mirror of
https://github.com/hpcaitech/ColossalAI.git
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[shardformer] update colo attention to support custom mask (#5510)
* [feature] refactor colo attention (#5462) * [extension] update api * [feature] add colo attention * [feature] update sdpa * [feature] update npu attention * [feature] update flash-attn * [test] add flash attn test * [test] update flash attn test * [shardformer] update modeling to fit colo attention (#5465) * [misc] refactor folder structure * [shardformer] update llama flash-attn * [shardformer] fix llama policy * [devops] update tensornvme install * [test] update llama test * [shardformer] update colo attn kernel dispatch * [shardformer] update blip2 * [shardformer] update chatglm * [shardformer] update gpt2 * [shardformer] update gptj * [shardformer] update opt * [shardformer] update vit * [shardformer] update colo attention mask prep * [shardformer] update whisper * [test] fix shardformer tests (#5514) * [test] fix shardformer tests * [test] fix shardformer tests
This commit is contained in:
Hongxin Liu
GitHub
@@ -19,9 +19,54 @@ from transformers.models.gptj.modeling_gptj import (
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from transformers.utils import is_torch_fx_proxy, logging
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from colossalai.pipeline.stage_manager import PipelineStageManager
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from colossalai.shardformer.layer import ColoAttention
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from colossalai.shardformer.layer._operation import gather_forward_split_backward, split_forward_gather_backward
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from colossalai.shardformer.shard import ShardConfig
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logger = logging.get_logger(__name__)
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def _get_attention_mask(
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self: GPTJModel,
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shard_config: ShardConfig,
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hidden_states: torch.Tensor,
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past_key_values: Optional[Tuple[Tuple[torch.Tensor]]],
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attention_mask: Optional[torch.FloatTensor],
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) -> Optional[Union[torch.Tensor, dict]]:
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batch_size, seq_len = hidden_states.shape[:2]
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past_key_values_length = 0
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if past_key_values is not None and past_key_values[0] is not None:
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past_key_values_length = past_key_values[0][0].shape[2]
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if shard_config.enable_flash_attention:
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if attention_mask is not None:
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attention_mask = attention_mask.view(batch_size, -1)
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attention_mask = ColoAttention.prepare_attn_kwargs(
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(batch_size, 1, seq_len, seq_len + past_key_values_length),
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hidden_states.dtype,
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hidden_states.device,
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attention_mask,
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is_causal=True,
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)
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elif attention_mask is not None:
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if batch_size <= 0:
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raise ValueError("batch_size has to be defined and > 0")
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attention_mask = attention_mask.view(batch_size, -1)
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# We create a 3D attention mask from a 2D tensor mask.
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# Sizes are [batch_size, 1, 1, to_seq_length]
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# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
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# this attention mask is more simple than the triangular masking of causal attention
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# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
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attention_mask = attention_mask[:, None, None, :]
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# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
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# masked positions, this operation will create a tensor which is 0.0 for
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# positions we want to attend and the dtype's smallest value for masked positions.
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# Since we are adding it to the raw scores before the softmax, this is
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# effectively the same as removing these entirely.
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attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
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attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
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return attention_mask
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class GPTJPipelineForwards:
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"""
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@@ -96,26 +141,6 @@ class GPTJPipelineForwards:
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batch_size, seq_length = input_shape[0], input_shape[1]
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device = hidden_states.device
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# Attention mask.
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if attention_mask is not None:
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if batch_size <= 0:
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raise ValueError("batch_size has to be defined and > 0")
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attention_mask = attention_mask.view(batch_size, -1)
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# We create a 3D attention mask from a 2D tensor mask.
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# Sizes are [batch_size, 1, 1, to_seq_length]
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# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
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# this attention mask is more simple than the triangular masking of causal attention
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# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
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attention_mask = attention_mask[:, None, None, :]
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# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
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# masked positions, this operation will create a tensor which is 0.0 for
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# positions we want to attend and the dtype's smallest value for masked positions.
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# Since we are adding it to the raw scores before the softmax, this is
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# effectively the same as removing these entirely.
