[refactor]update Kunlun classes with monkey patch (#122)
Signed-off-by: Li Wei <liwei.109@outlook.com>
This commit is contained in:
Li Wei
@@ -17,112 +17,119 @@
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# limitations under the License.
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import torch
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from vllm.logger import init_logger
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from typing import Optional
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from vllm.model_executor.layers.quantization.awq import AWQLinearMethod
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logger = init_logger(__name__)
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class KunlunAWQLinearMethod(AWQLinearMethod):
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def repack_int4_for_kunlun(self, packed: torch.Tensor, num_bits: int = 4):
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"""Convert AWQ-packed int4 weights to Kunlun XPU format.
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Input: packed[N, K], dtype=int32, saved as AWQ order
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Output: packed_reordered[N, K], dtype=int32, saved as Kunlun order
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"""
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N, K = packed.shape
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self.align_type = 1 if K % 8 == 0 else 0
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assert num_bits == 4, "Only int4 supported now"
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shifts = torch.arange(0, 32, num_bits, device=packed.device, dtype=torch.int32)
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if self.align_type == 0: # NORMAL MODE
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# Unpack AWQ order:[0, 2, 4, 6, 1, 3, 5, 7]
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unpacked_awq = (packed.unsqueeze(-1) >> shifts) & 0xF # [N, K, 8]
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# Reverse AWQ order and convert to KUNLUN order
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AWQ_TO_KUNLUN_ORDER_NORMAL = [4, 0, 5, 1, 6, 2, 7, 3]
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# [0,2,4,6,1,3,5,7] --> [1, 0, 3, 2, 5, 4, 7, 6]
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unpacked_kunlun = unpacked_awq[..., AWQ_TO_KUNLUN_ORDER_NORMAL] # [N, K, 8]
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# Pack to int32, order[6, 7, 4, 5, 2, 3, 0, 1]
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packed_kunlun = (unpacked_kunlun << shifts).sum(
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dim=-1, dtype=torch.int32
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) # [N, K]
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elif self.align_type == 1: # FAST MODEL
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# Unpack AWQ order
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unpacked_awq = (
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packed.view(N, K // 8, 8).unsqueeze(-1) >> shifts
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) & 0xF # [N, K//8, 8, 8]
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# Reverse AWQ order and convert to KUNLUN order
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AWQ_TO_KUNLUN_ORDER_FAST = [
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32, 0, 36, 4, 33, 1, 37, 5,
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34, 2, 38, 6, 35, 3, 39, 7,
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40, 8, 44, 12, 41, 9, 45, 13,
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42, 10, 46, 14, 43, 11, 47, 15,
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48, 16, 52, 20, 49, 17, 53, 21,
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50, 18, 54, 22, 51, 19, 55, 23,
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56, 24, 60, 28, 57, 25, 61, 29,
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58, 26, 62, 30, 59, 27, 63, 31
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]
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unpacked_awq = unpacked_awq.reshape(N, K // 8, 64)
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unpacked_kunlun = unpacked_awq[..., AWQ_TO_KUNLUN_ORDER_FAST] # [N, K//8, 64]
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# Pack to int32
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unpacked_kunlun = unpacked_kunlun.reshape(N, K // 8, 8, 8)
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packed_kunlun = (
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(unpacked_kunlun << shifts).sum(dim=-1, dtype=torch.int32).reshape(N, K)
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) # [N, K]
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else:
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raise NotImplementedError
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return packed_kunlun
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def repack_int4_for_kunlun(self, packed: torch.Tensor, num_bits: int = 4):
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"""Convert AWQ-packed int4 weights to Kunlun XPU format.
