stage_manager import PipelineStageManager
 
 from .._utils import getattr_, setattr_
 from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
@@ -119,6 +127,46 @@ class GPT2ModelPolicy(GPT2Policy):
     def __init__(self) -> None:
         super().__init__()
 
+    def module_policy(self):
+        from transformers.models.gpt2.modeling_gpt2 import GPT2Model
+
+        policy = super().module_policy()
+        if self.pipeline_stage_manager:
+            # set None as default
+            stage_manager = self.pipeline_stage_manager
+            layers_per_stage = Policy.distribute_layers(len(self.model.h), stage_manager.num_stages)
+            stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
+            method_replacement = {
+                'forward':
+                    partial(GPT2PipelineForwards.gpt2_model_forward,
+                            stage_manager=stage_manager,
+                            stage_index=stage_index)
+            }
+            self.append_or_create_method_replacement(description=method_replacement,
+                                                     policy=policy,
+                                                     target_key=GPT2Model)
+        return policy
+
+    def get_held_layers(self) -> List[Module]:
+        """Get pipeline layers for current stage."""
+        module = self.model
+        stage_manager = self.pipeline_stage_manager
+        held_layers = []
+        layers_per_stage = self.distribute_layers(len(module.h), stage_manager.num_stages)
+        if stage_manager.is_first_stage():
+            held_layers.append(module.wte)
+            held_layers.append(module.wpe)
+        start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
+        held_layers.extend(module.h[start_idx:end_idx])
+        if stage_manager.is_last_stage():
+            held_layers.append(module.ln_f)
+        return held_layers
+
+    def get_shared_params(self) -> List[Dict[int, Tensor]]:
+        # TODO: check whether there is shared param in gpt2model
+        """No shared params in gpt2 model."""
+        return []
+
 
 # GPT2LMHeadModel
 class GPT2LMHeadModelPolicy(GPT2Policy):
@@ -194,3 +242,223 @@ class GPT2ForSequenceClassificationPolicy(GPT2Policy):
 
     def __init__(self) -> None:
         super().__init__()
+
+
+class GPT2PipelineForwards:
+    '''
+    This class serves as a micro library for forward function substitution of GPT2 models
+    under pipeline setting.
+    '''
+
+    @staticmethod
+    def gpt2_model_forward(
+            self: 'GPT2Model',
+            input_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            token_type_ids: Optional[torch.LongTensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            head_mask: Optional[torch.FloatTensor] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            encoder_hidden_states: Optional[torch.Tensor] = None,
+            encoder_attention_mask: Optional[torch.FloatTensor] = 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,
+            stage_index: Optional[List[int]] = None) -> Union[Tuple, 'BaseModelOutputWithPastAndCrossAttentions']:
+
+        # This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2Model.forward.
+        # Please refer to original code of transformers for more details.
+
+        from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
+
+        # Preprocess passed in arguments
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (output_hidden_states
+                                if output_hidden_states is not None else self.config.output_hidden_states)
+        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
+
+        if stage_manager.is_first_stage():
+            if input_ids is not None and inputs_embeds is not None:
+                raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+            elif input_ids is not None:
+                batch_size, seq_length = input_ids.shape
+                input_shape = input_ids.size()
+                input_ids = input_ids.view(-1, seq_length)
+            elif inputs_embeds is not None:
+                input_shape = inputs_embeds.size()[:-1]
+                batch_size = inputs_embeds.shape[0]
+            else:
+                raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            if token_type_ids is not None:
+                token_type_ids = token_type_ids.view(-1, seq_length)
+        else:
+            if hidden_states is None:
+                raise ValueError("hidden_states shouln't be None for stages other than the first stage.")
+            input_shape = hidden_states.size()[:-1]
+            batch_size, seq_length = input_shape[0], input_shape[1]
+            device = hidden_states.device
+
+        # GPT2Attention mask.
+        if attention_mask is not None:
+            if batch_size <= 0:
+                raise ValueError("batch_size has to be defined and > 0")
+            attention_mask = attention_mask.view(batch_size, -1)
+            # We create a 3D attention mask from a 2D tensor mask.
+            # Sizes are [batch_size, 1, 1, to_seq_length]
+            # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+            # this attention mask is more simple than the triangular masking of causal attention
+            # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+            attention_mask = attention_mask[:, None, None, :]
+
+            # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+            # masked positions, this operation will create a tensor which is 0.0 for
+            # positions we want to attend and the dtype's smallest value for masked positions.
+            # Since we are adding it to the raw scores before the softmax, this is
+            # effectively the same as removing these entirely.
+            attention_mask = attention_mask.to(dtype=self.dtype)    # fp16 compatibility
+            attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
+
+        # If a 2D or 3D attention mask is provided for the cross-attention
+        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+        if self.config.add_cross_attention and encoder_hidden_states is not None:
+            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+            if encoder_attention_mask is None:
+                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+            encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+        else:
+            encoder_attention_mask = None
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # head_mask has shape n_layer x batch x n_heads x N x N
+        head_mask = self.get_head_mask(head_mask, self.config.n_layer)
+
+        if stage_manager.is_first_stage():
+            if position_ids is not None:
+                position_ids = position_ids.view(-1, seq_length)
+            else:
+                position_ids = torch.arange(0, seq_length, dtype=torch.long, device=device)
+                position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
+
+            if inputs_embeds is None:
+                inputs_embeds = self.wte(input_ids)
+            position_embeds = self.wpe(position_ids)
+            hidden_states = inputs_embeds + position_embeds
+            if token_type_ids is not None:
+                token_type_embeds = self.wte(token_type_ids)
+                hidden_states = hidden_states + token_type_embeds
+                hidden_states = self.drop(hidden_states)
+
+        output_shape = input_shape + (hidden_states.size(-1),)
+
+        # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
+        if past_key_values:
+            logging.warning('Non-empty past_key_values is not supported for pipeline models at the moment.')
+            past_key_values = None
+        if output_attentions:
+            logging.warning('output_attentions=True is not supported for pipeline models at the moment.')
+            output_attentions = False
+        if output_hidden_states:
+            logging.warning('output_hidden_states=True is not supported for pipeline models at the moment.')
+            output_hidden_states = False
+        if use_cache:
+            logging.warning('use_cache=True is not supported for pipeline models at the moment.')
+            use_cache = False
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logging.warning(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
+                use_cache = False
+        presents = () if use_cache else None
+        all_self_attentions = () if output_attentions else None
+        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
+        all_hidden_states = () if output_hidden_states else None
+
+        # Going through held blocks.
+        start_idx, end_idx = stage_index[0], stage_index[1]
+        for i in range(start_idx, end_idx):
+            block = self.h[i]
+            torch.cuda.set_device(hidden_states.device)
+            # Ensure that attention_mask is always on the same device as hidden_states
+            if attention_mask is not None:
+                attention_mask = attention_mask.to(hidden_states.device)
+            if isinstance(head_mask, torch.Tensor):
+                head_mask = head_mask.to(hidden_states.device)
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, use_cache, output_attentions)
+
+                    return custom_forward
+
+                outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    None,
+                    attention_mask,
+                    head_mask[i],
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                )
+            else:
+                outputs = block(
+                    hidden_states,
+                    layer_past=None,
+                    attention_mask=attention_mask,
+                    head_mask=head_mask[i],
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_attention_mask=encoder_attention_mask,
+                    use_cache=use_cache,
+                    output_attentions=output_attentions,
+                )
+
+            hidden_states = outputs[0]
+            if use_cache is True:
+                presents = presents + (outputs[1],)
+
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
+                if self.config.add_cross_attention:
+                    all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],)
+
+        if stage_manager.is_last_stage():
+            hidden_states = self.ln_f(hidden_states)
+
+        hidden_states = hidden_states.view(output_shape)
+
+        # Add last hidden state
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if stage_manager.is_last_stage():
+            if not return_dict:
+                return tuple(
+                    v for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions]
+                    if v is not None)
+
+            return BaseModelOutputWithPastAndCrossAttentions(
+                last_hidden_state=hidden_states,
+                past_key_values=presents,
+                hidden_states=all_hidden_states,
+                attentions=all_self_attentions,
+                cross_attentions=all_cross_attentions,
+            )
+        else:
+            # always return dict for intermediate stage
+            return {'hidden_states': hidden_states}

