[shardformer] support whisper (#4212)

* support whisper

* fix bug in vocabembedding

* support downstream model of whisper

* update readme

colossalai/shardformer/README.md

@@ -102,7 +102,7 @@ We will follow this roadmap to develop Shardformer:
       - [ ] SwinTransformer
       - [ ] SwinTransformer V2
     - [ ] Audio
-      - [ ] Whisper
+      - [x] Whisper
     - [ ] Multi-modal
       - [ ] To be added
 

colossalai/shardformer/layer/embedding.py

@@ -202,7 +202,6 @@ class VocabParallelEmbedding1D(ParallelModule):
         super().__init__()
         self.num_embeddings = num_embeddings
         self.embedding_dim = embedding_dim
-        self.padding_idx = padding_idx
         self.embed_args = args
         self.embed_kwargs = kwargs
         self.process_group = process_group
@@ -276,6 +275,15 @@ class VocabParallelEmbedding1D(ParallelModule):
             with torch.no_grad():
                 self.weight[self.padding_idx - self.vocab_start_index].fill_(0)
 
+    def _select_padding_idx(self, padding_idx: int):
+        # select padding index according to the rank
+        if padding_idx is None:
+            return None
+        elif padding_idx < self.vocab_end_index and padding_idx >= self.vocab_start_index:
+            return padding_idx - self.vocab_start_index
+        else:
+            return None
+
     def forward(self, input_: Tensor) -> Tensor:
         # Build the mask.
         input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index)

colossalai/shardformer/policies/auto_policy.py

@@ -105,6 +105,14 @@ _POLICY_LIST = {
     "transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering":
         PolicyLocation(file_name="bloom", class_name="BloomForQuestionAnsweringPolicy"),
 
+    # Whisper
+    "transformers.models.whisper.modeling_whisper.WhisperModel":
+        PolicyLocation(file_name="whisper", class_name="WhisperModelPolicy"),
+    "transformers.models.whisper.modeling_whisper.WhisperForConditionalGeneration":
+        PolicyLocation(file_name="whisper", class_name="WhisperForConditionalGenerationPolicy"),
+    "transformers.models.whisper.modeling_whisper.WhisperForAudioClassification":
+        PolicyLocation(file_name="whisper", class_name="WhisperForAudioClassificationPolicy"),
+
     # Sam
     "transformers.models.sam.modeling_sam.SamModel":
         PolicyLocation(file_name="sam", class_name="SamModelPolicy"),