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Input: packed[N, K], dtype=int32, saved as AWQ order
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Output: packed_reordered[N, K], dtype=int32, saved as Kunlun order
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"""
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N, K = packed.shape
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self.align_type = 1 if K % 8 == 0 else 0
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assert num_bits == 4, "Only int4 supported now"
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shifts = torch.arange(0, 32, num_bits, device=packed.device, dtype=torch.int32)
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if self.align_type == 0: # NORMAL MODE
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# Unpack AWQ order:[0, 2, 4, 6, 1, 3, 5, 7]
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unpacked_awq = (packed.unsqueeze(-1) >> shifts) & 0xF # [N, K, 8]
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# Reverse AWQ order and convert to KUNLUN order
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AWQ_TO_KUNLUN_ORDER_NORMAL = [4, 0, 5, 1, 6, 2, 7, 3]
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# [0,2,4,6,1,3,5,7] --> [1, 0, 3, 2, 5, 4, 7, 6]
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unpacked_kunlun = unpacked_awq[..., AWQ_TO_KUNLUN_ORDER_NORMAL] # [N, K, 8]
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# Pack to int32, order[6, 7, 4, 5, 2, 3, 0, 1]
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packed_kunlun = (unpacked_kunlun << shifts).sum(
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dim=-1, dtype=torch.int32
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) # [N, K]
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elif self.align_type == 1: # FAST MODEL
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# Unpack AWQ order
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unpacked_awq = (
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packed.view(N, K // 8, 8).unsqueeze(-1) >> shifts
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) & 0xF # [N, K//8, 8, 8]
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# Reverse AWQ order and convert to KUNLUN order
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AWQ_TO_KUNLUN_ORDER_FAST = [
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32, 0, 36, 4, 33, 1, 37, 5,
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34, 2, 38, 6, 35, 3, 39, 7,
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40, 8, 44, 12, 41, 9, 45, 13,
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42, 10, 46, 14, 43, 11, 47, 15,
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48, 16, 52, 20, 49, 17, 53, 21,
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50, 18, 54, 22, 51, 19, 55, 23,
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56, 24, 60, 28, 57, 25, 61, 29,
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58, 26, 62, 30, 59, 27, 63, 31
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]
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unpacked_awq = unpacked_awq.reshape(N, K // 8, 64)
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unpacked_kunlun = unpacked_awq[..., AWQ_TO_KUNLUN_ORDER_FAST] # [N, K//8, 64]
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# Pack to int32
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unpacked_kunlun = unpacked_kunlun.reshape(N, K // 8, 8, 8)
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packed_kunlun = (
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(unpacked_kunlun << shifts).sum(dim=-1, dtype=torch.int32).reshape(N, K)
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) # [N, K]
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else:
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raise NotImplementedError
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return packed_kunlun
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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layer.qweight = torch.nn.Parameter(
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(
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self.repack_int4_for_kunlun(layer.qweight.data)
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if layer.qweight.data.dtype == torch.int32
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else layer.qweight.data
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),
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requires_grad=False,
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)
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layer.qzeros = torch.nn.Parameter(
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(
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self.repack_int4_for_kunlun(layer.qzeros.data)
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if layer.qzeros.data.dtype == torch.int32
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else layer.qzeros.data
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),
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requires_grad=False,
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)
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layer.scales = torch.nn.Parameter(layer.scales.data, requires_grad=False)
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def apply(
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self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor] = None
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) -> torch.Tensor:
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qweight = layer.qweight
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scales = layer.scales
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qzeros = layer.qzeros
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pack_factor = self.quant_config.pack_factor
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out_shape = x.shape[:-1] + (qweight.shape[-1] * pack_factor,)
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reshaped_x = x.reshape(-1, x.shape[-1])
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# num_tokens >= threshold
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FP16_MATMUL_HEURISTIC_CONDITION = x.shape[:-1].numel() >= 256
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if FP16_MATMUL_HEURISTIC_CONDITION:
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out = torch.ops._C.awq_dequantize(
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qweight, scales, qzeros, quant_type=0, align_type=self.align_type
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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logger.warning_once(f"Repacking INT4 for XPU ...")
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layer.qweight = torch.nn.Parameter(
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(
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self.repack_int4_for_kunlun(layer.qweight.data)
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if layer.qweight.data.dtype == torch.int32
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else layer.qweight.data
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),
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requires_grad=False,
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)
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out = torch.matmul(reshaped_x, out)
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else:
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out = torch.ops._C.awq_gemm(
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reshaped_x, qweight, scales, qzeros, align_type=self.align_type
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layer.qzeros = torch.nn.Parameter(
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(
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self.repack_int4_for_kunlun(layer.qzeros.data)
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if layer.qzeros.data.dtype == torch.int32
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else layer.qzeros.data
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),
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requires_grad=False,
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)
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if bias is not None:
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out.add_(bias)
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return out.reshape(out_shape)
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layer.scales = torch.nn.Parameter(layer.scales.data, requires_grad=False)
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AWQLinearMethod.repack_int4_for_kunlun = repack_int4_for_kunlun
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AWQLinearMethod.process_weights_after_loading = process_weights_after_loading
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AWQLinearMethod.apply = apply
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def apply(
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self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor] = None
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) -> torch.Tensor:
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qweight = layer.qweight
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scales = layer.scales
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qzeros = layer.qzeros
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pack_factor = self.quant_config.pack_factor
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out_shape = x.shape[:-1] + (qweight.shape[-1] * pack_factor,)
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reshaped_x = x.reshape(-1, x.shape[-1])
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# num_tokens >= threshold
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FP16_MATMUL_HEURISTIC_CONDITION = x.shape[:-1].numel() >= 256
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if FP16_MATMUL_HEURISTIC_CONDITION:
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out = torch.ops._C.awq_dequantize(
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qweight, scales, qzeros, quant_type=0, align_type=self.align_type
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)
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out = torch.matmul(reshaped_x, out)
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else:
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out = torch.ops._C.awq_gemm(
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reshaped_x, qweight, scales, qzeros, align_type=self.align_type
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)
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if bias is not None:
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out.add_(bias)
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return out.reshape(out_shape)
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# monkey patch
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from vllm.model_executor.layers.quantization import awq
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awq.AWQLinearMethod = KunlunAWQLinearMethod
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print(
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"[Monkey Patch Applied] >>> vllm.model_executor.layers.quantization.awq.AWQLinearMethod \
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--> vllm_kunlun.ops.quantization.awq.KunlunAWQLinearMethod"
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)
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