Codegeex support (#12303)
* new codegeex attn * use kv cache * add compress/quantize kv * remove compress/quantize kv * fix style check * fix style * fix codegeex
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Xin Qiu
GitHub
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72605c7016
commit
97a0f7fd35
2 changed files with 233 additions and 1 deletions
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@ -1364,7 +1364,7 @@ def _optimize_post(model, lightweight_bmm=False):
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and model.config.architectures[0] in ["ChatGLMModel", "ChatGLMForConditionalGeneration"]
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and model.config.architectures[0] in ["ChatGLMModel", "ChatGLMForConditionalGeneration"]
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):
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):
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if hasattr(model.config, 'padded_vocab_size') and \
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if hasattr(model.config, 'padded_vocab_size') and \
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model.config.padded_vocab_size in [65024, 64896]:
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model.config.padded_vocab_size == 65024:
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# chatglm2-6b, chatglm2-6b-32k, chatglm3-6b, chatglm3-6b-32k, chatglm3-6b-128k
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# chatglm2-6b, chatglm2-6b-32k, chatglm3-6b, chatglm3-6b-32k, chatglm3-6b-128k
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modeling_module_name = model.__class__.__module__
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modeling_module_name = model.__class__.__module__
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module = importlib.import_module(modeling_module_name)
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module = importlib.import_module(modeling_module_name)
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@ -1384,6 +1384,27 @@ def _optimize_post(model, lightweight_bmm=False):
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convert_forward(model,
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convert_forward(model,
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module.RMSNorm,
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module.RMSNorm,
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chatglm_rms_norm_forward)
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chatglm_rms_norm_forward)
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elif hasattr(model.config, 'padded_vocab_size') and \
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model.config.padded_vocab_size == 64896:
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# codegeex-nano
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modeling_module_name = model.__class__.__module__
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module = importlib.import_module(modeling_module_name)
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from ipex_llm.transformers.models.chatglm2 import codegeex_attention_forward
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from ipex_llm.transformers.models.chatglm2 import chatglm_rms_norm_forward
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from ipex_llm.transformers.models.chatglm2 import chatglm2_encoder_forward
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from ipex_llm.transformers.models.chatglm2 import codegeex_model_forward
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convert_forward(model,
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module.SelfAttention,
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codegeex_attention_forward)
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convert_forward(model,
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module.GLMTransformer,
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chatglm2_encoder_forward)
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convert_forward(model,
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module.ChatGLMModel,
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codegeex_model_forward)
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convert_forward(model,
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module.RMSNorm,
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chatglm_rms_norm_forward)
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elif hasattr(model.config, 'vocab_size') and model.config.vocab_size == 130528:
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elif hasattr(model.config, 'vocab_size') and model.config.vocab_size == 130528:
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# chatglm-6b
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# chatglm-6b
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modeling_module_name = model.__class__.__module__
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modeling_module_name = model.__class__.__module__
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@ -359,3 +359,214 @@ def chatglm2_attention_forward(
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output = self.dense(attn_output)
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output = self.dense(attn_output)
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return output, past_key_value
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return output, past_key_value
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@torch.jit.script
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def apply_rotary_pos_emb_original(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor:
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# x: [sq, b, np, hn]
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sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3)
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rot_dim = rope_cache.shape[-2] * 2
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x, x_pass = x[..., :rot_dim], x[..., rot_dim:]
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# truncate to support variable sizes
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rope_cache = rope_cache[:sq]
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xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2)
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rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2)
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x_out2 = torch.stack(
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[
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xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1],
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xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1],
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],
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-1,
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)
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x_out2 = x_out2.flatten(3)
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return torch.cat((x_out2, x_pass), dim=-1)
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def codegeex_model_forward(
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self,
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input_ids,
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position_ids: Optional[torch.Tensor]=None,
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attention_mask: Optional[torch.BoolTensor]=None,
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full_attention_mask: Optional[torch.BoolTensor]=None,
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past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]=None,
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inputs_embeds: Optional[torch.Tensor]=None,
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use_cache: 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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):
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None
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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 inputs_embeds is None:
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batch_size, seq_length = input_ids.shape
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inputs_embeds = self.embedding(input_ids)
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else:
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inputs_embeds = inputs_embeds.transpose(0, 1).contiguous()
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seq_length, batch_size, _ = inputs_embeds.shape
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input_ids = torch.empty((batch_size, seq_length),
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dtype=inputs_embeds.dtype, device=inputs_embeds.device)
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if full_attention_mask is None:
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if (attention_mask is not None and not attention_mask.all()) or (
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past_key_values and seq_length != 1):
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full_attention_mask = self.get_masks(input_ids,
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past_key_values,
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padding_mask=attention_mask)
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# ipex-llm changes begin
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# 1. replace `rotary_pos_emb` with `inv_freq` and `position_ids`
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# 2. generate `causal_mask` and replace `full_attention_mask` with it
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if position_ids is None:
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if past_key_values is None:
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position_ids = torch.arange(seq_length, dtype=torch.int64, device=inputs_embeds.device)
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else:
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if isinstance(past_key_values, DynamicCompressCache):
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kv_length = past_key_values.get_seq_length()
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else:
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kv_length = past_key_values[0][0].size(0)
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position_ids = torch.arange(kv_length, kv_length + seq_length,
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dtype=torch.int64, device=inputs_embeds.device)
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position_ids = position_ids.repeat(batch_size, 1)
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use_fuse_rope = input_ids.device.type == "xpu" and not self.training
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# Rotary positional embeddings
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rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
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if position_ids is not None:
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rotary_pos_emb = rotary_pos_emb[position_ids]
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else:
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rotary_pos_emb = rotary_pos_emb[None, :seq_length]
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if use_fuse_rope:
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# Repeat cos sin here, call only once for each token.