colossalai/shardformer/policies/whisper.py

+import torch.nn as nn
+
+import colossalai.shardformer.layer as col_nn
+
+from .._utils import getattr_, setattr_
+from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
+
+__all__ = [
+    'WhisperPolicy', 'WhisperModelPolicy', 'WhisperForConditionalGenerationPolicy', 'WhisperForAudioClassification'
+]
+
+
+class WhisperPolicy(Policy):
+
+    def config_sanity_check(self):
+        pass
+
+    def preprocess(self):
+        # reshape the embedding layer
+        r"""
+        Reshape the Embedding layer to make the embedding dimension divisible by world_size
+        """
+        # TODO:
+        vocab_size = self.model.config.vocab_size
+        world_size = self.shard_config.tensor_parallel_size
+        if vocab_size % world_size != 0:
+            new_vocab_size = vocab_size + world_size - vocab_size % world_size
+            self.model.resize_token_embeddings(new_vocab_size)
+        return self.model
+
+    def module_policy(self):
+        from transformers.models.whisper.modeling_whisper import (
+            WhisperDecoder,
+            WhisperDecoderLayer,
+            WhisperEncoder,
+            WhisperEncoderLayer,
+        )
+
+        policy = {}
+
+        if self.shard_config.enable_tensor_parallelism:
+            policy[WhisperEncoderLayer] = ModulePolicyDescription(attribute_replacement={
+                "self_attn.embed_dim":
+                    self.model.config.d_model // self.shard_config.tensor_parallel_size,
+                "self_attn.num_heads":
+                    self.model.config.encoder_attention_heads // self.shard_config.tensor_parallel_size,
+            },
+                                                                  sub_module_replacement=[
+                                                                      SubModuleReplacementDescription(
+                                                                          suffix="self_attn.q_proj",
+                                                                          target_module=col_nn.Linear1D_Col,
+                                                                      ),
+                                                                      SubModuleReplacementDescription(
+                                                                          suffix="self_attn.k_proj",
+                                                                          target_module=col_nn.Linear1D_Col,
+                                                                      ),
+                                                                      SubModuleReplacementDescription(
+                                                                          suffix="self_attn.v_proj",
+                                                                          target_module=col_nn.Linear1D_Col,
+                                                                      ),
+                                                                      SubModuleReplacementDescription(
+                                                                          suffix="self_attn.out_proj",
+                                                                          target_module=col_nn.Linear1D_Row,
+                                                                      ),
+                                                                      SubModuleReplacementDescription(
+                                                                          suffix="fc1",
+                                                                          target_module=col_nn.Linear1D_Col,
+                                                                      ),
+                                                                      SubModuleReplacementDescription(
+                                                                          suffix="fc2",
+                                                                          target_module=col_nn.Linear1D_Row,
+                                                                      ),
+                                                                  ])
+
+            policy[WhisperDecoderLayer] = ModulePolicyDescription(attribute_replacement={
+                "self_attn.embed_dim":
+                    self.model.config.d_model // self.shard_config.tensor_parallel_size,
+                "self_attn.num_heads":
+                    self.model.config.decoder_attention_heads // self.shard_config.tensor_parallel_size,
+                "encoder_attn.embed_dim":
+                    self.model.config.d_model // self.shard_config.tensor_parallel_size,
+                "encoder_attn.num_heads":
+                    self.model.config.encoder_attention_heads // self.shard_config.tensor_parallel_size,
+            },
+                                                                  sub_module_replacement=[
+                                                                      SubModuleReplacementDescription(
+                                                                          suffix="self_attn.q_proj",
+                                                                          target_module=col_nn.Linear1D_Col,
+                                                                      ),
+                                                                      SubModuleReplacementDescription(
+                                                                          suffix="self_attn.k_proj",
+                                                                          target_module=col_nn.Linear1D_Col,
+                                                                      ),
+                                                                      SubModuleReplacementDescription(
+                                                                          suffix="self_attn.v_proj",
+                                                                          target_module=col_nn.Linear1D_Col,
+                                                                      ),
+                                                                      SubModuleReplacementDescription(
+                                                                          suffix="self_attn.out_proj",
+                                                                          target_module=col_nn.Linear1D_Row,
+                                                                      ),
+                                                                      SubModuleReplacementDescription(
+                                                                          suffix="encoder_attn.q_proj",
+                                                                          target_module=col_nn.Linear1D_Col,
+                                                                      ),
+                                                                      SubModuleReplacementDescription(
+                                                                          suffix="encoder_attn.k_proj",
+                                                                          target_module=col_nn.Linear1D_Col,
+                                                                      ),
+                                                                      SubModuleReplacementDescription(
+                                                                          suffix="encoder_attn.v_proj",
+                                                                          target_module=col_nn.Linear1D_Col,
+                                                                      ),
+                                                                      SubModuleReplacementDescription(
+                                                                          suffix="encoder_attn.out_proj",
+                                                                          target_module=col_nn.Linear1D_Row,
+                                                                      ),
+                                                                      SubModuleReplacementDescription(
+                                                                          suffix="fc1",
+                                                                          target_module=col_nn.Linear1D_Col,
+                                                                      ),
+                                                                      SubModuleReplacementDescription(
+                                                                          suffix="fc2",
+                                                                          target_module=col_nn.Linear1D_Row,
+                                                                      ),
+                                                                  ])
+
+            policy[WhisperDecoder] = ModulePolicyDescription(sub_module_replacement=[
+                SubModuleReplacementDescription(
+                    suffix="embed_tokens",
+                    target_module=col_nn.VocabParallelEmbedding1D,
+                ),
+            ])
+
+        # optimization configuration
+        if self.shard_config.enable_fused_normalization:
+            # Handle encoder layer
+            self.append_or_create_submodule_replacement(description=[
+                SubModuleReplacementDescription(
+                    suffix="self_attn_layer_norm",
+                    target_module=col_nn.FusedLayerNorm,
+                ),
+                SubModuleReplacementDescription(
+                    suffix="final_layer_norm",
+                    target_module=col_nn.FusedLayerNorm,
+                )
+            ],
+                                                        policy=policy,
+                                                        target_key=WhisperEncoderLayer)
+
+            # Handle decoder layer
+            self.append_or_create_submodule_replacement(description=[
+                SubModuleReplacementDescription(
+                    suffix="self_attn_layer_norm",
+                    target_module=col_nn.FusedLayerNorm,
+                ),
+                SubModuleReplacementDescription(
+                    suffix="final_layer_norm",
+                    target_module=col_nn.FusedLayerNorm,
+                )
+            ],
+                                                        policy=policy,
+                                                        target_key=WhisperDecoderLayer)
+
+            # handle encoder layer
+            self.append_or_create_submodule_replacement(description=[
+                SubModuleReplacementDescription(
+                    suffix="layer_norm",
+                    target_module=col_nn.FusedLayerNorm,
+                )
+            ],
+                                                        policy=policy,
+                                                        target_key=WhisperEncoder)
+
+            # handle decoder layer
+            self.append_or_create_submodule_replacement(description=[
+                SubModuleReplacementDescription(
+                    suffix="layer_norm",
+                    target_module=col_nn.FusedLayerNorm,
+                )
+            ],
+                                                        policy=policy,
+                                                        target_key=WhisperDecoder)
+        return policy
+
+    def add_lm_head_policy(self, base_policy):
+        from transformers.models.whisper.modeling_whisper import WhisperForConditionalGeneration
+
+        # optimize for tensor parallelism
+        if self.shard_config.enable_tensor_parallelism:
+            self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
+                suffix="proj_out", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}),
+                                                        policy=base_policy,
+                                                        target_key=WhisperForConditionalGeneration)
+
+        return base_policy
+
+    def postprocess(self):
+        return self.model
+
+
+# WhisperModel
+class WhisperModelPolicy(WhisperPolicy):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+
+# WhisperForConditionalGeneration
+class WhisperForConditionalGenerationPolicy(WhisperPolicy):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def module_policy(self):
+        module_policy = super().module_policy()
+        module_policy = self.add_lm_head_policy(module_policy)
+        return module_policy
+
+    def postprocess(self):
+        binding_map = {"model.decoder.embed_tokens.weight": "proj_out.weight"}
+        for k, v in binding_map.items():
+            param = getattr_(self.model, k)
+            setattr_(self.model, v, param)
+        return self.model
+
+
+# WhisperForAudioClassification
+class WhisperForAudioClassificationPolicy(WhisperPolicy):
+
+    def __init__(self) -> None:
+        super().__init__()

