use new rotary two in chatglm4 (#11312)
* use new rotary two in chatglm4 * rempve
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Xin Qiu
GitHub
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f1410d6823
commit
1b0c4c8cb8
1 changed files with 32 additions and 65 deletions
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@ -47,10 +47,6 @@ def chatglm4_model_forward(
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) -> Union[Tuple, BaseModelOutputWithPast]:
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from ipex_llm.transformers.kv import DynamicFp8Cache
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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# if use_cache and use_quantize_kv_cache(
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# self.encoder.layers[0].self_attention.query_key_value, input_ids):
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# if not isinstance(past_key_values, DynamicFp8Cache):
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# past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
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return chatglm4_model_forward_internal(
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self=self,
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input_ids=input_ids,
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@ -108,25 +104,17 @@ def chatglm4_model_forward_internal(
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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"
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use_fuse_rope = use_fuse_rope 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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if getattr(self.rotary_pos_emb, "cached_dtype", None) != inputs_embeds.dtype:
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rot_dim = self.rotary_pos_emb.dim
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base = 10000 * getattr(self.rotary_pos_emb, "rope_ratio", 1)
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# We should generate float inv_freq to avoid overflow, as base is too large.
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inv_freq = 1.0 / (base ** (torch.arange(0, rot_dim, 2,
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dtype=torch.float,
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device=inputs_embeds.device) / rot_dim))
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self.rotary_pos_emb.register_buffer("inv_freq",
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inv_freq.to(inputs_embeds.dtype),
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persistent=False)
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self.rotary_pos_emb.cached = True
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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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@ -148,7 +136,7 @@ def chatglm4_model_forward_internal(
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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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rotary_pos_emb=(self.rotary_pos_emb.inv_freq, position_ids),
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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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@ -172,26 +160,6 @@ def chatglm4_model_forward_internal(
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)
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def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor:
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# x: [b, np, sq, hn]
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b, np, sq, 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(b, np, sq, rot_dim // 2, 2)
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rope_cache = rope_cache.view(-1, 1, sq, 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 chatglm4_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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@ -209,34 +177,33 @@ def chatglm4_attention_forward(
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qkv = self.query_key_value(hidden_states)
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# [bs, q_len, np * 3 * hn] -> [bsz, n_head, seq_len, head_dim]
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qkv = qkv.view(bsz, q_len, n_head + 2 * n_kv_head, head_dim)
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qkv = qkv.transpose(1, 2)
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query_states, key_states, value_states = qkv.split([n_head,
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n_kv_head,
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n_kv_head], dim=2)
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n_kv_head], dim=1)
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kv_seq_len = key_states.shape[1]
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kv_seq_len = key_states.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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if isinstance(rotary_pos_emb, tuple) and len(rotary_pos_emb) == 2:
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# use_fuse_rope, see chatglm4_model_forward
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cos, sin = rotary_pos_emb
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rot_dim = cos.shape[-1]
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query_layer_cur = query_states[..., :rot_dim]
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key_layer_cur = key_states[..., :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_states = query_states.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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value_states = value_states.transpose(1, 2)
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elif rotary_pos_emb is not None:
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query_states = query_states.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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value_states = value_states.transpose(1, 2)
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query_states = apply_rotary_pos_emb(query_states, rotary_pos_emb)
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key_states = apply_rotary_pos_emb(key_states, rotary_pos_emb)
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# IPEX-LLM OPT: fuse rope
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inv_freq, position_ids = rotary_pos_emb
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rot_dim = inv_freq.size(-1) * 2
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if should_use_fuse_rope(hidden_states, rotary_pos_emb[1], self.training):
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import xe_addons
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xe_addons.rotary_two_inplaced(inv_freq, position_ids,
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query_states[..., :rot_dim], key_states[..., :rot_dim])
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else:
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idx_theta = torch.outer(position_ids[0].float(),
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inv_freq.float()).to(hidden_states.dtype)
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idx_theta = idx_theta.unsqueeze(0).unsqueeze(0)
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cos = torch.cos(idx_theta).repeat_interleave(2, -1)
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sin = torch.sin(idx_theta).repeat_interleave(2, -1)
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q_rot, k_rot = apply_rotary_pos_emb(query_states[..., :rot_dim], key_states[..., :rot_dim],
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cos, sin, position_ids, "chatglm")
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query_states[..., :rot_dim] = q_rot[...]
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key_states[..., :rot_dim] = k_rot[...]
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# IPEX-LLM OPT: kv cache and quantize kv
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use_quantize_kv = use_quantize_kv_cache(self.query_key_value, hidden_states)
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