[pipeline] add bloom model pipeline (#4210)

* bloom policy

* llama pipeline forward and tests

* fix the output and attention_mask

* fix name

* bind argument to policy

* finish bloom model

* test shard gpt2

* clear cache

colossalai/shardformer/policies/bloom.py

+import warnings
+from functools import partial
+from types import MethodType
+from typing import Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
 import torch.nn as nn
+from torch import Tensor
+from torch.nn import CrossEntropyLoss, Module
+from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
+from transformers.models.bloom.modeling_bloom import BloomModel
+from transformers.utils import logging
 
 import colossalai.shardformer.layer as col_nn
+from colossalai.pipeline.stage_manager import PipelineStageManager
 
 from .._utils import getattr_, setattr_
 from ..modeling.bloom import build_bloom_alibi_tensor_fn
 from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
 
+logger = logging.get_logger(__name__)
+
 
 class BloomPolicy(Policy):
 
@@ -110,7 +125,46 @@ class BloomPolicy(Policy):
 
 
 class BloomModelPolicy(BloomPolicy):
-    pass
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def module_policy(self):
+        policy = super().module_policy()
+        from transformers.models.bloom.modeling_bloom import BloomModel
+        if self.pipeline_stage_manager:
+            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)
+            policy[BloomModel] = ModulePolicyDescription(method_replacement={
+                "forward":
+                    partial(bloom_model_forward, stage_manager=self.pipeline_stage_manager, stage_index=stage_index)
+            })
+        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.word_embeddings)
+            held_layers.append(module.word_embeddings_layernorm)
+
+        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]]:
+        '''no shared params in bloommodel'''
+        return []
 
 
 class BloomForCausalLMPolicy(BloomPolicy):
@@ -181,3 +235,174 @@ class BloomForTokenClassificationPolicy(BloomPolicy):
 class BloomForQuestionAnsweringPolicy(BloomPolicy):
     # No head sharding as the output features is only 2
     pass
+
+
+def bloom_model_forward(
+    self: BloomModel,
+    input_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    head_mask: Optional[torch.LongTensor] = None,
+    inputs_embeds: 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,
+    stage_index: Optional[List[int]] = None,
+    **deprecated_arguments,
+) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
+    if deprecated_arguments.pop("position_ids", False) is not False:
+        # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
+        warnings.warn(
+            "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
+            " passing `position_ids`.",
+            FutureWarning,
+        )
+    if len(deprecated_arguments) > 0:
+        raise ValueError(f"Got unexpected arguments: {deprecated_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
+
+    # add warnings here
+    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
+    # Prepare head mask if needed
+    # 1.0 in head_mask indicate we keep the head
+    # attention_probs has shape batch_size x num_heads x N x N
+
+    # head_mask has shape n_layer x batch x num_heads x N x N
+    head_mask = self.get_head_mask(head_mask, self.config.n_layer)
+
+    # case: First stage of training
+    if stage_manager.is_first_stage():
+        # check input_ids and inputs_embeds
+        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
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+
+        hidden_states = self.word_embeddings_layernorm(inputs_embeds)
+        # initialize in the first stage and then pass to the next stage
+    else:
+        input_shape = hidden_states.shape[:-1]
+        batch_size, seq_length = input_shape
+
+    # extra recording tensor should be generated in the first stage
+
+    presents = () if use_cache else None
+    all_self_attentions = () if output_attentions else None
+    all_hidden_states = () if output_hidden_states else None
+
+    if self.gradient_checkpointing and self.training:
+        if use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
+            use_cache = False
+
+    if past_key_values is None:
+        past_key_values = tuple([None] * len(self.h))
+    # Compute alibi tensor: check build_alibi_tensor documentation,build for every stage
+    seq_length_with_past = seq_length
+    past_key_values_length = 0
+    if past_key_values[0] is not None:
+        past_key_values_length = past_key_values[0][0].shape[2]    # source_len
+
+        seq_length_with_past = seq_length_with_past + past_key_values_length
+    if attention_mask is None:
+        attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
+    else:
+        attention_mask = attention_mask.to(hidden_states.device)
+
+    alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
+
+    # causal_mask is constructed every stage and its input is passed through different stages
+    causal_mask = self._prepare_attn_mask(
+        attention_mask,
+        input_shape=(batch_size, seq_length),
+        past_key_values_length=past_key_values_length,
+    )
+
+    start_idx, end_idx = stage_index[0], stage_index[1]
+    for i, (block, layer_past) in enumerate(zip(self.h[start_idx:end_idx], past_key_values[start_idx:end_idx])):
+        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=use_cache, output_attentions=output_attentions)
+
+                return custom_forward
+
+            outputs = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(block),
+                hidden_states,
+                alibi,
+                causal_mask,
+                layer_past,
+                head_mask[i],
+            )
+        else:
+            outputs = block(
+                hidden_states,
+                layer_past=layer_past,
+                attention_mask=causal_mask,
+                head_mask=head_mask[i],
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                alibi=alibi,
+            )
+
+        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 stage_manager.is_last_stage():
+        # Add last hidden state
+        hidden_states = self.ln_f(hidden_states)
+
+    # TODO: deal with all_hidden_states, all_self_attentions, presents
+    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] if v is not None)
+
+        # attention_mask is not returned ; presents = past_key_values
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=presents,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+        )
+    else:
+        # always return dict for imediate stage
+        return {'hidden_states': hidden_states}

