Modernize MosaicBERT

examples/benchmarks/bert/main.py

@@ -246,7 +246,9 @@ def main(cfg: DictConfig,
         load_path=cfg.get('load_path', None),
         load_weights_only=cfg.get('load_weights_only', False),
         python_log_level=cfg.get('python_log_level', None),
-    )
+        autoresume=cfg.get('autoresume', None),
+        fsdp_config=cfg.get('fsdp_config', None),
+        compile_config=cfg.get('compile_config', None))
 
     print('Logging config...')
     log_config(cfg)

examples/benchmarks/bert/requirements-cpu.txt

@@ -1,6 +1,6 @@
 einops==0.5.0
-torch==1.13.1
-mosaicml[nlp,wandb]>=0.14.0,<0.15
-mosaicml-streaming==0.4.1
-omegaconf==2.2.3
-transformers==4.28.1
+torch==2.1.1
+composer[nlp,wandb]>=0.17.0,<0.18
+mosaicml-streaming<=0.7
+omegaconf==2.3.0
+transformers==4.35.2

examples/benchmarks/bert/requirements.txt

@@ -1,8 +1,10 @@
 einops==0.5.0
-torch==1.13.1
-mosaicml[nlp,wandb]>=0.14.0,<0.15
-mosaicml-streaming==0.4.1
-omegaconf==2.2.3
-transformers==4.28.1
+torch==2.1.1
+composer[nlp,wandb]>=0.17.0,<0.18
+mosaicml-streaming<=0.7
+omegaconf==2.3.0
+transformers==4.35.2
+# need a newer version of FA2
+flash_attn>=2.4.2
 # need a newer version of triton
-triton==2.0.0.dev20221103
+#triton==2.0.0.dev20221103

examples/benchmarks/bert/src/bert_layers.py

@@ -54,11 +54,26 @@
                                            SequenceClassifierOutput)
 from transformers.models.bert.modeling_bert import BertPreTrainedModel
 
+IMPL_USE_FLASH2 = False
 try:
-    import flash_attn_triton as flash_attn_triton
-    flash_attn_qkvpacked_func = flash_attn_triton.flash_attn_qkvpacked_func
+    import importlib
+
+    from flash_attn import flash_attn_qkvpacked_func
+    installed_version = importlib.metadata.version('flash_attn')
+    if installed_version < '2.4.2':
+        raise ImportError('newer version of flash_attn required (>= 2.4.2)')
+    IMPL_USE_FLASH2 = True
 except ImportError as e:
-    flash_attn_qkvpacked_func = None
+    warnings.warn(
+        f'Failed to import flash_attn. Will try to import triton implementation: {e}',
+        stacklevel=2)
+    try:
+        import flash_attn_triton as flash_attn_triton
+        flash_attn_qkvpacked_func = flash_attn_triton.flash_attn_qkvpacked_func
+    except ImportError as e:
+        flash_attn_qkvpacked_func = None
+        warnings.warn(f'Failed to import flash_attn_triton as a fallback: {e}',
+                      stacklevel=2)
 
 logger = logging.getLogger(__name__)
 
@@ -183,7 +198,8 @@ def __init__(self, config):
 
     def forward(self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor,
                 max_seqlen_in_batch: int, indices: torch.Tensor,
-                attn_mask: torch.Tensor, bias: torch.Tensor) -> torch.Tensor:
+                attn_mask: torch.Tensor, bias: torch.Tensor,
+                slopes: torch.Tensor) -> torch.Tensor:
         """Perform self-attention.
 
         If dropout is zero, then we can use the Triton kernel, so we do that. However, if not, we send through a standard PyTorch
@@ -201,6 +217,7 @@ def forward(self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor,
             indices: (total_nnz,)
             attn_mask: (batch, max_seqlen_in_batch)
             bias: (batch, heads, max_seqlen_in_batch, max_seqlen_in_batch)
+            slopes: (heads) or (batch, heads)
 
