Refactor baichuan1 7B and 13B (#11258)
This commit is contained in:
Yishuo Wang
GitHub
parent
1aa9c9597a
commit
ea0d03fd28
3 changed files with 253 additions and 822 deletions
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@ -680,6 +680,11 @@ def _optimize_pre(model):
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if model.lm_head.weight.data.device != "meta":
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norm_weight = nn.functional.normalize(lm_head_weight_data)
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model.lm_head.weight.data = norm_weight
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# for baichuan2-7B
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if model.config.hidden_size in [4096, 2048]:
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from ipex_llm.transformers.models.baichuan import pre_compute_inv_freq
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model.apply(pre_compute_inv_freq)
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# for yuan 2.0
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if model.config.model_type == "yuan":
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from ipex_llm.transformers.models.yuan import merge_qk
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@ -703,12 +708,6 @@ def _optimize_pre(model):
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model.apply(pre_compute_inv_freq)
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from ipex_llm.transformers.models.phi3 import split_mlp
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model.apply(split_mlp)
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# for baichuan2
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if model.config.model_type == "baichuan" and model.config.vocab_size == 125696:
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if model.config.hidden_size in [4096, 2048]:
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# baichuan2-7B
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from ipex_llm.transformers.models.baichuan2 import pre_compute_inv_freq
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model.apply(pre_compute_inv_freq)
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# for qwen2
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if model.config.model_type == "qwen2":
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from ipex_llm.transformers.models.qwen2 import merge_qkv
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@ -1125,84 +1124,39 @@ def _optimize_post(model, lightweight_bmm=False):
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module.FalconAttention,
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falcon_attention_forward
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)
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elif model.config.model_type == "baichuan" and model.config.vocab_size == 125696:
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# baichuan2
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if model.config.hidden_size in [4096, 2048]:
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# baichuan2-7B
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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.baichuan2 import baichuan_attention_forward_7b
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from ipex_llm.transformers.models.baichuan2 import baichuan_mlp_forward
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convert_forward(model,
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module.Attention,
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baichuan_attention_forward_7b
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)
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convert_forward(model,
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module.RMSNorm,
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llama_rms_norm_forward)
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convert_forward(model,
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module.MLP,
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baichuan_mlp_forward)
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elif model.config.hidden_size == 5120:
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# baichuan2-13B
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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.baichuan2 import baichuan_attention_forward_13b
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from ipex_llm.transformers.models.baichuan2 import baichuan_13b_rms_norm_forward
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from ipex_llm.transformers.models.baichuan2 import baichuan_mlp_forward
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from ipex_llm.transformers.models.baichuan2 import baichuan_13b_get_alibi_mask
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convert_forward(model,
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module.BaichuanAttention,
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baichuan_attention_forward_13b
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)
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# baichuan2-13B's RMSNorm is a little different
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convert_forward(model,
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module.RMSNorm,
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baichuan_13b_rms_norm_forward)
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convert_forward(model,
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module.MLP,
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baichuan_mlp_forward)
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if hasattr(model.model, 'get_alibi_mask_orig'):
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# deepspeed rewrite "get_alibi_mask" to support baichuan
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# https://github.com/microsoft/DeepSpeed/pull/4721
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replace_func(model,
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module.BaichuanModel,
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"get_alibi_mask_orig",
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baichuan_13b_get_alibi_mask)
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else:
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replace_func(model,
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module.BaichuanModel,
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"get_alibi_mask",
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baichuan_13b_get_alibi_mask)
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elif model.config.model_type == "baichuan":
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# baichuan1
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if model.config.hidden_size == 4096:
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# baichuan-7B
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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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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.baichuan import baichuan_mlp_forward
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convert_forward(model, module.MLP, baichuan_mlp_forward)
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if model.config.hidden_size in [4096, 2048]:
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# baichuan-7B and baichuan2-7B
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from ipex_llm.transformers.models.baichuan import baichuan_attention_forward_7b
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convert_forward(model,
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module.Attention,
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baichuan_attention_forward_7b
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)
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convert_forward(model,
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module.RMSNorm,
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llama_rms_norm_forward)
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convert_forward(model, module.Attention, baichuan_attention_forward_7b)
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convert_forward(model, module.RMSNorm, llama_rms_norm_forward)
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elif model.config.hidden_size == 5120:
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# baichuan-13B
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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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# baichuan-13B and baichuan2-13B
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from ipex_llm.transformers.models.baichuan import baichuan_attention_forward_13b
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from ipex_llm.transformers.models.baichuan2 import baichuan_13b_rms_norm_forward
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convert_forward(model,
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module.BaichuanAttention,
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baichuan_attention_forward_13b
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)
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# baichuan-13B's RMSNorm is a little different
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convert_forward(model,
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module.RMSNorm,
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baichuan_13b_rms_norm_forward)
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from ipex_llm.transformers.models.baichuan import baichuan_13b_rms_norm_forward
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convert_forward(model, module.BaichuanAttention, baichuan_attention_forward_13b)
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convert_forward(model, module.RMSNorm, baichuan_13b_rms_norm_forward)
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if model.config.vocab_size == 125696:
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# baichaun2-13B
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from ipex_llm.transformers.models.baichuan import baichuan_13b_get_alibi_mask
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if hasattr(model.model, 'get_alibi_mask_orig'):
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# deepspeed rewrite "get_alibi_mask" to support baichuan
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# https://github.com/microsoft/DeepSpeed/pull/4721
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replace_func(model,
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module.BaichuanModel,
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"get_alibi_mask_orig",
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baichuan_13b_get_alibi_mask)
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else:
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replace_func(model,
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module.BaichuanModel,
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"get_alibi_mask",
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baichuan_13b_get_alibi_mask)
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elif model.config.model_type == "gpt_neox":
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from ipex_llm.transformers.models.gptneox import gptneox_attention_forward
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convert_forward(model,
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@ -14,30 +14,61 @@
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# limitations under the License.
