refactor baichuan2-13b (#11064)
This commit is contained in:
Yishuo Wang
GitHub
parent
67db925112
commit
31ce3e0c13
2 changed files with 88 additions and 234 deletions
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@ -23,19 +23,13 @@ from typing import Optional, Tuple
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import torch
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import torch.utils.checkpoint
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from torch.nn import functional as F
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from ipex_llm.transformers.models.utils import init_fp8_kv_cache, append_fp8_kv_cache, \
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restore_fp8_kv_cache, use_quantize_kv_cache
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from ipex_llm.transformers.models.utils import init_kv_cache, extend_kv_cache, \
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append_kv_cache, is_enough_kv_cache_room_4_31
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from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
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from ipex_llm.transformers.models.utils import update_past_key_value
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from ipex_llm.transformers.models.utils import should_use_fuse_rope
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from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp, use_sdp_causal
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from ipex_llm.transformers.models.utils import apply_rotary_pos_emb, SILU
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from ipex_llm.transformers.models.utils import mlp_fusion_check
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import warnings
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import os
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KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
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def pre_compute_inv_freq(module: torch.nn.Module):
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@ -114,52 +108,16 @@ def baichuan_attention_forward_7b(
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# IPEX-LLM OPT: kv cache and quantize kv
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use_quantize_kv = use_quantize_kv_cache(self.W_pack, hidden_states)
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if use_quantize_kv:
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if past_key_value is None:
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k_cache, v_cache = init_fp8_kv_cache(
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bsz, self.num_heads, kv_seq_len, self.head_dim,
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device=device
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key_states, value_states = update_past_key_value(
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past_key_value, key_states, value_states,
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kv_seq_len, use_quantize_kv, device
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)
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else:
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k_cache, v_cache = past_key_value
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key_states, value_states = append_fp8_kv_cache(k_cache, v_cache,
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key_states, value_states)
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else:
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if past_key_value is None:
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max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
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k_cache, v_cache = init_kv_cache(bsz,
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self.num_heads,
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self.head_dim,
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kv_seq_len,
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max_cache_length,
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dtype=key_states.dtype,
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device=device)
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k_cache[...] = key_states
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v_cache[...] = value_states
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key_states = k_cache
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value_states = v_cache
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else:
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k_cache, v_cache = past_key_value
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if k_cache.stride(1) < kv_seq_len * k_cache.size(3):
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max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
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new_k_cache, new_v_cache = extend_kv_cache(bsz,
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self.num_heads,
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self.head_dim,
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k_cache.size(2),
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max_cache_length,
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dtype=k_cache.dtype,
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device=device)
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new_k_cache[...] = k_cache
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new_v_cache[...] = v_cache
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k_cache = new_k_cache
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v_cache = new_v_cache
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key_states, value_states = append_kv_cache(k_cache, v_cache, key_states, value_states)
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past_key_value = (key_states, value_states) if use_cache else None
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if self.training:
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warnings.warn("xops is not supported on Intel GPU, so just use normal implementation")
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# IPEX-LLM OPT: sdp
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attn_weights = None
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if not self.training and not hidden_states.requires_grad and \
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use_flash_attention(query_states, key_states, attention_mask):
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@ -211,207 +169,56 @@ def baichuan_attention_forward_13b(
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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if use_quantize_kv_cache(self.W_pack, hidden_states):
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forward_function = baichuan_attention_forward_13b_quantized
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else:
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forward_function = baichuan_attention_forward_13b_origin
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return forward_function(
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self=self,
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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past_key_value=past_key_value,
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output_attentions=output_attentions,
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use_cache=use_cache,
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)
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def baichuan_attention_forward_13b_quantized(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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device = hidden_states.device
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proj = self.W_pack(hidden_states)
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proj = (
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proj.unflatten(-1, (3, self.hidden_size))
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.unsqueeze(0)
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.transpose(0, -2)
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.squeeze(-2)
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)
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query_states = (
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proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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)
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key_states = (
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proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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)
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value_states = (
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proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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)
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if past_key_value is None:
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kv_seq_len = key_states.shape[-2]
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k_cache, v_cache = init_fp8_kv_cache(
