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fix fuse mlp when using q5_0 or fp8 (#9689)

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Yishuo Wang 2023-12-14 16:16:05 +08:00 committed by GitHub
parent 82ac2dbf55
commit 9a330bfc2b
2 changed files with 4 additions and 0 deletions
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2
python/llm/src/bigdl/llm/transformers/models/baichuan2.py
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@ -26,6 +26,7 @@ from torch import nn
from torch.nn import functional as F from torch.nn import functional as F
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from bigdl.llm.utils.common import invalidInputError from bigdl.llm.utils.common import invalidInputError
from bigdl.llm.ggml.quantize import ggml_tensor_qtype
from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
from bigdl.llm.transformers.models.utils import rotate_half, apply_rotary_pos_emb from bigdl.llm.transformers.models.utils import rotate_half, apply_rotary_pos_emb
from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb_no_cache_xpu from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb_no_cache_xpu
@ -75,6 +76,7 @@ def baichuan_mlp_forward(
x: torch.Tensor, x: torch.Tensor,
) -> torch.Tensor: ) -> torch.Tensor:
if x.shape[1] == 1 and x.dtype == torch.float32 and x.device.type == 'xpu' \ if x.shape[1] == 1 and x.dtype == torch.float32 and x.device.type == 'xpu' \
and self.gate_proj.qtype == ggml_tensor_qtype["sym_int4"] \
and not (self.training and x.requires_grad): and not (self.training and x.requires_grad):
import linear_q4_0 import linear_q4_0
x_2d = x.view(-1, x.shape[-1]) x_2d = x.view(-1, x.shape[-1])

2
python/llm/src/bigdl/llm/transformers/models/qwen.py
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@ -39,6 +39,7 @@ except ImportError:
from bigdl.llm.transformers.models.utils import extend_kv_cache, init_kv_cache, append_kv_cache from bigdl.llm.transformers.models.utils import extend_kv_cache, init_kv_cache, append_kv_cache
from bigdl.llm.transformers.models.utils import rotate_half from bigdl.llm.transformers.models.utils import rotate_half
from bigdl.llm.utils.common import invalidInputError from bigdl.llm.utils.common import invalidInputError
from bigdl.llm.ggml.quantize import ggml_tensor_qtype
apply_rotary_emb_func = None apply_rotary_emb_func = None
@ -214,6 +215,7 @@ def qwen_attention_forward(
def qwen_mlp_forward(self, x: torch.Tensor) -> torch.Tensor: def qwen_mlp_forward(self, x: torch.Tensor) -> torch.Tensor:
if x.shape[1] == 1 and x.dtype == torch.float32 and x.device.type == 'xpu' \ if x.shape[1] == 1 and x.dtype == torch.float32 and x.device.type == 'xpu' \
and self.w2.qtype == ggml_tensor_qtype["sym_int4"] \
and not (self.training and x.requires_grad): and not (self.training and x.requires_grad):
import linear_q4_0 import linear_q4_0
x_2d = x.view(-1, x.shape[-1]) x_2d = x.view(-1, x.shape[-1])