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# This file is adapted from
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# https://huggingface.co/baichuan-inc/Baichuan-7B/blob/c1a5c7d5b7f50ecc51bb0e08150a9f12e5656756/modeling_baichuan.py
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# https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/cb7fc748b78b7ea99772e4cf76db155729ce774e/modeling_baichuan.py
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# and
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# https://huggingface.co/baichuan-inc/Baichuan-13B-Chat/blob/a4a558127068f2ce965aa56aeb826bf501a68970/modeling_baichuan.py
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# https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/blob/c6f8592a60b4ad73c210b28dd2ab3cca51abbf93/modeling_baichuan.py
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import math
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from typing import List, Optional, Tuple, Union
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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 import nn
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import torch.nn.functional as F
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ipex_llm.utils.common import invalidInputError
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from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp
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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 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 rotate_half, apply_rotary_pos_emb
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from ipex_llm.transformers.models.utils import apply_rotary_pos_emb_no_cache_xpu
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from torch.nn import functional as F
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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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if module.__class__.__name__ == "RotaryEmbedding":
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inv_freq = module.inv_freq
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del module.inv_freq
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module.register_buffer("inv_freq", inv_freq, persistent=False)
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def baichuan_13b_rms_norm_forward(self, hidden_states):
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if hidden_states.device.type == "xpu" and not (self.training or hidden_states.requires_grad):
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import xe_addons
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x_2d = hidden_states.reshape(-1, hidden_states.size(-1)).contiguous()
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output = xe_addons.rms_norm(self.weight, x_2d, self.epsilon)
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return output.reshape(hidden_states.shape)
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.epsilon)
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return self.weight * hidden_states.to(input_dtype)
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def baichuan_mlp_forward(
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self,
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x: torch.Tensor,
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) -> torch.Tensor:
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x_2d = x.view(-1, x.shape[-1])
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qtype = getattr(self.gate_proj, "qtype", None)
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if mlp_fusion_check(x_2d, qtype, self.training) and not self.down_proj.enable_xetla:
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import xe_linear
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if not x_2d.is_contiguous():
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x_2d = x_2d.contiguous()
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return self.down_proj(xe_linear.mlp_forward_xpu(
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x_2d, self.gate_proj.weight.data, self.up_proj.weight.data,
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x_2d.shape[0], x_2d.shape[1], self.gate_proj.out_len,
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SILU, qtype
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))
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return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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def baichuan_attention_forward_7b(
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@ -48,269 +79,82 @@ def baichuan_attention_forward_7b(
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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_7b_quantized
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else:
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forward_function = baichuan_attention_forward_7b_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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position_ids=position_ids,
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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_7b_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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position_ids: Optional[torch.LongTensor] = 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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):
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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 = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
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# batch_size x source_len x hidden_size
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query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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# batch_size x target_len x head_size
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key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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# batch_size x source_len x hidden_size
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value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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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.num_heads], dim=1)