colossalai/shardformer/shard/sharder.py

@@ -72,17 +72,18 @@ def _recursive_replace_layer(
         attr_replacement: Dict[str, Any],
         param_replacement: List[Callable],
         method_replacement: Dict[str, Callable],
-        sub_module_replacement: List[Callable],
+        sub_module_replacement: List[SubModuleReplacementDescription],
     ) -> None:
         r"""
         Reverse the replace layer operation
 
         Args:
-            layer (torch.nn.Module): The object of layer to shard
-            origin_cls (Union[str, torch.nn.Module]): The origin layer class or a string of layer class name.
-            attr_replacement (Dict): The attribute dict to modify
+            module (torch.nn.Module): The object of layer to shard
+            origin_cls (Union[str, torch.nn.Module]): The origin layer class or a string of layer class name
+            attr_replacement (Dict[str, Any]): The attribute dict to modify
             param_replacement (List[Callable]): The function list to get parameter shard information in policy
-            sub_module_replacement (List[Callable]): The function list to get sub module shard information in policy
+            method_replacement (Dict[str, Callable]):  Key is the method name, value is the method for replacement
+            sub_module_replacement ((List[SubModuleReplacementDescription]): The function list to get sub module shard information in policy
         """
         if (isinstance(origin_cls, str) and origin_cls == module.__class__.__name__) or \
            (module.__class__ == origin_cls):
@@ -111,7 +112,7 @@ def _replace_attr(
         Replace the attribute of the layer
 
         Args:
-            layer (:class:`torch.nn.Module`): The object of layer to shard
+            module (:class:`torch.nn.Module`): The object of layer to shard
             attr_replacement (Dict): The attribute dict to modify
         """
         for k, v in attr_replacement.items():
@@ -126,7 +127,7 @@ def _replace_param(
         Replace the parameter of the layer
 
         Args:
-            layer (:class:`torch.nn.Module`): The object of layer to shard
+            module (:class:`torch.nn.Module`): The object of layer to shard
             param_replacement (List[Callable]): The function list to get parameter shard information in policy
         """
         for param_func in param_replacement:

tests/test_shardformer/test_model/test_shard_gpt2.py

@@ -65,6 +65,7 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
     assert torch.allclose(
         org_grad, all_shard_grad,
         atol=1e-5), f"shard model grad is not equal to origin model grad\n{org_grad}\n{all_shard_grad}"
+    torch.cuda.empty_cache()
 
 
 @parameterize('enable_fused_normalization', [True, False])
@@ -77,6 +78,7 @@ def run_gpt2_test(enable_fused_normalization, enable_tensor_parallelism, use_laz
         org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism,
                                                use_lazy_init)
         check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
+
     torch.cuda.empty_cache()