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bsz, self.num_heads, kv_seq_len, self.head_dim,
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device=device
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)
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else:
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k_cache, v_cache = past_key_value
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key_states, value_states = append_fp8_kv_cache(k_cache, v_cache,
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key_states, value_states)
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past_key_value = (key_states, value_states)
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if query_states.size(2) != 1 or device.type != 'xpu':
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key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
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query_states.dtype)
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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3))
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else:
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import linear_q4_0
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attn_weights = linear_q4_0.query_key_fp8_matmul(query_states, key_states)
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attn_weights = attn_weights / math.sqrt(self.head_dim)
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if attention_mask is not None:
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if q_len == 1: # inference with cache
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if len(attention_mask.size()) == 4:
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attention_mask = attention_mask[:, :, -1:, :]
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else:
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attention_mask = attention_mask[:, -1:, :]
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attn_weights = attn_weights + attention_mask
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attn_weights = torch.max(attn_weights,
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torch.tensor(torch.finfo(attn_weights.dtype).min))
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attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
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attn_weights = attn_weights.to(hidden_states.dtype)
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if query_states.size(2) != 1 or device.type != 'xpu':
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attn_output = torch.matmul(attn_weights, value_states)
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else:
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import linear_q4_0
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attn_output = linear_q4_0.attn_value_fp8_matmul(attn_weights, value_states)
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attn_output = attn_output.transpose(1, 2)
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
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attn_output = self.o_proj(attn_output)
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if not output_attentions:
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attn_weights = None
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return attn_output, attn_weights, past_key_value
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def baichuan_attention_forward_13b_origin(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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device = hidden_states.device
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proj = self.W_pack(hidden_states)
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proj = (
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proj.unflatten(-1, (3, self.hidden_size))
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.unsqueeze(0)
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.transpose(0, -2)
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.squeeze(-2)
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)
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query_states = (
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proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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)
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key_states = (
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proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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)
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value_states = (
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proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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)
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kv_seq_len = key_states.shape[-2]
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enough_kv_room = True
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if past_key_value is not None:
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enough_kv_room = is_enough_kv_cache_room_4_31(past_key_value, seq_len=kv_seq_len)
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kv_seq_len += past_key_value[0].shape[-2]
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# if past_key_value is not None:
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# # reuse k, v, self_attention
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# key_states = torch.cat([past_key_value[0], key_states], dim=2)
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# value_states = torch.cat([past_key_value[1], value_states], dim=2)
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if past_key_value is not None:
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# reuse k, v, self_attention
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cache_k = past_key_value[0]
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cache_v = past_key_value[1]
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if not enough_kv_room:
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if device.type == 'xpu':
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torch.xpu.empty_cache()
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# allocate new
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new_cache_k, new_cache_v = extend_kv_cache(bsz,
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qkv = self.W_pack(hidden_states)
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qkv = qkv.view(bsz, q_len, self.num_heads * 3, self.head_dim)
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qkv = qkv.transpose(1, 2)
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query_states, key_states, value_states = qkv.split([self.num_heads,
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self.num_heads,
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self.head_dim,
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cache_k.size(2),
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kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
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dtype=cache_k.dtype,
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device=device)
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new_cache_k[:] = cache_k
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new_cache_v[:] = cache_v
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cache_k = new_cache_k
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cache_v = new_cache_v
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self.num_heads], dim=1)
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key_states, value_states = append_kv_cache(cache_k, cache_v, key_states, value_states)
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elif use_cache:
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max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
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new_key_states, new_value_states = init_kv_cache(bsz,
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self.num_heads,
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self.head_dim,
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kv_seq_len,
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max_cache_length,
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dtype=key_states.dtype,
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device=device)
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new_key_states[:] = key_states
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new_value_states[:] = value_states
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key_states = new_key_states
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value_states = new_value_states
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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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# IPEX-LLM OPT: kv cache and quantize kv
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use_quantize_kv = use_quantize_kv_cache(self.W_pack, hidden_states)
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key_states, value_states = update_past_key_value(