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kv_seq_len = key_states.shape[-2]
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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 query_states.device.type == "xpu" and not (self.training and query_states.requires_grad):
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query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
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key_states,
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position_ids,
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"baichuan")
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kv_seq_len += past_key_value[0].shape[2]
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# IPEX-LLM OPT: fuse rope
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if should_use_fuse_rope(hidden_states, position_ids, self.training):
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import xe_addons
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xe_addons.rotary_half_inplaced(self.rotary_emb.inv_freq, position_ids,
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query_states, key_states)
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else:
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
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cos, sin, position_ids, "baichuan")
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# [bsz, nh, t, hd]
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query_states = query_states.to(hidden_states.dtype)
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key_states = key_states.to(hidden_states.dtype)
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if past_key_value is None:
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attn_weights = torch.matmul(query_states,
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key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
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invalidInputError(
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False,
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f"Attention weights should be of size "
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f"{(bsz, self.num_heads, q_len, kv_seq_len)}"
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f", but is {attn_weights.size()}"
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)
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if attention_mask is not None:
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invalidInputError(
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attention_mask.size() == (bsz, 1, q_len, kv_seq_len),
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f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, "
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f"but is {attention_mask.size()}"
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)
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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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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1,
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dtype=torch.float32).to(query_states.dtype)
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attn_output = torch.matmul(attn_weights, value_states)
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kv_seq_len = key_states.shape[-2]
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if use_cache:
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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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key_states, value_states = append_fp8_kv_cache(k_cache, v_cache, key_states,
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value_states)
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past_key_value = (key_states, value_states)
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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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kv_seq_len = key_states.shape[-2]
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past_key_value = (key_states, value_states)
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if query_states.size(2) != 1 or query_states.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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attn_weights = attn_weights / math.sqrt(self.head_dim)
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if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
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invalidInputError(
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False,
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f"Attention weights should be of size "
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f"{(bsz, self.num_heads, q_len, kv_seq_len)}"
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f", but is {attn_weights.size()}"
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)
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if attention_mask is not None:
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invalidInputError(
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attention_mask.size() == (bsz, 1, q_len, kv_seq_len),
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f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, "
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f"but is {attention_mask.size()}"
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)
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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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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1,
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dtype=torch.float32).to(query_states.dtype)
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attn_output = torch.matmul(attn_weights, value_states)
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else:
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import xe_addons
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attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states,
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attention_mask)
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attn_weights = None
|
||||
|
