mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-08-08 11:27:24 +00:00
upgrade_bloom
This commit is contained in:
wangbluo
parent
08787f0b6e
commit
5480b811c5
@ -6,7 +6,7 @@ import torch.distributed as dist
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from torch.distributed import ProcessGroup
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from torch.nn import functional as F
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from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
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from transformers.cache_utils import Cache, DynamicCache
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from transformers.modeling_outputs import (
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BaseModelOutputWithPastAndCrossAttentions,
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CausalLMOutputWithCrossAttentions,
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@ -108,7 +108,7 @@ class BloomPipelineForwards:
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def bloom_model_forward(
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self: BloomModel,
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input_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
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past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]] = None,
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attention_mask: Optional[torch.Tensor] = None,
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head_mask: Optional[torch.LongTensor] = None,
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inputs_embeds: Optional[torch.LongTensor] = None,
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@ -116,6 +116,7 @@ class BloomPipelineForwards:
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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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cache_position: Optional[torch.LongTensor] = None,
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stage_manager: Optional[PipelineStageManager] = None,
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hidden_states: Optional[torch.FloatTensor] = None,
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stage_index: Optional[List[int]] = None,
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@ -151,6 +152,8 @@ class BloomPipelineForwards:
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if use_cache:
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logger.warning_once("use_cache=True is not supported for pipeline models at the moment.")
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use_cache = False
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past_key_values = None
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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 batch_size x num_heads x N x N
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@ -161,46 +164,60 @@ class BloomPipelineForwards:
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# case: First stage of training
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if stage_manager.is_first_stage():
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# check input_ids and inputs_embeds
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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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batch_size, seq_length = input_ids.shape
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elif inputs_embeds is not None:
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batch_size, seq_length, _ = inputs_embeds.shape
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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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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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hidden_states = self.word_embeddings_layernorm(inputs_embeds)
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# initialize in the first stage and then pass to the next stage
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else:
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input_shape = hidden_states.shape[:-1]
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batch_size, seq_length = input_shape
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# extra recording tensor should be generated in the first stage
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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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if self.gradient_checkpointing and self.training:
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if use_cache:
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if (input_ids is None) ^ (inputs_embeds is not None):
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raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
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if self.gradient_checkpointing and self.training and 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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if past_key_values is None:
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past_key_values = tuple([None] * len(self.h))
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# Compute alibi tensor: check build_alibi_tensor documentation,build for every stage
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seq_length_with_past = seq_length
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past_key_values_length = 0
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if past_key_values[0] is not None:
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past_key_values_length = past_key_values[0][0].shape[2] # source_len
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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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hidden_states = self.word_embeddings_layernorm(inputs_embeds)
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batch_size, seq_length, _ = inputs_embeds.shape
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past_length = past_key_values.get_seq_length() if past_key_values is not None else 0
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if cache_position is None:
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cache_position = torch.arange(past_length, past_length + seq_length, device=inputs_embeds.device)
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# initialize in the first stage and then pass to the next stage
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else:
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input_shape = hidden_states.shape[:-1]
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batch_size, seq_length = input_shape
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past_length = past_key_values.get_seq_length() if past_key_values is not None else 0
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if cache_position is None:
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cache_position = torch.arange(past_length, past_length + seq_length, device=hidden_states.device)
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# extra recording tensor should be generated in the first stage
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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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if self.gradient_checkpointing and self.training and 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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# kept for BC (non `Cache` `past_key_values` inputs)
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return_legacy_cache = False
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if use_cache and not isinstance(past_key_values, Cache):
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return_legacy_cache = True
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if past_key_values is None:
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past_key_values = DynamicCache()
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else:
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past_key_values = DynamicCache.from_legacy_cache(past_key_values)
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logger.warning_once(
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"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
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"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
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"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
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)
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# Compute alibi tensor: check build_alibi_tensor documentation,build for every stage
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past_length = 0
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seq_length_with_past = seq_length + past_length
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seq_length_with_past = seq_length_with_past + past_key_values_length
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if attention_mask is None:
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attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
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else:
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@ -209,13 +226,10 @@ class BloomPipelineForwards:
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alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
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# causal_mask is constructed every stage and its input is passed through different stages
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causal_mask = _prepare_4d_causal_attention_mask(
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attention_mask,
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input_shape=(batch_size, seq_length),
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inputs_embeds=hidden_states,
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past_key_values_length=past_key_values_length,
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causal_mask = self._update_causal_mask(
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attention_mask, hidden_states, cache_position, past_key_values, output_attentions
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)
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causal_mask = causal_mask.bool()
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# split the input tensor along sequence dimension
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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 and shard_config.enable_sequence_parallelism:
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@ -228,9 +242,7 @@ class BloomPipelineForwards:
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)
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start_idx, end_idx = stage_index[0], stage_index[1]
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for i, (block, layer_past) in enumerate(
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zip(self.h[start_idx:end_idx], past_key_values[start_idx:end_idx]), start=start_idx
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):
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for i, block in enumerate(self.h[start_idx:end_idx], start=start_idx):
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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@ -240,26 +252,28 @@ class BloomPipelineForwards:
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hidden_states,
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alibi,
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causal_mask,
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layer_past,
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past_key_values,
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head_mask[i],
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use_cache,
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output_attentions,
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cache_position,
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)
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else:
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outputs = block(
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hidden_states,
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layer_past=layer_past,
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layer_past=past_key_values,
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attention_mask=causal_mask,
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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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alibi=alibi,
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cache_position=cache_position,
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)
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hidden_states = outputs[0]
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if use_cache:
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next_decoder_cache = outputs[1]
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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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@ -277,20 +291,23 @@ class BloomPipelineForwards:
