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98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
import torch
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from typing import List
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AWQ_PACK_ORDER = [0, 2, 4, 6, 1, 3, 5, 7]
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REVERSE_AWQ_PACK_ORDER = [0, 4, 1, 5, 2, 6, 3, 7]
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def pack(imatrix: torch.Tensor, direction: str = "column"):
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"""
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Packs a 4-bit integer matrix into a packed 32-bit integer matrix.
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Args:
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imatrix (torch.Tensor): matrix of integers
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direction (str): direction of packing, either "column" or "row"
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Returns:
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qmatrix (torch.Tensor): packed matrix of integers
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"""
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shifts = torch.arange(0, 32, 4, dtype=torch.int32, device=imatrix.device)
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imatrix = imatrix.to(torch.int8) & 0x0F # eventually correct overflow
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if direction == "column":
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imatrix = imatrix.view(-1, imatrix.shape[1] // (32 // 4), (32 // 4))
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qmatrix = torch.bitwise_left_shift(imatrix, shifts[None, None, :]).sum(dim=-1)
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elif direction == "row":
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imatrix = imatrix.view(imatrix.shape[0] // (32 // 4), (32 // 4), -1)
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qmatrix = torch.bitwise_left_shift(imatrix, shifts[None, :, None]).sum(dim=1)
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qmatrix = qmatrix.to(torch.int32)
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return qmatrix
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def unpack(qmatrix: torch.Tensor, direction: str = "column"):
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"""
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Unpacks a 32-bit packed integer matrix into a 4-bit integer matrix.
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Args:
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qmatrix (torch.Tensor): matrix of packed integers
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direction (str): direction of unpacking, either "column" or "row"
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Returns:
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imatrix (torch.Tensor): matrix of integers
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"""
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shifts = torch.arange(0, 32, 4, device=qmatrix.device)
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if direction == "column":
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imatrix = torch.bitwise_right_shift(
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qmatrix[:, :, None], shifts[None, None, :]
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).view(qmatrix.shape[0], -1)
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elif direction == "row":
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imatrix = torch.bitwise_right_shift(
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qmatrix[:, None, :], shifts[None, :, None]
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).view(-1, qmatrix.shape[-1])
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imatrix = imatrix.to(torch.int8) & 0x0F # eventually correct overflow
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return imatrix
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def apply_order(
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imatrix: torch.Tensor,
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direction: str = "column",
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order: List[int] = AWQ_PACK_ORDER,
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):
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"""
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Applies the order to a 4-bit integer matrix.
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Args:
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imatrix (torch.Tensor): matrix of integers
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direction (str): direction of applying order, either "column" or "row"
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order (List[int]): order to apply, default is AWQ_PACK_ORDER
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Returns:
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imatrix (torch.Tensor): matrix of integers
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"""
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if direction == "column":
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imatrix = imatrix.view(-1, (32 // 4))[:, order].view(imatrix.shape)
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elif direction == "row":
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imatrix = imatrix.view((32 // 4), -1)[order, :].view(imatrix.shape)
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return imatrix
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def fast_awq_to_gptq(qweight, qzeros):
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# awq uses column packing for both weights and zeros
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izeros = unpack(qzeros, direction="column")
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iweights = unpack(qweight, direction="column")
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# Reverse the order of the iweight and izeros tensors
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izeros = apply_order(izeros, direction="column", order=REVERSE_AWQ_PACK_ORDER)
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iweights = apply_order(iweights, direction="column", order=REVERSE_AWQ_PACK_ORDER)
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# Subtract 1 from the izeros tensor (gptq adds 1 to the zeros)
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izeros = izeros - 1
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# exllama uses row packing for weights and column packing for zeros
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qzeros = pack(izeros, direction="column")
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qweight = pack(iweights, direction="row")
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return qweight, qzeros
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