||||
invalidInputError(
|
||||
attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
|
||||
f"`attn_output` should be of size "
|
||||
f"{(bsz, self.num_heads, q_len, self.head_dim)},"
|
||||
f"but is {attn_output.size()}"
|
||||
# IPEX-LLM OPT: kv cache and quantize kv
|
||||
use_quantize_kv = use_quantize_kv_cache(self.W_pack, hidden_states)
|
||||
key_states, value_states = update_past_key_value(
|
||||
past_key_value, key_states, value_states,
|
||||
kv_seq_len, use_quantize_kv, device
|
||||
)
|
||||
|
||||
attn_output = attn_output.transpose(1, 2)
|
||||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
||||
|
||||
attn_output = self.o_proj(attn_output)
|
||||
|
||||
if not output_attentions:
|
||||
attn_weights = None
|
||||
|
||||
return attn_output.to(hidden_states.dtype), attn_weights, past_key_value
|
||||
|
||||
|
||||
def baichuan_attention_forward_7b_origin(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||||
output_attentions: bool = False,
|
||||
use_cache: bool = False,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
device = hidden_states.device
|
||||
|
||||
proj = self.W_pack(hidden_states)
|
||||
proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
|
||||
# batch_size x source_len x hidden_size
|
||||
query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
# batch_size x target_len x head_size
|
||||
key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
# batch_size x source_len x hidden_size
|
||||
value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
|
||||
kv_seq_len = key_states.shape[-2]
|
||||
enough_kv_room = True
|
||||
if past_key_value is not None:
|
||||
enough_kv_room = is_enough_kv_cache_room_4_31(past_key_value, seq_len=kv_seq_len)
|
||||
kv_seq_len += past_key_value[0].shape[-2]
|
||||
if query_states.device.type == "xpu" and not (self.training and query_states.requires_grad):
|
||||
query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
|
||||
key_states,
|
||||
position_ids,
|
||||
"baichuan")
|
||||
else:
|
||||
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
|
||||
cos, sin, position_ids, "baichuan")
|
||||
# [bsz, nh, t, hd]
|
||||
|
||||
# if past_key_value is not None:
|
||||
# # reuse k, v, self_attention
|
||||
# key_states = torch.cat([past_key_value[0], key_states], dim=2)
|
||||
# value_states = torch.cat([past_key_value[1], value_states], dim=2)
|
||||
if past_key_value is not None:
|
||||
# reuse k, v, self_attention
|
||||
cache_k = past_key_value[0]
|
||||
cache_v = past_key_value[1]
|
||||
if not enough_kv_room:
|
||||
# allocate new
|
||||
new_cache_k, new_cache_v = extend_kv_cache(bsz,
|
||||
self.num_heads,
|
||||
self.head_dim,
|
||||
cache_k.size(2),
|
||||
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
|
||||
dtype=cache_k.dtype,
|
||||
device=device)
|
||||
new_cache_k[:] = cache_k
|
||||
new_cache_v[:] = cache_v
|
||||
cache_k = new_cache_k
|
||||
cache_v = new_cache_v
|
||||
|
||||
key_states, value_states = append_kv_cache(cache_k, cache_v, key_states, value_states)
|
||||
|
||||
elif use_cache:
|
||||
max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
|
||||
new_key_states, new_value_states = init_kv_cache(bsz,
|
||||
self.num_heads,
|
||||
self.head_dim,
|
||||
kv_seq_len,
|
||||
max_cache_length,
|
||||
dtype=key_states.dtype,
|
||||
device=device)
|
||||
new_key_states[:] = key_states
|
||||
new_value_states[:] = value_states
|
||||
key_states = new_key_states
|
||||
value_states = new_value_states
|
||||
|
||||
past_key_value = (key_states, value_states) if use_cache else None
|
||||
|
||||
if self.training:
|
||||
warnings.warn("xops is not supported on Intel GPU, so just use normal implementation")
|
||||
|
||||
# IPEX-LLM OPT: sdp
|
||||
attn_weights = None
|
||||
if not self.training and not hidden_states.requires_grad and \
|
||||
use_flash_attention(query_states, key_states, attention_mask):
|
||||
attn_output = F.scaled_dot_product_attention(query_states.to(device, dtype=torch.float16),
|
||||
key_states.to(device, dtype=torch.float16),
|
||||
value_states.to(device, dtype=torch.float16),
|
||||
is_causal=True)
|
||||
attn_weights = None
|
||||
elif not self.training and not hidden_states.requires_grad and \
|
||||
use_sdp(q_len, key_states.shape[2], self.head_dim, query_states):
|
||||
attn_output = F.scaled_dot_product_attention(query_states.to(dtype=torch.float16),
|
||||
key_states.to(dtype=torch.float16),
|
||||
value_states.to(dtype=torch.float16),
|
||||
is_causal=True).to(hidden_states.dtype)
|
||||
elif use_sdp(q_len, kv_seq_len, self.head_dim, query_states):
|
||||
import xe_addons
|
||||
attn_output = xe_addons.sdp(query_states, key_states, value_states,
|
||||
attention_mask)
|
||||
attn_output = attn_output.view(query_states.shape)
|
||||
attn_weights = None
|
||||
if use_quantize_kv:
|
||||
attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states,
|
||||
attention_mask)
|
||||
else:
|
||||
attn_output = xe_addons.sdp(query_states, key_states, value_states,
|
||||
attention_mask)
|
||||
elif use_sdp_causal(q_len, kv_seq_len, self.head_dim, query_states, self.training):
|
||||
import xe_addons
|
||||
if use_quantize_kv:
|
||||
attn_output = xe_addons.sdp_fp8_causal(query_states, key_states,
|
||||
value_states, attention_mask)
|
||||
else:
|
||||
attn_output = xe_addons.sdp_causal(query_states, key_states,
|
||||
value_states, attention_mask)
|
||||
else:
|
||||
if use_quantize_kv:
|
||||
key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
|
||||
query_states.dtype)
|
||||
attn_weights = torch.matmul(query_states,
|
||||
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
||||
|
||||
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
||||
invalidInputError(False,
|
||||
f"Attention weights should be of size "
|
||||
f"{(bsz, self.num_heads, q_len, kv_seq_len)}"
|
||||
f", but is {attn_weights.size()}")
|
||||
|
||||
if attention_mask is not None:
|
||||
invalidInputError(attention_mask.size() == (bsz, 1, q_len, kv_seq_len),
|
||||
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, "
|
||||
f"but is {attention_mask.size()}")
|
||||
attn_weights = attn_weights + attention_mask
|
||||
attn_weights = torch.max(attn_weights,
|
||||
torch.tensor(torch.finfo(attn_weights.dtype).min))
|
||||
|
||||
# upcast attention to fp32
|
||||
attn_weights = nn.functional.softmax(attn_weights, dim=-1,
|
||||