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attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
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attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
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# Prepare head mask if needed
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# 1.0 in head_mask indicate we keep the head
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# attention_probs has shape bsz x num_attention_heads x N x N
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@@ -139,6 +164,8 @@ class GPTJPipelineForwards:
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output_shape = input_shape + (hidden_states.size(-1),)
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attention_mask = _get_attention_mask(self, shard_config, hidden_states, past_key_values, attention_mask)
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if self.gradient_checkpointing and self.training:
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if use_cache:
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logger.warning_once(
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@@ -154,7 +181,9 @@ class GPTJPipelineForwards:
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# [batch_size, seq_len, hidden_size] -> [batch_size, seq_len/TP_size, hidden_size]
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if shard_config.enable_sequence_parallelism:
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hidden_states = split_forward_gather_backward(
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hidden_states, dim=1, process_group=shard_config.tensor_parallel_process_group
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hidden_states,
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dim=1,
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process_group=shard_config.tensor_parallel_process_group,
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)
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# Going through held blocks.
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@@ -209,7 +238,9 @@ class GPTJPipelineForwards:
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# When sequence parallelism done, gather the output tensor in forward and split it in backward
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if shard_config.enable_sequence_parallelism:
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hidden_states = gather_forward_split_backward(
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hidden_states, dim=1, process_group=shard_config.tensor_parallel_process_group
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hidden_states,
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dim=1,
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process_group=shard_config.tensor_parallel_process_group,
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)
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if stage_manager.is_last_stage():
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@@ -223,7 +254,14 @@ class GPTJPipelineForwards:
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if stage_manager.is_last_stage():
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if not return_dict:
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return tuple(
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v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None
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v
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for v in [
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hidden_states,
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presents,
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all_hidden_states,
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all_self_attentions,
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]
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if v is not None
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)
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return BaseModelOutputWithPast(
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@@ -530,24 +568,11 @@ class GPTJPipelineForwards:
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def get_gptj_flash_attention_forward():
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from transformers.models.gptj.modeling_gptj import GPTJAttention
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from colossalai.nn.layer.colo_attention import AttnMaskType, ColoAttention
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def split_heads(tensor, num_attention_heads, attn_head_size, rotary):
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"""
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Splits hidden dim into attn_head_size and num_attention_heads
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"""
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new_shape = tensor.size()[:-1] + (num_attention_heads, attn_head_size)
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tensor = tensor.view(new_shape)
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if rotary or len(tensor.shape) in [4, 5]:
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return tensor
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else:
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raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}")
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def forward(
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self: GPTJAttention,
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hidden_states: torch.FloatTensor,
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layer_past: Optional[Tuple[torch.Tensor]] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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attention_mask: Optional[dict] = None,
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position_ids: Optional[torch.LongTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = False,
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@@ -556,13 +581,14 @@ def get_gptj_flash_attention_forward():
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Tuple[torch.Tensor, Tuple[torch.Tensor]],
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Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]],
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]:
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assert head_mask is None, "head_mask is not supported for FlashAttention"
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query = self.q_proj(hidden_states)
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key = self.k_proj(hidden_states)
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value = self.v_proj(hidden_states)
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query = split_heads(query, self.num_attention_heads, self.head_dim, True)
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key = split_heads(key, self.num_attention_heads, self.head_dim, True)
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value = split_heads(value, self.num_attention_heads, self.head_dim, False)
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query = self._split_heads(query, self.num_attention_heads, self.head_dim, True)
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key = self._split_heads(key, self.num_attention_heads, self.head_dim, True)
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value = self._split_heads(value, self.num_attention_heads, self.head_dim, False)
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if is_torch_fx_proxy(position_ids) or torch.jit.is_tracing():
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# The logic to conditionally copy to GPU could not be traced, so we do this
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@@ -591,41 +617,23 @@ def get_gptj_flash_attention_forward():
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key = apply_rotary_pos_emb(key, sin, cos)
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query = apply_rotary_pos_emb(query, sin, cos)
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# key = key.permute(0, 2, 1, 3)
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# query = query.permute(0, 2, 1, 3)
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key = key.to(dtype=value.dtype) # fp16 compatibility
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query = query.to(dtype=value.dtype)
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key = key.permute(0, 2, 1, 3)