         Returns:
             attention: (total_nnz, dim)
@@ -213,7 +230,8 @@ def forward(self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor,
                         'b s (t h d) -> b s t h d',
                         t=3,
                         h=self.num_attention_heads)
-        if self.p_dropout or flash_attn_qkvpacked_func is None:
+        if (not IMPL_USE_FLASH2 and
+                self.p_dropout) or flash_attn_qkvpacked_func is None:
             # if we have nonzero attention dropout (e.g. during fine-tuning) or no Triton, compute attention in PyTorch
             q = qkv[:, :, 0, :, :].permute(0, 2, 1, 3)  # b h s d
             k = qkv[:, :, 1, :, :].permute(0, 2, 3, 1)  # b h d s
@@ -226,19 +244,41 @@ def forward(self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor,
             attention = torch.matmul(attention_probs, v).permute(0, 2, 1,
                                                                  3)  # b s h d
         else:
-            # Triton implementation only supports 0 attention dropout
-            convert_dtype = qkv.dtype not in [torch.float16, torch.bfloat16]
-            if convert_dtype:
-                # Triton implementation only supports fp16 and bf16
-                orig_dtype = qkv.dtype
-                qkv = qkv.to(torch.float16)
-                bias_dtype = bias.dtype
-                bias = bias.to(torch.float16)
-                attention = flash_attn_qkvpacked_func(qkv, bias)
-                attention = attention.to(orig_dtype)
-                bias = bias.to(bias_dtype)
+            if IMPL_USE_FLASH2:
+                assert 1 <= len(slopes.shape) <= 2, f'{slopes=}'
+                assert slopes.shape[
+                    -1] == self.num_attention_heads, f'{slopes=}'
+
+                # Triton implementation only supports 0 attention dropout
+                convert_dtype = qkv.dtype not in [torch.float16, torch.bfloat16]
+                if convert_dtype:
+                    # Triton implementation only supports fp16 and bf16
+                    orig_dtype = qkv.dtype
+                    qkv = qkv.to(torch.float16)
+                    bias_dtype = bias.dtype
+                    bias = bias.to(torch.float16)
+
+                    attention = flash_attn_qkvpacked_func(
+                        qkv, dropout_p=self.p_dropout, alibi_slopes=slopes)
+                    attention = attention.to(orig_dtype)
+                    bias = bias.to(bias_dtype)
+                else:
+                    attention = flash_attn_qkvpacked_func(
+                        qkv, dropout_p=self.p_dropout, alibi_slopes=slopes)
             else:
-                attention = flash_attn_qkvpacked_func(qkv, bias)
+                # Triton implementation only supports 0 attention dropout
+                convert_dtype = qkv.dtype not in [torch.float16, torch.bfloat16]
+                if convert_dtype:
+                    # Triton implementation only supports fp16 and bf16
+                    orig_dtype = qkv.dtype
+                    qkv = qkv.to(torch.float16)
+                    bias_dtype = bias.dtype
+                    bias = bias.to(torch.float16)
+                    attention = flash_attn_qkvpacked_func(qkv, bias)
+                    attention = attention.to(orig_dtype)
+                    bias = bias.to(bias_dtype)
+                else:
+                    attention = flash_attn_qkvpacked_func(qkv, bias)
 
         # attn_mask is 1 for attend and 0 for don't
         attention = bert_padding_module.unpad_input_only(
@@ -291,6 +331,7 @@ def forward(
         indices: Optional[torch.Tensor] = None,
         attn_mask: Optional[torch.Tensor] = None,
         bias: Optional[torch.Tensor] = None,
+        slopes: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         """Forward pass for scaled self-attention without padding.
 
@@ -303,9 +344,11 @@ def forward(
             indices: None or (total_nnz,)
             attn_mask: None or (batch, max_seqlen_in_batch)
             bias: None or (batch, heads, max_seqlen_in_batch, max_seqlen_in_batch)
+            slopes: None or (batch, heads) or (heads,)
         """
+        assert (bias is None) == (slopes is None), f'{bias=}, {slopes=}'
         self_output = self.self(input_tensor, cu_seqlens, max_s, indices,
-                                attn_mask, bias)
+                                attn_mask, bias, slopes)
         if subset_idx is not None:
             return self.output(
                 bert_padding_module.index_first_axis(self_output, subset_idx),
@@ -379,6 +422,7 @@ def forward(
         indices: Optional[torch.Tensor] = None,
         attn_mask: Optional[torch.Tensor] = None,
         bias: Optional[torch.Tensor] = None,
+        slopes: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         """Forward pass for a BERT layer, including both attention and MLP.
 
@@ -391,9 +435,12 @@ def forward(
             indices: None or (total_nnz,)
             attn_mask: None or (batch, max_seqlen_in_batch)
             bias: None or (batch, heads, max_seqlen_in_batch, max_seqlen_in_batch)
+            slopes: None or (batch, heads) or (heads,)
         """
+        assert (bias is None) == (slopes is None), f'{bias=}, {slopes=}'
         attention_output = self.attention(hidden_states, cu_seqlens, seqlen,
-                                          subset_idx, indices, attn_mask, bias)
+                                          subset_idx, indices, attn_mask, bias,
+                                          slopes)
         layer_output = self.mlp(attention_output)
         return layer_output
 