dtype=torch.float32).to(query_states.dtype)
|
||||
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1,
|
||||
dtype=torch.float32).to(value_states.dtype)
|
||||
attn_output = torch.matmul(attn_weights, value_states)
|
||||
|
||||
invalidInputError(attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
|
||||
f"`attn_output` should be of size "
|
||||
f"{(bsz, self.num_heads, q_len, self.head_dim)},"
|
||||
f"but is {attn_output.size()}")
|
||||
|
||||
attn_output = attn_output.transpose(1, 2)
|
||||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
||||
|
||||
attn_output = self.o_proj(attn_output)
|
||||
|
|
@ -318,7 +162,7 @@ def baichuan_attention_forward_7b_origin(
|
|||
if not output_attentions:
|
||||
attn_weights = None
|
||||
|
||||
return attn_output.to(hidden_states.dtype), attn_weights, past_key_value
|
||||
return attn_output, attn_weights, past_key_value
|
||||
|
||||
|
||||
def baichuan_attention_forward_13b(
|
||||
|
|
@ -329,101 +173,57 @@ def baichuan_attention_forward_13b(
|
|||
output_attentions: bool = False,
|
||||
use_cache: bool = False,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
if use_quantize_kv_cache(self.W_pack, hidden_states):
|
||||
forward_function = baichuan_attention_forward_13b_quantized
|
||||
else:
|
||||
forward_function = baichuan_attention_forward_13b_origin
|
||||
return forward_function(
|
||||
self=self,
|
||||
hidden_states=hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
past_key_value=past_key_value,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache
|
||||
)
|
||||
|
||||
|
||||
def baichuan_attention_forward_13b_quantized(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||||
output_attentions: bool = False,
|
||||
use_cache: bool = False,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
device = hidden_states.device
|
||||
|
||||
proj = self.W_pack(hidden_states)
|
||||
proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
|
||||
query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
qkv = self.W_pack(hidden_states)
|
||||
qkv = qkv.view(bsz, q_len, self.num_heads * 3, self.head_dim)
|
||||
qkv = qkv.transpose(1, 2)
|
||||
query_states, key_states, value_states = qkv.split([self.num_heads,
|
||||
self.num_heads,
|
||||
self.num_heads], dim=1)
|
||||
|
||||
kv_seq_len = key_states.shape[-2]
|
||||
kv_seq_len = key_states.shape[2]
|
||||
if past_key_value is not None:
|
||||
kv_seq_len += past_key_value[0].shape[-2]
|
||||
kv_seq_len += past_key_value[0].shape[2]
|
||||
|
||||
if past_key_value is None:
|
||||
# IPEX-LLM OPT: kv cache and quantize kv
|
||||
use_quantize_kv = use_quantize_kv_cache(self.W_pack, hidden_states)
|
||||
key_states, value_states = update_past_key_value(
|
||||
past_key_value, key_states, value_states,
|
||||
kv_seq_len, use_quantize_kv, device
|
||||
)
|
||||
past_key_value = (key_states, value_states) if use_cache else None
|
||||
|
||||
if self.training:
|
||||
warnings.warn("xops is not supported on Intel GPU, so just use normal implementation")
|
||||
|
||||
if attention_mask is not None:
|
||||
if len(attention_mask.size()) == 4:
|
||||
attention_mask = attention_mask[:, :, -q_len:, :]
|
||||
else:
|
||||
attention_mask = attention_mask[None, :, -q_len:, :]
|
||||
|
||||
if use_sdp(q_len, kv_seq_len, self.head_dim, query_states):
|
||||
import xe_addons
|
||||
if use_quantize_kv:
|
||||
attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states,
|
||||
attention_mask)
|
||||
else:
|
||||
attn_output = xe_addons.sdp(query_states, key_states, value_states,
|
||||
attention_mask)
|
||||
attn_weights = None
|
||||
else:
|
||||
if use_quantize_kv:
|
||||
key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
|
||||
query_states.dtype)
|
||||
attn_weights = torch.matmul(query_states,
|
||||
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
||||
if attention_mask is not None:
|
||||
if q_len == 1: # inference with cache
|
||||
if len(attention_mask.size()) == 4:
|
||||
attention_mask = attention_mask[:, :, -1:, :]
|
||||
else:
|
||||
attention_mask = attention_mask[:, -1:, :]
|
||||
attn_weights = attn_weights + attention_mask
|
||||
attn_weights = torch.max(attn_weights,
|
||||
torch.tensor(torch.finfo(attn_weights.dtype).min))
|
||||
|
||||
attn_weights = attn_weights.to(query_states.dtype)
|
||||
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
|
||||
|
||||
attn_output = torch.matmul(attn_weights, value_states)
|
||||
kv_seq_len = key_states.shape[-2]
|
||||
if use_cache:
|
||||
k_cache, v_cache = init_fp8_kv_cache(
|
||||
bsz, self.num_heads, kv_seq_len, self.head_dim,
|
||||
device=device
|
||||
)
|
||||
key_states, value_states = append_fp8_kv_cache(k_cache, v_cache,
|
||||
key_states, value_states)
|
||||
past_key_value = (key_states, value_states)
|
||||
else:
|
||||
k_cache, v_cache = past_key_value
|
||||
key_states, value_states = append_fp8_kv_cache(k_cache, v_cache,
|
||||
key_states, value_states)
|
||||
kv_seq_len = key_states.shape[-2]
|
||||
past_key_value = (key_states, value_states)
|
||||
if query_states.size(2) != 1 or query_states.device.type != 'xpu':
|
||||
key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
|
||||
query_states.dtype)
|
||||
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3))
|
||||
else:
|
||||
import xe_addons
|
||||
attn_weights = xe_addons.query_key_fp8_matmul(query_states, key_states)
|
||||
|
||||
attn_weights = attn_weights / math.sqrt(self.head_dim)
|
||||
|
||||
if attention_mask is not None:
|
||||
if q_len == 1: # inference with cache
|
||||
if len(attention_mask.size()) == 4:
|
||||
attention_mask = attention_mask[:, :, -1:, :]
|
||||
else:
|
||||
attention_mask = attention_mask[:, -1:, :]
|
||||
attn_weights = attn_weights + attention_mask
|
||||
attn_weights = torch.max(attn_weights,
|
||||
torch.tensor(torch.finfo(attn_weights.dtype).min))
|
||||
|
||||
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
|
||||
if query_states.size(2) != 1 or query_states.device.type != 'xpu':
|
||||
attn_output = torch.matmul(attn_weights, value_states)
|
||||
else:
|
||||
import xe_addons
|
||||
attn_output = xe_addons.attn_value_fp8_matmul(attn_weights,
|
||||
value_states)
|
||||
|
||||
attn_output = torch.matmul(attn_weights.to(dtype=value_states.dtype), value_states)
|
||||
attn_output = attn_output.transpose(1, 2)
|
||||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
||||