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query = query.permute(0, 2, 1, 3)
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if layer_past is not None:
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past_key = layer_past[0]
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past_value = layer_past[1]
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key = torch.cat((past_key, key), dim=1)
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value = torch.cat((past_value, value), dim=1)
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key = torch.cat((past_key, key), dim=-2)
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value = torch.cat((past_value, value), dim=-2)
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if use_cache is True:
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present = (key, value)
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else:
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present = None
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# use AttnMaskType and ColoAttention
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attn_mask_type = AttnMaskType.causal
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flash_attention_mask = None
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if attention_mask != None:
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if attn_mask_type == AttnMaskType.causal:
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attn_mask_type == AttnMaskType.paddedcausal
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else:
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attn_mask_type = AttnMaskType.padding
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flash_attention_mask = ~(attention_mask[:, :, -1].squeeze(1).to(torch.bool)).contiguous()
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# use coloattention
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scale = value.size(-1) ** -0.5
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attention = ColoAttention(
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embed_dim=self.embed_dim, num_heads=self.num_attention_heads, dropout=self.attn_dropout.p, scale=scale
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)
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attn_output = attention(query, key, value, attn_mask=flash_attention_mask, attn_mask_type=attn_mask_type)
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dropout_p = self.attn_dropout.p if self.training else 0.0
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attn_output = ColoAttention.attention(query, key, value, **attention_mask, dropout_p=dropout_p)
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attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_dim)
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attn_output = self.out_proj(attn_output)
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attn_output = self.resid_dropout(attn_output)
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outputs = (attn_output, present, None)
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@@ -635,6 +643,180 @@ def get_gptj_flash_attention_forward():
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return forward
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def gptj_model_forward_for_flash_attention(shard_config: ShardConfig):
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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token_type_ids: Optional[torch.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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) -> Union[Tuple, BaseModelOutputWithPast]:
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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if input_ids is not None and inputs_embeds is not None:
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raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
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elif input_ids is not None:
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self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
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input_shape = input_ids.size()
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input_ids = input_ids.view(-1, input_shape[-1])
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input_ids.shape[0]
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elif inputs_embeds is not None:
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input_shape = inputs_embeds.size()[:-1]
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inputs_embeds.shape[0]
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else:
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raise ValueError("You have to specify either input_ids or inputs_embeds")
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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if token_type_ids is not None:
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token_type_ids = token_type_ids.view(-1, input_shape[-1])
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if position_ids is not None:
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position_ids = position_ids.view(-1, input_shape[-1]).long()
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if past_key_values is None:
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past_length = 0
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past_key_values = tuple([None] * len(self.h))
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else:
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past_length = past_key_values[0][0].size(-2)
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if position_ids is None:
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position_ids = torch.arange(
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past_length,
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input_shape[-1] + past_length,
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dtype=torch.long,
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device=device,
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)
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position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
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# Prepare head mask if needed
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# 1.0 in head_mask indicate we keep the head
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# attention_probs has shape bsz x num_attention_heads x N x N
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# head_mask has shape n_layer x batch x num_attention_heads x N x N
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head_mask = self.get_head_mask(head_mask, self.config.n_layer)
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if inputs_embeds is None:
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inputs_embeds = self.wte(input_ids)
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hidden_states = inputs_embeds
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if token_type_ids is not None:
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token_type_embeds = self.wte(token_type_ids)
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hidden_states = hidden_states + token_type_embeds
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hidden_states = self.drop(hidden_states)
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attention_mask = _get_attention_mask(self, shard_config, hidden_states, past_key_values, attention_mask)
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output_shape = (-1,) + input_shape[1:] + (hidden_states.size(-1),)
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if self.gradient_checkpointing and self.training:
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if use_cache:
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logger.warning_once(
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"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
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)
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use_cache = False
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presents = () if use_cache else None
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all_self_attentions = () if output_attentions else None
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all_hidden_states = () if output_hidden_states else None
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for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