@@ -463,6 +510,7 @@ def get_slopes_power_of_2(n_heads: int) -> List[float]:
         relative_position = relative_position.unsqueeze(0).expand(
             n_heads, -1, -1)
         slopes = torch.Tensor(_get_alibi_head_slopes(n_heads)).to(device)
+        self.slopes = slopes
         alibi = slopes.unsqueeze(1).unsqueeze(1) * -relative_position
         # [1, n_heads, max_token_length, max_token_length]
         alibi = alibi.unsqueeze(0)
@@ -504,6 +552,7 @@ def forward(
         elif self.alibi.device != hidden_states.device:
             # Device catch-up
             self.alibi = self.alibi.to(hidden_states.device)
+            self.slopes = self.slopes.to(hidden_states.device)
         alibi_bias = self.alibi[:, :, :seqlen, :seqlen]
         attn_bias = extended_attention_mask[:, :, :seqlen, :seqlen]
         alibi_attn_mask = attn_bias + alibi_bias
@@ -517,7 +566,8 @@ def forward(
                                              None,
                                              indices,
                                              attn_mask=attention_mask,
-                                             bias=alibi_attn_mask)
+                                             bias=alibi_attn_mask,
+                                             slopes=self.slopes)
                 if output_all_encoded_layers:
                     all_encoder_layers.append(hidden_states)
             # Pad inputs and mask. It will insert back zero-padded tokens.
@@ -536,7 +586,8 @@ def forward(
                                              None,
                                              indices,
                                              attn_mask=attention_mask,
-                                             bias=alibi_attn_mask)
+                                             bias=alibi_attn_mask,
+                                             slopes=self.slopes)
                 if output_all_encoded_layers:
                     all_encoder_layers.append(hidden_states)
             subset_idx = torch.nonzero(subset_mask[attention_mask_bool],
@@ -547,7 +598,8 @@ def forward(
                                            subset_idx=subset_idx,
                                            indices=indices,
                                            attn_mask=attention_mask,
-                                           bias=alibi_attn_mask)
+                                           bias=alibi_attn_mask,
+                                           slopes=self.slopes)
 
         if not output_all_encoded_layers:
             all_encoder_layers.append(hidden_states)

examples/benchmarks/bert/src/mosaic_bert.py

@@ -119,8 +119,7 @@ def create_mosaic_bert_mlm(pretrained_model_name: str = 'bert-base-uncased',
             pretrained_model_name)
 
     metrics = [
-        LanguageCrossEntropy(ignore_index=-100,
-                             vocab_size=model.config.vocab_size),
+        LanguageCrossEntropy(ignore_index=-100),
         MaskedAccuracy(ignore_index=-100)
     ]
 

examples/benchmarks/bert/src/text_data.py

@@ -69,9 +69,6 @@ class StreamingTextDataset(StreamingDataset):
         keep_zip (bool): Whether to keep or delete the compressed form when decompressing
             downloaded shards. If ``False``, keep iff remote is local or no remote. Defaults to
             `False``.
-        keep_raw (bool): Whether to keep or delete the decompressed form (or only form)
-            of shards after all their samples have been yielded this epoch. If ``False``, keep iff
-            remote is local or no remote and no compression. Defaults to ``True``.
         samples_per_epoch (int, optional): Provide this field iff you are weighting sub-datasets
             proportionally. Defaults to ``None``.
         predownload (int, optional): Target number of samples ahead to download the shards of while
@@ -99,7 +96,6 @@ def __init__(self,
                  download_timeout: float = 60,
                  validate_hash: Optional[str] = None,
                  keep_zip: bool = False,
-                 keep_raw: bool = True,
                  samples_per_epoch: Optional[int] = None,
                  predownload: int = 100_000,
                  partition_algo: str = 'orig',
@@ -140,7 +136,6 @@ def __init__(self,
             download_timeout=download_timeout,
             validate_hash=validate_hash,
             keep_zip=keep_zip,
-            keep_raw=keep_raw,
             samples_per_epoch=samples_per_epoch,
             predownload=predownload,
             partition_algo=partition_algo,
@@ -266,8 +261,6 @@ def build_text_dataloader(
                     cfg.dataset.get('validate_hash', None),
                     keep_zip=stream.get('keep_zip', None) or
                     cfg.dataset.get('keep_zip', False),
-                    keep_raw=stream.get('keep_raw', None) or
-                    cfg.dataset.get('keep_raw', True),
                 ))
 
     # build dataset potentially with streams
@@ -282,7 +275,6 @@ def build_text_dataloader(
         download_timeout=cfg.dataset.get('download_timeout', 60),
         validate_hash=cfg.dataset.get('validate_hash', None),
         keep_zip=cfg.dataset.get('keep_zip', False),
-        keep_raw=cfg.dataset.get('keep_raw', True),
         samples_per_epoch=cfg.dataset.get('samples_per_epoch', None),
         predownload=cfg.dataset.get('predownload', 100_000),
         partition_algo=cfg.dataset.get('partition_algo', 'orig'),