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past_key_value, key_states, value_states,
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kv_seq_len, use_quantize_kv, device
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)
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past_key_value = (key_states, value_states) if use_cache else None
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if self.training:
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warnings.warn("xops is not supported on Intel GPU, so just use normal implementation")
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attn_weights = torch.matmul(
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query_states.to(dtype=key_states.dtype), key_states.transpose(2, 3)
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) / math.sqrt(self.head_dim)
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if attention_mask is not None:
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if q_len == 1: # inference with cache
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if len(attention_mask.size()) == 4:
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attention_mask = attention_mask[:, :, -1:, :]
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attention_mask = attention_mask[:, :, -q_len:, :]
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else:
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attention_mask = attention_mask[:, -1:, :]
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if attention_mask.shape[-2] == attn_weights.shape[-2]:
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attn_weights = attn_weights + attention_mask
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else:
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# support for Baichuan/Baichuan2 13B Chat running speculative decoding
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# split attention mask on dim -2
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split_sizes = [attention_mask.shape[-2] - attn_weights.shape[-2],
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attn_weights.shape[-2]]
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# the last chunk of splited is the new attention mask
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attention_mask = attention_mask.split(split_sizes, dim=-2)[-1]
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attn_weights = attn_weights + attention_mask
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attn_weights = torch.max(
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attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
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)
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attention_mask = attention_mask[:, None, -q_len:, :]
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if use_quantize_kv and q_len == 1:
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import linear_q4_0
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attn_weights = linear_q4_0.query_key_fp8_matmul(query_states, key_states)
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else:
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if use_quantize_kv:
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key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
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query_states.dtype)
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attn_weights = torch.matmul(query_states,
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key_states.transpose(2, 3))
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attn_weights = attn_weights / math.sqrt(self.head_dim)
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if attention_mask is not None:
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attn_weights = attn_weights + attention_mask
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attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
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if use_quantize_kv and q_len == 1:
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import linear_q4_0
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attn_output = linear_q4_0.attn_value_fp8_matmul(attn_weights, value_states)
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else:
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attn_output = torch.matmul(attn_weights.to(dtype=value_states.dtype), value_states)
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attn_output = attn_output.transpose(1, 2)
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
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attn_output = self.o_proj(attn_output)
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@ -24,6 +24,7 @@ from ipex_llm.transformers.low_bit_linear import SYM_INT4, SYM_INT8, FP8E5, IQ2_
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from ipex_llm.transformers.convert import is_deepspeed_available
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FP8_KV_ALLOC_LENGTH = 512
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KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
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# used in fused mlp forward
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SILU = 0
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@ -426,3 +427,49 @@ def fp16_fusion_check(proj, x, training):
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if device_type != "pvc":
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return False
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return True
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def update_past_key_value(past_key_value, key_states, value_states,
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kv_seq_len, use_quantize_kv, device):
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bsz, num_heads, _, head_dim = key_states.shape
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if use_quantize_kv:
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if past_key_value is None:
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k_cache, v_cache = init_fp8_kv_cache(
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bsz, num_heads, kv_seq_len, head_dim,
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device=device
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)
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else:
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k_cache, v_cache = past_key_value
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key_states, value_states = append_fp8_kv_cache(k_cache, v_cache,
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key_states, value_states)
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else:
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if past_key_value is None:
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max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
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k_cache, v_cache = init_kv_cache(bsz,
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num_heads,
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head_dim,
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kv_seq_len,
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max_cache_length,
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dtype=key_states.dtype,
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device=device)
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k_cache[...] = key_states
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v_cache[...] = value_states
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key_states = k_cache
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value_states = v_cache
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else:
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k_cache, v_cache = past_key_value
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if k_cache.stride(1) < kv_seq_len * k_cache.size(3):
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max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
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new_k_cache, new_v_cache = extend_kv_cache(bsz,
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num_heads,
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head_dim,
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k_cache.size(2),
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max_cache_length,
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dtype=k_cache.dtype,
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device=device)
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new_k_cache[...] = k_cache
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new_v_cache[...] = v_cache
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k_cache = new_k_cache
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v_cache = new_v_cache
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key_states, value_states = append_kv_cache(k_cache, v_cache, key_states, value_states)
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return key_states, value_states
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