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# Model parallel
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if self.model_parallel:
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torch.cuda.set_device(hidden_states.device)
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# Ensure layer_past is on same device as hidden_states (might not be correct)
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if layer_past is not None:
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layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past)
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# Ensure that attention_mask is always on the same device as hidden_states
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if attention_mask is not None:
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attention_mask = attention_mask.to(hidden_states.device)
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if isinstance(head_mask, torch.Tensor):
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head_mask = head_mask.to(hidden_states.device)
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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if self.gradient_checkpointing and self.training:
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def create_custom_forward(module):
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def custom_forward(*inputs):
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# None for past_key_value
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return module(*inputs, use_cache, output_attentions)
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return custom_forward
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outputs = torch.utils.checkpoint.checkpoint(
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create_custom_forward(block),
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hidden_states,
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None,
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attention_mask,
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position_ids,
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head_mask[i],
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)
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else:
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outputs = block(
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hidden_states=hidden_states,
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layer_past=layer_past,
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attention_mask=attention_mask,
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position_ids=position_ids,
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head_mask=head_mask[i],
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use_cache=use_cache,
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output_attentions=output_attentions,
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)
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hidden_states = outputs[0]
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if use_cache is True:
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presents = presents + (outputs[1],)
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if output_attentions:
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all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
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# Model Parallel: If it's the last layer for that device, put things on the next device
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if self.model_parallel:
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for k, v in self.device_map.items():
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if i == v[-1] and "cuda:" + str(k) != self.last_device:
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hidden_states = hidden_states.to("cuda:" + str(k + 1))
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hidden_states = self.ln_f(hidden_states)
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hidden_states = hidden_states.view(output_shape)
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# Add last hidden state
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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if not return_dict:
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return tuple(
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v
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for v in [
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hidden_states,
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presents,
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all_hidden_states,
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all_self_attentions,
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]
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if v is not None
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)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=presents,
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hidden_states=all_hidden_states,
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attentions=all_self_attentions,
|
||||
)
|
||||
|
||||
return forward
|
||||
|
||||
|
||||
def gptj_sequence_parallel_forward_fn(shard_config: ShardConfig):
|
||||
def forward(
|
||||
self,
|
||||
@@ -662,10 +844,10 @@ def gptj_sequence_parallel_forward_fn(shard_config: ShardConfig):
|
||||
elif input_ids is not None:
|
||||
input_shape = input_ids.size()
|
||||
input_ids = input_ids.view(-1, input_shape[-1])
|
||||
batch_size = input_ids.shape[0]
|
||||
input_ids.shape[0]
|
||||
elif inputs_embeds is not None:
|
||||
input_shape = inputs_embeds.size()[:-1]
|
||||
batch_size = inputs_embeds.shape[0]
|
||||
inputs_embeds.shape[0]
|
||||
else:
|
||||
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
||||
|
||||
@@ -684,29 +866,14 @@ def gptj_sequence_parallel_forward_fn(shard_config: ShardConfig):
|
||||
past_length = past_key_values[0][0].size(-2)
|
||||
|
||||
if position_ids is None:
|
||||
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
|
||||
position_ids = torch.arange(
|
||||
past_length,
|
||||
input_shape[-1] + past_length,
|
||||
dtype=torch.long,
|
||||
device=device,
|
||||
)
|
||||
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
|
||||
|
||||
# Attention 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
|
||||
|
||||
# Prepare head mask if needed
|
||||
# 1.0 in head_mask indicate we keep the head
|
||||
# attention_probs has shape bsz x num_attention_heads x N x N
|
||||
@@ -725,6 +892,7 @@ def gptj_sequence_parallel_forward_fn(shard_config: ShardConfig):
|
||||
hidden_states = self.drop(hidden_states)
|
||||
|
||||
output_shape = input_shape + (hidden_states.size(-1),)
|
||||
attention_mask = _get_attention_mask(self, shard_config, hidden_states, past_key_values, attention_mask)
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
if use_cache:
|
||||
@@ -740,7 +908,9 @@ def gptj_sequence_parallel_forward_fn(shard_config: ShardConfig):
|
||||
# split the input tensor along sequence dimension
|
||||
# [batch_size, seq_len, hidden_size] -> [batch_size, seq_len/TP_size, hidden_size]
|
||||
hidden_states = split_forward_gather_backward(
|
||||
hidden_states, dim=1, process_group=shard_config.tensor_parallel_process_group
|
||||
hidden_states,
|
||||
dim=1,
|
||||
process_group=shard_config.tensor_parallel_process_group,
|
||||
)
|
||||
|
||||
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
|
||||
@@ -801,7 +971,9 @@ def gptj_sequence_parallel_forward_fn(shard_config: ShardConfig):
|
||||
|
||||
# When sequence parallelism done, gather the output tensor in forward and split it in backward
|
||||
hidden_states = gather_forward_split_backward(
|
||||
hidden_states, dim=1, process_group=shard_config.tensor_parallel_process_group
|
||||
hidden_states,
|
||||
dim=1,
|
||||
process_group=shard_config.tensor_parallel_process_group,
|
||||
)
|
||||
|
||||
hidden_states = self.ln_f(hidden_states)
|
||||
@@ -812,7 +984,16 @@ def gptj_sequence_parallel_forward_fn(shard_config: ShardConfig):
|
||||
all_hidden_states = all_hidden_states + (hidden_states,)
|
||||
|
||||
if not return_dict:
|
||||
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
|
||||
return tuple(
|
||||
v
|
||||
for v in [
|
||||
hidden_states,
|
||||
presents,
|
||||
all_hidden_states,
|
||||
all_self_attentions,
|
||||
]
|
||||
if v is not None
|
||||
)
|
||||
|
||||
return BaseModelOutputWithPast(
|
||||
last_hidden_state=hidden_states,
|
||||
|
||||
Reference in New Issue
Block a user