tests/kit/model_zoo/transformers/gpt.py

@@ -51,15 +51,17 @@ output_transform_fn = lambda x: x
 loss_fn_for_gpt2_model = lambda x: x.last_hidden_state.mean()
 loss_fn = lambda x: x.loss
 
-config = transformers.GPT2Config(n_layer=2,
-                                 n_head=4,
-                                 vocab_size=50258,
-                                 attn_pdrop=0,
-                                 embd_pdrop=0,
-                                 resid_pdrop=0,
-                                 summary_first_dropout=0,
-                                 hidden_dropout=0,
-                                 problem_type="single_label_classification")
+config = transformers.GPT2Config(
+    n_layer=2,
+    n_head=4,
+    #n_embd=128,
+    vocab_size=50258,
+    attn_pdrop=0,
+    embd_pdrop=0,
+    resid_pdrop=0,
+    summary_first_dropout=0,
+    hidden_dropout=0,
+    problem_type="single_label_classification")
 
 # register the following models
 model_zoo.register(name='transformers_gpt',

tests/test_shardformer/test_model/test_shard_bloom_pipeline.py

+import pytest
+import torch
+
+import colossalai
+from colossalai.cluster import ProcessGroupMesh
+from colossalai.logging import disable_existing_loggers
+from colossalai.pipeline.stage_manager import PipelineStageManager
+from colossalai.shardformer.policies.base_policy import Policy
+from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
+from colossalai.testing import (
+    assert_hf_output_close,
+    clear_cache_before_run,
+    parameterize,
+    rerun_if_address_is_in_use,
+    spawn,
+)
+from tests.kit.model_zoo import model_zoo
+from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward
+
+
+def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
+    # check forward
+    pass
+
+
+@parameterize('enable_fused_normalization', [False])
+@parameterize('enable_tensor_parallelism', [False])
+@parameterize('use_lazy_init', [False])
+#TODO: merge this into test_shard_bloom
+def run_bloom_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init):
+    DP_DIM, PP_DIM = 0, 1
+    DP_SIZE, PP_SIZE = 2, 2
+    RANK_TO_COORDINATE = {
+        0: (0, 0),
+        1: (0, 1),
+        2: (1, 0),
+        3: (1, 1),
+    }
+    PP_RANKS_IN_GROUP = {
+        0: [0, 1],
+        1: [0, 1],
+        2: [2, 3],
+        3: [2, 3],
+    }
+    pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE)
+    stage_manager = PipelineStageManager(pg_mesh, PP_DIM)
+
+    sub_model_zoo = model_zoo.get_sub_registry('transformers_bloom')
+    x = torch.randint(0, 1000, (2, 3)).cuda()
+    hidden_states = torch.randint(0, 1000, (2, 3, 64)).to(torch.float32).cuda()
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        if name == 'transformers_bloom':
+            org_model, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization,
+                                                            enable_tensor_parallelism, use_lazy_init)
+            if stage_manager.stage == 0:
+                attention_mask = torch.ones_like(x).cuda()
+                output = sharded_model(input_ids=x, attention_mask=attention_mask)
+                assert output['hidden_states'].shape == (2, 3, 64)
+            else:
+                attention_mask = torch.ones((2, 3)).cuda()
+                output = sharded_model(
+                    hidden_states=hidden_states,
+                    attention_mask=attention_mask,
+                )
+                assert output[0].shape == (2, 3, 64)
+
+    torch.cuda.empty_cache()
+
+
+def check_bloom(rank, world_size, port):
+    disable_existing_loggers()
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    run_bloom_test()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+@clear_cache_before_run()
+def test_bloom():
+    spawn(check_bloom, 4)
+
+
+if __name__ == "__main__":
+    test_bloom()

tests/test_shardformer/test_model/test_shard_gpt2.py

@@ -70,6 +70,7 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
 @parameterize('enable_fused_normalization', [True, False])
 @parameterize('enable_tensor_parallelism', [True, False])
 @parameterize('use_lazy_init', [False, True])
+@clear_cache_before_run()
 def run_gpt2_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init):
     sub_model_zoo = model_zoo.get_sub_registry('transformers_gpt')
     for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():