fix fuse mlp when using q5_0 or fp8 (#9689)
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2 changed files with 4 additions and 0 deletions
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@ -26,6 +26,7 @@ from torch import nn
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from torch.nn import functional as F
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from bigdl.llm.utils.common import invalidInputError
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from bigdl.llm.ggml.quantize import ggml_tensor_qtype
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from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
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from bigdl.llm.transformers.models.utils import rotate_half, apply_rotary_pos_emb
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from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb_no_cache_xpu
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@ -75,6 +76,7 @@ def baichuan_mlp_forward(
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x: torch.Tensor,
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) -> torch.Tensor:
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if x.shape[1] == 1 and x.dtype == torch.float32 and x.device.type == 'xpu' \
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and self.gate_proj.qtype == ggml_tensor_qtype["sym_int4"] \
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and not (self.training and x.requires_grad):
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import linear_q4_0
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x_2d = x.view(-1, x.shape[-1])
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@ -39,6 +39,7 @@ except ImportError:
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from bigdl.llm.transformers.models.utils import extend_kv_cache, init_kv_cache, append_kv_cache
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from bigdl.llm.transformers.models.utils import rotate_half
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from bigdl.llm.utils.common import invalidInputError
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from bigdl.llm.ggml.quantize import ggml_tensor_qtype
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apply_rotary_emb_func = None
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@ -214,6 +215,7 @@ def qwen_attention_forward(
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def qwen_mlp_forward(self, x: torch.Tensor) -> torch.Tensor:
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if x.shape[1] == 1 and x.dtype == torch.float32 and x.device.type == 'xpu' \
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and self.w2.qtype == ggml_tensor_qtype["sym_int4"] \
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and not (self.training and x.requires_grad):
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import linear_q4_0
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x_2d = x.view(-1, x.shape[-1])
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