249 lines
7.4 KiB
Python
249 lines
7.4 KiB
Python
# Adapted from turboderp exllama: https://github.com/turboderp/exllamav2
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from dataclasses import dataclass
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from typing import Optional
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import torch
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import torch.nn as nn
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from loguru import logger
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from text_generation_server.layers.exl2 import Exl2Weight
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from text_generation_server.layers.gptq import GPTQWeight
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try:
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from exllamav2_kernels import make_q_matrix, gemm_half_q_half
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except ImportError:
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logger.error("exllamav2_kernels not installed.")
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raise
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# Dummy tensor to pass instead of g_idx since there is no way to pass "None" to a C++ extension
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none_tensor = torch.empty((1, 1), device="meta")
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@dataclass
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class _ExtraTensors:
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"""Additional generated quantizer tensors."""
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q_group_map: Optional[torch.Tensor] = None
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q_invperm: Optional[torch.Tensor] = None
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q_perm: Optional[torch.Tensor] = None
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def ext_gemm_half_q_half(x, q_handle, q4_width, force_cuda):
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"""Matrix multiplication, returns x @ q4"""
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output_shape = x.shape[:-1] + (q4_width,)
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x = x.view(-1, x.shape[-1])
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output = torch.empty((x.shape[0], q4_width), dtype=torch.half, device=x.device)
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gemm_half_q_half(x, q_handle, output, force_cuda)
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return output.view(output_shape)
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def make_group_map(q_groups: torch.Tensor, num_qrows: int):
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gr = q_groups.tolist()
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group_map = []
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num_groups = len(gr) // 2
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for i in range(num_groups):
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bits = gr[i * 2]
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if i < num_groups - 1:
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qrows = gr[i * 2 + 3] - gr[i * 2 + 1]
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else:
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qrows = num_qrows - gr[i * 2 + 1]
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rows = qrows * 32 // bits
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for j in range(rows):
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group_map += [i]
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group_map += [rows - j]
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return torch.tensor(group_map, dtype=torch.short, device=q_groups.device)
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# Create Q matrix
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def ext_make_q_matrix(
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w: Exl2Weight | GPTQWeight,
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extra: _ExtraTensors,
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temp_dq,
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key: Optional[str] = None,
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):
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"""
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Create Q matrix
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"""
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# EXL2
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if isinstance(w, Exl2Weight):
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extra.q_group_map = make_group_map(w.q_groups, w.q_weight.shape[0])
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extra.q_perm = torch.argsort(w.q_invperm).short()
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return make_q_matrix(
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w.q_weight,
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extra.q_perm,
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w.q_invperm,
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w.q_scale,
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w.q_scale_max,
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w.q_groups,
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extra.q_group_map,
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none_tensor,
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none_tensor,
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none_tensor,
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temp_dq,
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)
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# GPTQ
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elif isinstance(w, GPTQWeight):
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if w.scales.dtype == torch.float:
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w.scales = w.scales.half()
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# GPTQ with g_idx (act_order)
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if w.g_idx is not None and not (w.g_idx == 0).all().item():
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extra.q_perm = torch.empty(
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(w.qweight.shape[0] * 8,),
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dtype=torch.short,
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device=w.qweight.device,
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)
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extra.q_invperm = torch.empty_like(extra.q_perm)
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# make_q4 segfaults if g_idx is not on cpu in the act-order case. In the non act-order case, None needs to be passed for g_idx.
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return make_q_matrix(
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w.qweight,
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extra.q_perm,
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extra.q_invperm,
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none_tensor,
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none_tensor,
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none_tensor,
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none_tensor,
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w.qzeros,
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w.scales,
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w.g_idx.cpu(),
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temp_dq,
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)
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# GPTQ without g_idx
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else:
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return make_q_matrix(
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w.qweight,
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none_tensor,
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none_tensor,
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none_tensor,
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none_tensor,
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none_tensor,
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none_tensor,
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w.qzeros,
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w.scales,
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none_tensor,
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temp_dq,
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)
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else:
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RuntimeError("Cannot create handle")
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DEVICE = None
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LAYERS = []
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def set_device(device):
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global DEVICE
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DEVICE = device
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def create_exllama_buffers(max_total_tokens: int):
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global LAYERS, DEVICE
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# Find the size of the scratch space.
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scratch_bytes = max(
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layer.scratch_space_fixed(max_input_len=max_total_tokens, max_batch_size=1)
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for layer in LAYERS
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)
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temp_dq = ExLlamaV2DeviceTensors(DEVICE, scratch_bytes)
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for layer in LAYERS:
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layer.post_init(temp_dq)
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class QuantLinear(nn.Module):
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QUANT_TYPE = "exllamav2"
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"""Linear layer implementation with per-group 4-bit quantization of the weights"""
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def __init__(
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self,
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weight: Exl2Weight | GPTQWeight,
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bias: torch.Tensor,
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):
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super().__init__()
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self.q_handle = None
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self.q_tensors = weight
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self.extra_tensors = _ExtraTensors()
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if isinstance(weight, Exl2Weight):
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self.infeatures = weight.q_invperm.shape[0]
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self.outfeatures = weight.q_weight.shape[1]
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elif isinstance(weight, GPTQWeight):
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if weight.bits != 4:
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raise ValueError(
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f"Exllamav2 kernel supports only bits=4, requested bits={weight.bits}. Something is wrong in the model initialization."
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)
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self.infeatures = weight.qweight.shape[0] // weight.bits * 32
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self.outfeatures = weight.qweight.shape[1]
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self.padding = -self.outfeatures % 32
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self.outfeatures = self.outfeatures + self.padding
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self.device = weight.device
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self.bias = bias if bias is not None else None
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global LAYERS
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LAYERS.append(self)
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def post_init(self, temp_dq):
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device = self.q_tensors.device
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assert device.type == "cuda"
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assert device.index is not None
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temp_dq = temp_dq.get_scratch_slice(self.temp_dq_size())
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# We NEED to keep a pointer on Python side, otherwise the garbage collector will mess with us,
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# and `Memory access fault by GPU node-2` will EAT you.
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self.temp_dq = temp_dq
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self.q_handle = ext_make_q_matrix(self.q_tensors, self.extra_tensors, temp_dq)
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def forward(self, x, force_cuda=False):
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output = ext_gemm_half_q_half(x, self.q_handle, self.outfeatures, force_cuda)
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if self.bias is not None:
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output.add_(self.bias)
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return output
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def temp_dq_size(self):
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return self.infeatures * self.outfeatures * 2 + 128
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def temp_fwd_size(self, max_input_len, max_batch_size):
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return self.outfeatures * max_input_len * max_batch_size * 4 + 128
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def scratch_space_fixed(self, max_input_len, max_batch_size):
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return self.temp_dq_size() + self.temp_fwd_size(max_input_len, max_batch_size)
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class ExLlamaV2DeviceTensors:
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device_idx: int
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scratch_bytes: int
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scratch_idx: int
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scratch: torch.tensor = None
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def __init__(self, device, scratch_bytes):
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self.device = device
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self.scratch_bytes = scratch_bytes
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def prepare(self):
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self.scratch = torch.empty(
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(self.scratch_bytes // 2,), dtype=torch.half, device=self.device
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)
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def get_scratch_slice(self, size_bytes):
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if self.scratch is None:
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self.prepare()
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size_bytes = ((size_bytes + 127) // 128) * 128
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size_half = size_bytes // 2
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scratch_slice = self.scratch.narrow(0, 0, size_half)
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return scratch_slice
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