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# Chatglm2's rotary embedding is similar to gptj's, is rotate_every_two.
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# If put this to attension forward, it will generate too many times.
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cos, sin = rotary_pos_emb.split(rotary_pos_emb.shape[-1] // 2, dim=-1)
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cos = cos.squeeze(-1)
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sin = sin.squeeze(-1)
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cos = torch.repeat_interleave(cos[:, :, None, :], 2, 3)
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sin = torch.repeat_interleave(sin[:, :, None, :], 2, 3)
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rotary_pos_emb = (cos, sin)
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else:
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rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous()
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# `full_attention_mask` is not None only when
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# `past_key_values` is not None and `seq_length` > 1
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if full_attention_mask is not None:
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causal_mask = torch.zeros([batch_size, 1, seq_length, full_attention_mask.size(-1)],
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dtype=inputs_embeds.dtype, device=inputs_embeds.device)
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mask_value = torch.finfo(inputs_embeds.dtype).min
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causal_mask.masked_fill_(full_attention_mask, mask_value)
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elif self.training or (inputs_embeds.device.type != "xpu" and past_key_values is None):
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full_attention_mask = self.get_masks(input_ids,
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past_key_values,
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padding_mask=attention_mask)
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causal_mask = torch.zeros([batch_size, 1, seq_length, full_attention_mask.size(-1)],
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dtype=inputs_embeds.dtype, device=inputs_embeds.device)
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mask_value = torch.finfo(inputs_embeds.dtype).min
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causal_mask.masked_fill_(full_attention_mask, mask_value)
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else:
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causal_mask = None
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# Run encoder.
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hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder(
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inputs_embeds, causal_mask,
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rotary_pos_emb=rotary_pos_emb,
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kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states
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)
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# ipex-llm changes end
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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]
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if v is not None)
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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,
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)
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def codegeex_attention_forward(
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self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True
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):
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q_len, bsz, _ = hidden_states.size()
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n_head = self.num_attention_heads_per_partition
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n_kv_head = self.num_multi_query_groups_per_partition if self.multi_query_attention else n_head
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head_dim = self.hidden_size_per_attention_head
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past_key_value = None if kv_cache is None else (kv_cache[0].permute(1, 2, 0, 3),
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kv_cache[1].permute(1, 2, 0, 3))
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qkv = self.query_key_value(hidden_states)
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qkv = qkv.view(q_len, bsz, n_head + 2 * n_kv_head, head_dim)
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# [seq_len, bsz, n_head, head_dim] -> [bsz, n_head, seq_len, head_dim]
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qkv = qkv.permute(1, 2, 0, 3)
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query_layer, key_layer, value_layer = qkv.split([n_head,
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n_kv_head,
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n_kv_head], dim=1)
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kv_seq_len = key_layer.shape[2]
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if past_key_value is not None:
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kv_seq_len += past_key_value[0].shape[2]
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# apply relative positional encoding (rotary embedding)
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if len(rotary_pos_emb) == 2 and isinstance(rotary_pos_emb, tuple):
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cos, sin = rotary_pos_emb
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rot_dim = cos.shape[-1]
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query_layer = query_layer.transpose(1, 2)
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key_layer = key_layer.transpose(1, 2)
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query_layer_cur = query_layer[..., :rot_dim]
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key_layer_cur = key_layer[..., :rot_dim]
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# ipex_llm's apply_rotary_embedding can change the origin storage,
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# so query_layer will get the result directly.
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torch.ops.torch_ipex.apply_rotary_embedding(query_layer_cur, sin, cos, query_layer_cur)
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torch.ops.torch_ipex.apply_rotary_embedding(key_layer_cur, sin, cos, key_layer_cur)
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query_layer = query_layer.transpose(1, 2)
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key_layer = key_layer.transpose(1, 2)
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else:
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query_layer = apply_rotary_pos_emb_original(query_layer, rotary_pos_emb)
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key_layer = apply_rotary_pos_emb_original(key_layer, rotary_pos_emb)
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key_layer, value_layer = update_past_key_value(
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past_key_value, key_layer, value_layer,
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kv_seq_len, False, hidden_states.device
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)
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# past_key_value: [bsz, n_kv_head, seq_len, head_dim] -> [seq_len, bsz, n_kv_head, head_dim]
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past_key_value = (key_layer.permute(2, 0, 1, 3),
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value_layer.permute(2, 0, 1, 3)) if use_cache else None
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# =================
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# Output. [sq, b, h]
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# =================
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context_layer = None
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if use_sdp(q_len, kv_seq_len, head_dim, query_layer):
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import xe_addons
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context_layer = xe_addons.sdp(query_layer, key_layer, value_layer, attention_mask)
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elif use_sdp_causal(q_len, kv_seq_len, head_dim, query_layer, self.training):
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import xe_addons
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context_layer = xe_addons.sdp_causal(query_layer, key_layer, value_layer, attention_mask)
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else:
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# repeat k/v heads if n_kv_heads < n_heads
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key_layer = repeat_kv(key_layer, n_head // n_kv_head)
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value_layer = repeat_kv(value_layer, n_head // n_kv_head)
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if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]:
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context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer,
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key_layer,
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value_layer,
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is_causal=True)
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else:
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if attention_mask is not None:
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attention_mask = ~attention_mask
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context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer,
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key_layer,
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value_layer,
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attention_mask)
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context_layer = context_layer.permute(2, 0, 1, 3).contiguous().view(q_len,
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bsz,
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n_head * head_dim)
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output = self.dense(context_layer)
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return output, past_key_value
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