attn_output = self.o_proj(attn_output)
|
||||
|
|
@ -434,90 +234,92 @@ def baichuan_attention_forward_13b_quantized(
|
|||
return attn_output, attn_weights, past_key_value
|
||||
|
||||
|
||||
def baichuan_attention_forward_13b_origin(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||||
output_attentions: bool = False,
|
||||
use_cache: bool = False,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
def _get_interleave(n):
|
||||
def _get_interleave_power_of_2(n):
|
||||
start = 2 ** (-(2 ** -(math.log2(n) - 3)))
|
||||
ratio = start
|
||||
return [start * ratio**i for i in range(n)]
|
||||
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
device = hidden_states.device
|
||||
if math.log2(n).is_integer():
|
||||
return _get_interleave_power_of_2(n)
|
||||
else:
|
||||
closest_power_of_2 = 2 ** math.floor(math.log2(n))
|
||||
return (
|
||||
_get_interleave_power_of_2(closest_power_of_2)
|
||||
+ _get_interleave(2 * closest_power_of_2)[0::2][: n - closest_power_of_2]
|
||||
)
|
||||
|
||||
proj = self.W_pack(hidden_states)
|
||||
proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
|
||||
query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
|
||||
kv_seq_len = key_states.shape[-2]
|
||||
enough_kv_room = True
|
||||
if past_key_value is not None:
|
||||
enough_kv_room = is_enough_kv_cache_room_4_31(past_key_value, seq_len=kv_seq_len)
|
||||
kv_seq_len += past_key_value[0].shape[-2]
|
||||
def _fill_with_neg_inf(t):
|
||||
"""FP16-compatible function that fills a tensor with -inf."""
|
||||
return t.float().fill_(float("-inf")).type_as(t)
|
||||
|
||||
# if past_key_value is not None:
|
||||
# # reuse k, v, self_attention
|
||||
# key_states = torch.cat([past_key_value[0], key_states], dim=2)
|
||||
# value_states = torch.cat([past_key_value[1], value_states], dim=2)
|
||||
if past_key_value is not None:
|
||||
# reuse k, v, self_attention
|
||||
cache_k = past_key_value[0]
|
||||
cache_v = past_key_value[1]
|
||||
if not enough_kv_room:
|
||||
# allocate new
|
||||
new_cache_k, new_cache_v = extend_kv_cache(bsz,
|
||||
self.num_heads,
|
||||
self.head_dim,
|
||||
cache_k.size(2),
|
||||
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
|
||||
dtype=cache_k.dtype,
|
||||
device=device)
|
||||
new_cache_k[:] = cache_k
|
||||
new_cache_v[:] = cache_v
|
||||
cache_k = new_cache_k
|
||||
cache_v = new_cache_v
|
||||
|
||||
key_states, value_states = append_kv_cache(cache_k, cache_v, key_states, value_states)
|
||||
def _buffered_future_mask(tensor, maxpos, alibi, attn_heads):
|
||||
_future_mask = torch.triu(_fill_with_neg_inf(torch.zeros([maxpos, maxpos])), 1)
|
||||
_future_mask = _future_mask.unsqueeze(0) + alibi
|
||||
new_future_mask = _future_mask.to(tensor)
|
||||
return new_future_mask[: tensor.shape[0] * attn_heads, :maxpos, :maxpos]
|
||||
|
||||
elif use_cache:
|
||||
max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
|
||||
new_key_states, new_value_states = init_kv_cache(bsz,
|
||||
self.num_heads,
|
||||
self.head_dim,
|
||||
kv_seq_len,
|
||||
max_cache_length,
|
||||
dtype=key_states.dtype,
|
||||
device=device)
|
||||
new_key_states[:] = key_states
|
||||
new_value_states[:] = value_states
|
||||
key_states = new_key_states
|
||||
value_states = new_value_states
|
||||
|
||||
past_key_value = (key_states, value_states) if use_cache else None
|
||||
def baichuan_13b_gen_alibi_mask(tensor, n_head, max_pos):
|
||||
slopes = torch.Tensor(_get_interleave(n_head)).to(tensor.dtype)
|
||||
position_point = torch.arange(max_pos) - max_pos + 1
|
||||
position_point = position_point.unsqueeze(0).unsqueeze(0).expand(n_head, -1, -1)
|
||||
diag = torch.diag(position_point[0])
|
||||
position_point = position_point - diag.unsqueeze(0).unsqueeze(0).transpose(-1, -2)
|
||||
alibi = slopes.unsqueeze(1).unsqueeze(1) * position_point
|
||||
alibi = alibi.view(n_head, 1, max_pos)
|
||||
alibi_mask = torch.triu(
|
||||
_fill_with_neg_inf(torch.zeros([max_pos, max_pos])), 1).to(tensor.dtype)
|
||||
alibi_mask = alibi_mask.unsqueeze(0) + alibi
|
||||
if tensor.device.type == "xpu":
|
||||
alibi_mask = alibi_mask.to(tensor.device)
|
||||
return alibi_mask
|
||||
|
||||
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
||||
|
||||
if attention_mask is not None:
|
||||
if q_len == 1: # inference with cache
|
||||
if len(attention_mask.size()) == 4:
|
||||
attention_mask = attention_mask[:, :, -1:, :]
|
||||
else:
|
||||
attention_mask = attention_mask[:, -1:, :]
|
||||
attn_weights = attn_weights + attention_mask
|
||||
attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
|
||||
MASK_BLOCK_SIZE = 512
|
||||
|
||||
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
|
||||
|
||||
attn_output = torch.matmul(attn_weights, value_states)
|
||||
|
||||
attn_output = attn_output.transpose(1, 2)
|
||||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
||||
attn_output = self.o_proj(attn_output)
|
||||
|
||||
if not output_attentions:
|
||||
attn_weights = None
|
||||
|
||||
return attn_output.to(hidden_states.dtype), attn_weights, past_key_value
|
||||
def baichuan_13b_get_alibi_mask(self, tensor, seq_length_with_past):
|
||||
if self.training:
|
||||
slopes = torch.Tensor(_get_interleave(self.n_head))
|
||||
position_point = (
|
||||
torch.arange(seq_length_with_past) - seq_length_with_past + 1
|
||||
)
|
||||
position_point = (
|
||||
position_point.unsqueeze(0)
|
||||
.unsqueeze(0)
|
||||
.expand(self.n_head, seq_length_with_past, -1)
|
||||
)
|
||||
diag = torch.diag(position_point[0])
|
||||
position_point = position_point - diag.unsqueeze(0).unsqueeze(0).transpose(
|
||||
-1, -2
|
||||
)
|
||||
alibi = slopes.unsqueeze(1).unsqueeze(1) * position_point
|
||||
mask = _buffered_future_mask(
|
||||
tensor, seq_length_with_past, alibi, self.n_head
|
||||
)
|
||||
else:
|
||||
if self.first_run:
|
||||
# Override the default max_cache_pos=4096 for memory considerations
|
||||
self.max_cache_pos = seq_length_with_past + MASK_BLOCK_SIZE
|
||||
self.first_run = False
|
||||
self.register_buffer(
|
||||
"future_mask",
|
||||
baichuan_13b_gen_alibi_mask(tensor, self.n_head, self.max_cache_pos),
|
||||
persistent=False,
|
||||
)
|
||||
if seq_length_with_past > self.max_cache_pos:
|
||||
# When max_cache_pos is not enough for current sequence length,
|
||||
# increase by MASK_BLOCK_SIZE and recalculate future_mask.
|
||||