tests/kit/model_zoo/transformers/__init__.py

@@ -7,3 +7,4 @@ from .opt import *
 from .sam import *
 from .t5 import *
 from .vit import *
+from .whisper import *

tests/kit/model_zoo/transformers/whisper.py

+import torch
+import transformers
+
+from ..registry import ModelAttribute, model_zoo
+
+# ===============================
+# Register single-sentence Whisper
+# ===============================
+
+
+# define data gen function
+def data_gen():
+    # Generated from following code snippet
+    #
+    # from transformers import AutoFeatureExtractor, WhisperModel
+    # from datasets import load_dataset
+
+    # model = WhisperModel.from_pretrained("openai/whisper-base")
+    # feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base")
+    # ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+    # inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt")
+    # input_features = inputs.input_features
+    # decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
+
+    input_features = torch.randn(1, 80, 3000)
+    decoder_input_ids = torch.tensor([[1, 1]]) * 50258
+    return dict(input_features=input_features, decoder_input_ids=decoder_input_ids)
+
+
+def data_gen_for_conditional_generation():
+    # labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+    #         Labels for computing the language modeling loss. Indices should either be in `[0, ..., config.vocab_size]`
+    #         or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is
+    #         only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+    data = data_gen()
+    data['labels'] = torch.tensor([[0, 1]], dtype=torch.int64)
+    return data
+
+
+def data_gen_for_audio_classification():
+    # labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+    #         Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+    #         config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+    #         `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+    # `WhisperForAudioClassification` does not need `decoder_input_ids`
+    data = data_gen()
+    data.pop('decoder_input_ids')
+    data['labels'] = torch.tensor([1], dtype=torch.int64)
+    return data
+
+
+# define output transform function
+output_transform_fn = lambda x: x
+
+# define loss funciton
+loss_fn = lambda x: x.last_hidden_state.mean()
+loss_fn_attr = lambda x: x.loss
+
+config = transformers.WhisperConfig(
+    classifier_proj_size=256,
+    d_model=256,
+    decoder_attention_heads=4,
+    decoder_ffn_dim=1536,
+    decoder_layers=2,
+    encoder_attention_heads=4,
+    encoder_ffn_dim=1536,
+    encoder_layers=2,
+    vocab_size=51866,
+)
+
+# register the Whisper variants
+model_zoo.register(name='transformers_whisper',
+                   model_fn=lambda: transformers.WhisperModel(config),
+                   data_gen_fn=data_gen,
+                   output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn,
+                   model_attribute=ModelAttribute(has_control_flow=True))
+
+model_zoo.register(name='transformers_whisperForConditionalGeneration',
+                   model_fn=lambda: transformers.WhisperForConditionalGeneration(config),
+                   data_gen_fn=data_gen_for_conditional_generation,
+                   output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn_attr,
+                   model_attribute=ModelAttribute(has_control_flow=True))
+
+model_zoo.register(name='transformers_whisperWhisperForAudioClassification',
+                   model_fn=lambda: transformers.WhisperForAudioClassification(config),
+                   data_gen_fn=data_gen_for_audio_classification,
+                   output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn_attr,
+                   model_attribute=ModelAttribute(has_control_flow=True))