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# Add last hidden state
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hidden_states = self.ln_f(hidden_states)
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# TODO(jianghai): deal with all_hidden_states, all_self_attentions, presents
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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next_cache = next_decoder_cache if use_cache else None
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if return_legacy_cache:
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next_cache = next_cache.to_legacy_cache()
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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 for v in [hidden_states, next_cache, all_hidden_states, all_self_attentions] if v is not None
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)
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# attention_mask is not returned ; presents = past_key_values
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return BaseModelOutputWithPastAndCrossAttentions(
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last_hidden_state=hidden_states,
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past_key_values=presents,
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past_key_values=next_cache,
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hidden_states=all_hidden_states,
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attentions=all_self_attentions,
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)
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@ -845,7 +862,7 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
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def forward(
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self: BloomModel,
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input_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
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past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]] = None,
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attention_mask: Optional[torch.Tensor] = None,
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head_mask: Optional[torch.LongTensor] = None,
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inputs_embeds: Optional[torch.LongTensor] = None,
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@ -853,6 +870,7 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
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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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cache_position: Optional[torch.LongTensor] = None,
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**deprecated_arguments,
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) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
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if deprecated_arguments.pop("position_ids", False) is not False:
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@ -864,7 +882,6 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
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)
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if len(deprecated_arguments) > 0:
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raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
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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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@ -872,62 +889,60 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
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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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batch_size, seq_length = input_ids.shape
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elif inputs_embeds is not None:
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batch_size, seq_length, _ = inputs_embeds.shape
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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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if (input_ids is None) ^ (inputs_embeds is not None):
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raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
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if past_key_values is None:
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past_key_values = tuple([None] * len(self.h))
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if self.gradient_checkpointing and self.training and 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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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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# kept for BC (non `Cache` `past_key_values` inputs)
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return_legacy_cache = False
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if use_cache and not isinstance(past_key_values, Cache):
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return_legacy_cache = True
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if past_key_values is None:
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past_key_values = DynamicCache()
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else:
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past_key_values = DynamicCache.from_legacy_cache(past_key_values)
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logger.warning_once(
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"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
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"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
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"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
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)
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batch_size, seq_length, _ = inputs_embeds.shape
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past_length = past_key_values.get_seq_length() if past_key_values is not None else 0
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seq_length_with_past = seq_length + past_length
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if cache_position is None:
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cache_position = torch.arange(past_length, past_length + seq_length, device=inputs_embeds.device)
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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 batch_size x num_heads x N x N
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# head_mask has shape n_layer x batch x num_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.word_embeddings(input_ids)
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hidden_states = self.word_embeddings_layernorm(inputs_embeds)
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presents = () if use_cache else None
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next_decoder_cache = 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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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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# Compute alibi tensor: check build_alibi_tensor documentation
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seq_length_with_past = seq_length
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past_key_values_length = 0
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if 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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seq_length_with_past = seq_length_with_past + past_key_values_length
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if attention_mask is None:
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attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
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else:
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attention_mask = attention_mask.to(hidden_states.device)
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alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
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causal_mask = _prepare_4d_causal_attention_mask(
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attention_mask,
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input_shape=(batch_size, seq_length),
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inputs_embeds=hidden_states,
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past_key_values_length=past_key_values_length,
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causal_mask = self._update_causal_mask(
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attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
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)
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causal_mask = causal_mask.bool()
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# split the input tensor along sequence dimension
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# [batch_size, seq_len, hidden_size] -> [batch_size, seq_len/TP_size, hidden_size]
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hidden_states = split_forward_gather_backward(
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hidden_states,
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dim=1,
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@ -935,7 +950,7 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
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fp8_communication=shard_config.fp8_communication,
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)
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for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
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for i, block in enumerate(self.h):
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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@ -945,48 +960,49 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
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hidden_states,
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alibi,
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causal_mask,
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layer_past,
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past_key_values,
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head_mask[i],
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use_cache,
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output_attentions,
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cache_position,
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)
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else:
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outputs = block(
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hidden_states,
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layer_past=layer_past,
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layer_past=past_key_values,
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attention_mask=causal_mask,
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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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alibi=alibi,
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cache_position=cache_position,
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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 use_cache:
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next_decoder_cache = 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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# When sequence parallelism done, gather the output tensor in forward and split it in backward
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hidden_states = gather_forward_split_backward(
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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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fp8_communication=shard_config.fp8_communication,
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)
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# Add last hidden state
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hidden_states = self.ln_f(hidden_states)
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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next_cache = next_decoder_cache if use_cache else None
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if return_legacy_cache:
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next_cache = next_cache.to_legacy_cache()
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if not return_dict:
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return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
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return tuple(
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v for v in [hidden_states, next_cache, all_hidden_states, all_self_attentions] if v is not None
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)
|
||||
|
||||
return BaseModelOutputWithPastAndCrossAttentions(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=presents,
|
||||
past_key_values=next_cache,
|
||||
hidden_states=all_hidden_states,
|
||||
attentions=all_self_attentions,
|
||||
)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user