125 lines
3.7 KiB
Python
125 lines
3.7 KiB
Python
import torch
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from text_generation_server.utils.import_utils import SYSTEM
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from torch.nn import functional as F
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import os
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if SYSTEM == "rocm":
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ROCM_USE_SKINNY_GEMM = os.getenv("ROCM_USE_SKINNY_GEMM", "True").lower() in (
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"true",
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"1",
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)
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if ROCM_USE_SKINNY_GEMM:
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try:
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from vllm import _custom_C
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except Exception as e:
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raise ImportError(f"Could not load `vllm._custom_C`. Full error: {e}")
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class FastLinear(torch.nn.Module):
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def __init__(
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self,
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weight,
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bias,
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) -> None:
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super().__init__()
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self.weight = torch.nn.Parameter(weight, requires_grad=False)
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if bias is not None:
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self.bias = torch.nn.Parameter(bias, requires_grad=False)
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else:
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self.bias = None
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@classmethod
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def load(cls, config, prefix: str, weights, bias: bool):
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weight = weights.get_tensor(f"{prefix}.weight")
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if bias:
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bias = weights.get_tensor(f"{prefix}.bias")
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else:
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bias = None
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return cls(weight, bias)
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def forward(self, input: torch.Tensor) -> torch.Tensor:
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return F.linear(input, self.weight, self.bias)
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class FastLinearROCm(torch.nn.Module):
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def __init__(
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self,
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weight,
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bias,
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) -> None:
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super().__init__()
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self.weight = torch.nn.Parameter(weight)
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if bias is not None:
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self.bias = torch.nn.Parameter(bias)
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else:
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self.bias = None
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self.cu_count = torch.cuda.get_device_properties(
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device="cuda"
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).multi_processor_count
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self.use_skinny_gemm = (
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ROCM_USE_SKINNY_GEMM
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and "gfx1" not in torch.cuda.get_device_properties("cuda").gcnArchName
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)
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@classmethod
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def load(cls, config, prefix: str, weights, bias: bool):
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weight = weights.get_tensor(f"{prefix}.weight")
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if bias:
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bias = weights.get_tensor(f"{prefix}.bias")
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else:
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bias = None
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return cls(weight, bias)
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def forward(self, inp: torch.Tensor) -> torch.Tensor:
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weight = self.weight
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bias = self.bias
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if (
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self.use_skinny_gemm
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and inp.dtype == torch.float16
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and inp.shape[-1] % 8 == 0
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):
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batched = False
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inp_shape = inp.shape
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if inp.dim() == 3:
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inp = inp.view(-1, inp_shape[-1])
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batched = True
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m, n, k = weight.shape[0], inp_shape[0], inp_shape[1]
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if m > 8 and n <= 4:
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out = torch.empty(
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inp_shape[0], weight.shape[0], dtype=inp.dtype, device=weight.device
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)
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_custom_C.wvSpltK(weight, inp, out, n, self.cu_count)
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elif m % 4 == 0 and n == 1 and k <= 8192:
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out = torch.empty(
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inp_shape[0], weight.shape[0], dtype=inp.dtype, device=weight.device
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)
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_custom_C.LLMM1(weight, inp, out, 4)
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else:
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out = F.linear(inp, weight)
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if batched:
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out.view(*inp_shape[:-1], out.shape[-1])
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if bias is not None:
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out = out + bias
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return out
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return F.linear(inp, self.weight, self.bias)
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def get_linear(weight, bias):
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# Weights that are loaded through methods that are not
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# quantization-aware are still bare tensors. We may want
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# to change this in the future.
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if isinstance(weight, torch.Tensor):
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if SYSTEM == "rocm":
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return FastLinearROCm(weight, bias)
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else:
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return FastLinear(weight, bias)
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return weight.get_linear(bias)
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