tests/test_shardformer/test_model/test_shard_whisper.py

+import pytest
+import torch
+
+import colossalai
+from colossalai.logging import disable_existing_loggers
+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, run_forward
+
+
+def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
+    # check forward
+    org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn,
+                                                                 output_transform_fn, loss_fn)
+    assert_hf_output_close(org_output, shard_output, ignore_keys='past_key_values')
+
+    # do backward
+    org_loss.backward()
+    shard_loss.backward()
+
+    assert torch.allclose(org_loss, shard_loss,
+                          atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
+
+    # check grad
+
+    if org_model.__class__.__name__ == 'WhisperForConditionalGeneration':
+        whisper = org_model.model
+        sharded_whisper = sharded_model.model
+    else:
+        whisper = org_model
+        sharded_whisper = sharded_model
+
+    # compare self attention grad
+    org_grad = whisper.encoder.layers[0].self_attn.q_proj.weight.grad
+    shard_grad = sharded_whisper.encoder.layers[0].self_attn.q_proj.weight.grad
+    shard_weight = sharded_whisper.encoder.layers[0].self_attn.q_proj.weight
+
+    if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
+        shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
+        shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
+        all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    else:
+        all_shard_grad = shard_grad
+    assert torch.allclose(org_grad, all_shard_grad,
+                          atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
+
+    # WhisperForAudioClassification does not have decoder and embedding layer
+    if org_model.__class__.__name__ == 'WhisperForAudioClassification':
+        return
+
+    # compare embedding grad
+    org_grad = whisper.decoder.embed_tokens.weight.grad
+    shard_grad = sharded_whisper.decoder.embed_tokens.weight.grad
+    shard_weight = sharded_whisper.decoder.embed_tokens.weight
+
+    if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
+        shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
+        shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
+        all_shard_grad = torch.cat(shard_grad_list, dim=0)
+    else:
+        all_shard_grad = shard_grad
+
+    assert torch.allclose(org_grad, all_shard_grad,
+                          atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
+
+
+@parameterize('enable_fused_normalization', [True, False])
+@parameterize('enable_tensor_parallelism', [True, False])
+def run_whisper_test(enable_fused_normalization, enable_tensor_parallelism):
+    sub_model_zoo = model_zoo.get_sub_registry('transformers_whisper')
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        org_model, sharded_model = build_model(model_fn,
+                                               enable_fused_normalization=enable_fused_normalization,
+                                               enable_tensor_parallelism=enable_tensor_parallelism)
+        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
+
+    torch.cuda.empty_cache()
+
+
+def check_whisper(rank, world_size, port):
+    disable_existing_loggers()
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    run_whisper_test()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+@clear_cache_before_run()
+def test_whisper():
+    spawn(check_whisper, 2)
+
+
+if __name__ == "__main__":
+    test_whisper()