self.max_cache_pos = seq_length_with_past + MASK_BLOCK_SIZE
|
||||
self.register_buffer(
|
||||
"future_mask",
|
||||
baichuan_13b_gen_alibi_mask(tensor, self.n_head, self.max_cache_pos),
|
||||
persistent=False,
|
||||
)
|
||||
mask = self.future_mask[
|
||||
: self.n_head, :seq_length_with_past, :seq_length_with_past
|
||||
]
|
||||
return mask
|
||||
|
|
|
|||
|
|
@ -1,325 +0,0 @@
|
|||
#
|
||||
# Copyright 2016 The BigDL Authors.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
# This file is adapted from
|
||||
# https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/cb7fc748b78b7ea99772e4cf76db155729ce774e/modeling_baichuan.py
|
||||
# and
|
||||
# https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/blob/c6f8592a60b4ad73c210b28dd2ab3cca51abbf93/modeling_baichuan.py
|
||||
|
||||
import math
|
||||
from typing import Optional, Tuple
|
||||
import torch
|
||||
import torch.utils.checkpoint
|
||||
from torch.nn import functional as F
|
||||
from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
|
||||
from ipex_llm.transformers.models.utils import update_past_key_value
|
||||
from ipex_llm.transformers.models.utils import should_use_fuse_rope
|
||||
from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp, use_sdp_causal
|
||||
from ipex_llm.transformers.models.utils import apply_rotary_pos_emb, SILU
|
||||
from ipex_llm.transformers.models.utils import mlp_fusion_check
|
||||
import warnings
|
||||
|
||||
|
||||
def pre_compute_inv_freq(module: torch.nn.Module):
|
||||
if module.__class__.__name__ == "RotaryEmbedding":
|
||||
inv_freq = module.inv_freq
|
||||
del module.inv_freq
|
||||
module.register_buffer("inv_freq", inv_freq, persistent=False)
|
||||
|
||||
|
||||
def baichuan_13b_rms_norm_forward(self, hidden_states):
|
||||
if hidden_states.device.type == "xpu" and not (self.training or hidden_states.requires_grad):
|
||||
import xe_addons
|
||||
x_2d = hidden_states.reshape(-1, hidden_states.size(-1)).contiguous()
|
||||
output = xe_addons.rms_norm(self.weight, x_2d, self.epsilon)
|
||||
return output.reshape(hidden_states.shape)
|
||||
|
||||
input_dtype = hidden_states.dtype
|
||||
hidden_states = hidden_states.to(torch.float32)
|
||||
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
||||
hidden_states = hidden_states * torch.rsqrt(variance + self.epsilon)
|
||||
return self.weight * hidden_states.to(input_dtype)
|
||||
|
||||
|
||||
def baichuan_mlp_forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
x_2d = x.view(-1, x.shape[-1])
|
||||
qtype = getattr(self.gate_proj, "qtype", None)
|
||||
if mlp_fusion_check(x_2d, qtype, self.training) and not self.down_proj.enable_xetla:
|
||||
import xe_linear
|
||||
if not x_2d.is_contiguous():
|
||||
x_2d = x_2d.contiguous()
|
||||
return self.down_proj(xe_linear.mlp_forward_xpu(
|
||||
x_2d, self.gate_proj.weight.data, self.up_proj.weight.data,
|
||||
x_2d.shape[0], x_2d.shape[1], self.gate_proj.out_len,
|
||||
SILU, qtype
|
||||
))
|
||||
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
||||
|
||||
|
||||
def baichuan_attention_forward_7b(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||||
output_attentions: bool = False,
|
||||
use_cache: bool = False,
|
||||
):
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
device = hidden_states.device
|
||||
|
||||
qkv = self.W_pack(hidden_states)
|
||||
qkv = qkv.view(bsz, q_len, self.num_heads * 3, self.head_dim)
|
||||
qkv = qkv.transpose(1, 2)
|
||||
query_states, key_states, value_states = qkv.split([self.num_heads,
|
||||
self.num_heads,
|
||||
self.num_heads], dim=1)
|
||||
|
||||
kv_seq_len = key_states.shape[2]
|
||||
if past_key_value is not None:
|
||||
kv_seq_len += past_key_value[0].shape[2]
|
||||
|
||||
# IPEX-LLM OPT: fuse rope
|
||||
if should_use_fuse_rope(hidden_states, position_ids, self.training):
|
||||
import xe_addons
|
||||
xe_addons.rotary_half_inplaced(self.rotary_emb.inv_freq, position_ids,
|
||||
query_states, key_states)
|
||||
else:
|
||||
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
|
||||
cos, sin, position_ids, "baichuan")
|
||||
query_states = query_states.to(hidden_states.dtype)
|
||||
key_states = key_states.to(hidden_states.dtype)
|
||||
|
||||
# IPEX-LLM OPT: kv cache and quantize kv
|
||||
use_quantize_kv = use_quantize_kv_cache(self.W_pack, hidden_states)
|
||||
key_states, value_states = update_past_key_value(
|
||||
past_key_value, key_states, value_states,
|
||||
kv_seq_len, use_quantize_kv, device
|
||||
)
|
||||
past_key_value = (key_states, value_states) if use_cache else None
|
||||
|
||||
if self.training:
|
||||
warnings.warn("xops is not supported on Intel GPU, so just use normal implementation")
|
||||
|
||||
# IPEX-LLM OPT: sdp
|
||||
attn_weights = None
|
||||
if not self.training and not hidden_states.requires_grad and \
|
||||
use_flash_attention(query_states, key_states, attention_mask):
|
||||
attn_output = F.scaled_dot_product_attention(query_states.to(dtype=torch.float16),
|
||||
key_states.to(dtype=torch.float16),
|
||||
value_states.to(dtype=torch.float16),
|
||||
is_causal=True).to(hidden_states.dtype)
|
||||
elif use_sdp(q_len, kv_seq_len, self.head_dim, query_states):
|
||||
import xe_addons
|
||||
if use_quantize_kv:
|
||||
attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states,
|
||||
attention_mask)
|
||||
else:
|
||||
attn_output = xe_addons.sdp(query_states, key_states, value_states,
|
||||
attention_mask)
|
||||
elif use_sdp_causal(q_len, kv_seq_len, self.head_dim, query_states, self.training):
|
||||
import xe_addons
|
||||
if use_quantize_kv:
|
||||
attn_output = xe_addons.sdp_fp8_causal(query_states, key_states,
|
||||
value_states, attention_mask)
|
||||
else:
|
||||
attn_output = xe_addons.sdp_causal(query_states, key_states,
|
||||
value_states, attention_mask)
|
||||
else:
|
||||
if use_quantize_kv:
|
||||
key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
|
||||
query_states.dtype)
|
||||
attn_weights = torch.matmul(query_states,
|
||||
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
||||
if attention_mask is not None:
|
||||
attn_weights = attn_weights + attention_mask
|
||||
# upcast attention to fp32
|
||||
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1,
|
||||
dtype=torch.float32).to(value_states.dtype)
|
||||
attn_output = torch.matmul(attn_weights, value_states)
|
||||
|
||||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
||||
|
||||
attn_output = self.o_proj(attn_output)
|
||||
|
||||
if not output_attentions:
|
||||
attn_weights = None
|
||||
|
||||
return attn_output, attn_weights, past_key_value
|
||||
|
||||
|
||||
def baichuan_attention_forward_13b(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||||
output_attentions: bool = False,
|
||||
use_cache: bool = False,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
device = hidden_states.device
|
||||
|
||||
qkv = self.W_pack(hidden_states)
|
||||
qkv = qkv.view(bsz, q_len, self.num_heads * 3, self.head_dim)
|
||||
qkv = qkv.transpose(1, 2)
|
||||
query_states, key_states, value_states = qkv.split([self.num_heads,
|
||||
self.num_heads,
|
||||
self.num_heads], dim=1)
|
||||
|
||||
kv_seq_len = key_states.shape[2]
|
||||
if past_key_value is not None:
|
||||
kv_seq_len += past_key_value[0].shape[2]
|
||||
|
||||
# IPEX-LLM OPT: kv cache and quantize kv
|
||||
use_quantize_kv = use_quantize_kv_cache(self.W_pack, hidden_states)
|
||||
key_states, value_states = update_past_key_value(
|
||||
past_key_value, key_states, value_states,
|
||||
kv_seq_len, use_quantize_kv, device
|
||||
)
|
||||
past_key_value = (key_states, value_states) if use_cache else None
|
||||
|
||||
if self.training:
|
||||
warnings.warn("xops is not supported on Intel GPU, so just use normal implementation")
|
||||
|
||||
if attention_mask is not None:
|
||||
if len(attention_mask.size()) == 4:
|
||||
attention_mask = attention_mask[:, :, -q_len:, :]
|
||||
else:
|
||||
attention_mask = attention_mask[:, None, -q_len:, :]
|
||||
|
||||
if use_sdp(q_len, kv_seq_len, self.head_dim, query_states):
|
||||
import xe_addons
|
||||
if use_quantize_kv:
|
||||
attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states,
|
||||
attention_mask)
|
||||
else:
|
||||
attn_output = xe_addons.sdp(query_states, key_states, value_states,
|
||||
attention_mask)
|
||||
attn_weights = None
|
||||
else:
|
||||
if use_quantize_kv:
|
||||
key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
|
||||
query_states.dtype)
|
||||
attn_weights = torch.matmul(query_states,
|
||||
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
||||
if attention_mask is not None:
|
||||
attn_weights = attn_weights + attention_mask
|
||||
attn_weights = attn_weights.to(query_states.dtype)
|
||||
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
|
||||
attn_output = torch.matmul(attn_weights.to(dtype=value_states.dtype), value_states)
|
||||
attn_output = attn_output.transpose(1, 2)
|
||||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
||||
attn_output = self.o_proj(attn_output)
|
||||
|
||||
if not output_attentions:
|
||||
attn_weights = None
|
||||
|
||||
return attn_output, attn_weights, past_key_value
|
||||
|
||||
|
||||
def _get_interleave(n):
|
||||
def _get_interleave_power_of_2(n):
|
||||
start = 2 ** (-(2 ** -(math.log2(n) - 3)))
|
||||
ratio = start
|
||||
return [start * ratio**i for i in range(n)]
|
||||
|
||||
if math.log2(n).is_integer():
|
||||
return _get_interleave_power_of_2(n)
|
||||
else:
|
||||
closest_power_of_2 = 2 ** math.floor(math.log2(n))
|
||||
return (
|
||||
_get_interleave_power_of_2(closest_power_of_2)
|
||||
+ _get_interleave(2 * closest_power_of_2)[0::2][: n - closest_power_of_2]
|
||||
)
|
||||
|
||||
|
||||
def _fill_with_neg_inf(t):
|
||||
"""FP16-compatible function that fills a tensor with -inf."""
|
||||
return t.float().fill_(float("-inf")).type_as(t)
|
||||
|
||||
|
||||
def _buffered_future_mask(tensor, maxpos, alibi, attn_heads):
|
||||
_future_mask = torch.triu(_fill_with_neg_inf(torch.zeros([maxpos, maxpos])), 1)
|
||||
_future_mask = _future_mask.unsqueeze(0) + alibi
|
||||
new_future_mask = _future_mask.to(tensor)
|
||||
return new_future_mask[: tensor.shape[0] * attn_heads, :maxpos, :maxpos]
|
||||
|
||||
|
||||
def baichuan_13b_gen_alibi_mask(tensor, n_head, max_pos):
|
||||
slopes = torch.Tensor(_get_interleave(n_head)).to(tensor.dtype)
|
||||
position_point = torch.arange(max_pos) - max_pos + 1
|
||||
position_point = position_point.unsqueeze(0).unsqueeze(0).expand(n_head, -1, -1)
|
||||
diag = torch.diag(position_point[0])
|
||||
position_point = position_point - diag.unsqueeze(0).unsqueeze(0).transpose(-1, -2)
|
||||
alibi = slopes.unsqueeze(1).unsqueeze(1) * position_point
|
||||
alibi = alibi.view(n_head, 1, max_pos)
|
||||
alibi_mask = torch.triu(
|
||||
_fill_with_neg_inf(torch.zeros([max_pos, max_pos])), 1).to(tensor.dtype)
|
||||
alibi_mask = alibi_mask.unsqueeze(0) + alibi
|
||||
if tensor.device.type == "xpu":
|
||||
alibi_mask = alibi_mask.to(tensor.device)
|
||||
return alibi_mask
|
||||
|
||||
|
||||
MASK_BLOCK_SIZE = 512
|
||||
|
||||
|
||||
def baichuan_13b_get_alibi_mask(self, tensor, seq_length_with_past):
|
||||
if self.training:
|
||||
slopes = torch.Tensor(_get_interleave(self.n_head))
|
||||
position_point = (
|
||||
torch.arange(seq_length_with_past) - seq_length_with_past + 1
|
||||
)
|
||||
position_point = (
|
||||
position_point.unsqueeze(0)
|
||||
.unsqueeze(0)
|
||||
.expand(self.n_head, seq_length_with_past, -1)
|
||||
)
|
||||
diag = torch.diag(position_point[0])
|
||||
position_point = position_point - diag.unsqueeze(0).unsqueeze(0).transpose(
|
||||
-1, -2
|
||||
)
|
||||
alibi = slopes.unsqueeze(1).unsqueeze(1) * position_point
|
||||
mask = _buffered_future_mask(
|
||||
tensor, seq_length_with_past, alibi, self.n_head
|
||||
)
|
||||
else:
|
||||
if self.first_run:
|
||||
# Override the default max_cache_pos=4096 for memory considerations
|
||||
self.max_cache_pos = seq_length_with_past + MASK_BLOCK_SIZE
|
||||
self.first_run = False
|
||||
self.register_buffer(
|
||||
"future_mask",
|
||||
baichuan_13b_gen_alibi_mask(tensor, self.n_head, self.max_cache_pos),
|
||||
persistent=False,
|
||||
)
|
||||
if seq_length_with_past > self.max_cache_pos:
|
||||
# When max_cache_pos is not enough for current sequence length,
|
||||
# increase by MASK_BLOCK_SIZE and recalculate future_mask.
|
||||
self.max_cache_pos = seq_length_with_past + MASK_BLOCK_SIZE
|
||||
self.register_buffer(
|
||||
"future_mask",
|
||||
baichuan_13b_gen_alibi_mask(tensor, self.n_head, self.max_cache_pos),
|
||||
persistent=False,
|
||||
)
|
||||
mask = self.future_mask[
|
||||
: self.n_head, :seq_length_with_past, :seq_length_with_past
|
||||
]
|
||||
return mask
|
||||
Loading…
Reference in a new issue