[refactor]update Kunlun classes with monkey patch (#122)
Signed-off-by: Li Wei <liwei.109@outlook.com>
This commit is contained in:
Li Wei
@@ -17,92 +17,99 @@
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# limitations under the License.
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import torch
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from torch.nn.parameter import Parameter
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from typing import Optional
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from torch.nn.parameter import Parameter
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from vllm.logger import init_logger
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from vllm.model_executor.layers.quantization.gptq import GPTQLinearMethod, ExllamaState
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logger = init_logger(__name__)
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class KunlunGPTQLinearMethod(GPTQLinearMethod):
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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# for torch.compile
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logger.warning_once(f"Repacking INT4 for XPU ...")
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layer.qzeros = Parameter(
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self.repack_int4_for_kunlun(layer.qzeros.data, self.quant_config.weight_bits)
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if self.quant_config.weight_bits == 4 else layer.qzeros.data,
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requires_grad=False
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)
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layer.qweight = Parameter(layer.qweight.data, requires_grad=False)
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layer.g_idx = Parameter(layer.g_idx.data, requires_grad=False)
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layer.scales = Parameter(layer.scales.data, requires_grad=False)
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# exllama needs to shuffle the weight after the weight is loaded
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# here we do the shuffle on first forward pass
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if layer.exllama_state == ExllamaState.UNINITIALIZED:
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if self.quant_config.desc_act:
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layer.g_idx.data = torch.argsort(layer.g_idx).to(torch.int)
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else:
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layer.g_idx.data = torch.empty((0, ),
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dtype=torch.int,
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device=layer.g_idx.device)
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layer.exllama_state = ExllamaState.READY
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# No need shuffle on xpu
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# ops.gptq_shuffle(layer.qweight, layer.g_idx,
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# self.quant_config.weight_bits)
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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# for torch.compile
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layer.qzeros = Parameter(
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self.repack_int4_for_kunlun(layer.qzeros.data, self.quant_config.weight_bits)
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if self.quant_config.weight_bits == 4 else layer.qzeros.data,
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requires_grad=False
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)
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layer.qweight = Parameter(layer.qweight.data, requires_grad=False)
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layer.g_idx = Parameter(layer.g_idx.data, requires_grad=False)
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layer.scales = Parameter(layer.scales.data, requires_grad=False)
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def repack_int4_for_kunlun(self, packed: torch.Tensor, num_bits: int = 4):
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N, K = packed.shape
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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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# exllama needs to shuffle the weight after the weight is loaded
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# here we do the shuffle on first forward pass
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if layer.exllama_state == ExllamaState.UNINITIALIZED:
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if self.quant_config.desc_act:
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layer.g_idx.data = torch.argsort(layer.g_idx).to(torch.int)
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else:
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layer.g_idx.data = torch.empty((0, ),
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dtype=torch.int,
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device=layer.g_idx.device)
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layer.exllama_state = ExllamaState.READY
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# Unpack int32 to int4 values
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unpacked_gptq = (
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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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# No need shuffle on xpu
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# ops.gptq_shuffle(layer.qweight, layer.g_idx,
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# self.quant_config.weight_bits)
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# Convert to KUNLUN order
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GPTQ_TO_KUNLUN_ORDER_FAST = [
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32, 0, 33, 1, 34, 2, 35, 3,
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36, 4, 37, 5, 38, 6, 39, 7,
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40, 8, 41, 9, 42, 10, 43, 11,
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44, 12, 45, 13, 46, 14, 47, 15,
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48, 16, 49, 17, 50, 18, 51, 19,
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52, 20, 53, 21, 54, 22, 55, 23,
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56, 24, 57, 25, 58, 26, 59, 27,
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60, 28, 61, 29, 62, 30, 63, 31,
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]
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unpacked_gptq = unpacked_gptq.reshape(N, K // 8, 64)
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unpacked_kunlun = unpacked_gptq[..., GPTQ_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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return packed_kunlun
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def repack_int4_for_kunlun(self, packed: torch.Tensor, num_bits: int = 4):
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N, K = packed.shape
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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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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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out_shape = x.shape[:-1] + (layer.qweight.shape[-1], )
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reshaped_x = x.reshape(-1, x.shape[-1])
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# Unpack int32 to int4 values
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unpacked_gptq = (
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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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output = torch.ops.xspeedgate_ops.gptq_gemm(
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reshaped_x,
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layer.qweight,
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layer.qzeros,
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layer.scales,
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layer.g_idx,
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layer.exllama_state == ExllamaState.READY,
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self.quant_config.weight_bits,
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)
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if bias is not None:
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output.add_(bias)
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return output.reshape(out_shape)
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# Convert to KUNLUN order
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GPTQ_TO_KUNLUN_ORDER_FAST = [
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32, 0, 33, 1, 34, 2, 35, 3,
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36, 4, 37, 5, 38, 6, 39, 7,
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40, 8, 41, 9, 42, 10, 43, 11,
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44, 12, 45, 13, 46, 14, 47, 15,
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48, 16, 49, 17, 50, 18, 51, 19,
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52, 20, 53, 21, 54, 22, 55, 23,
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56, 24, 57, 25, 58, 26, 59, 27,
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60, 28, 61, 29, 62, 30, 63, 31,
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]
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unpacked_gptq = unpacked_gptq.reshape(N, K // 8, 64)
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unpacked_kunlun = unpacked_gptq[..., GPTQ_TO_KUNLUN_ORDER_FAST] # [N, K//8, 64]
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# monkey patch
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from vllm.model_executor.layers.quantization import gptq
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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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return packed_kunlun
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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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out_shape = x.shape[:-1] + (layer.qweight.shape[-1], )
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reshaped_x = x.reshape(-1, x.shape[-1])
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output = torch.ops.xspeedgate_ops.gptq_gemm(
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reshaped_x,
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layer.qweight,
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layer.qzeros,
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layer.scales,
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layer.g_idx,
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layer.exllama_state == ExllamaState.READY,
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self.quant_config.weight_bits,
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)
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if bias is not None:
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output.add_(bias)
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return output.reshape(out_shape)
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GPTQLinearMethod.repack_int4_for_kunlun = repack_int4_for_kunlun
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GPTQLinearMethod.process_weights_after_loading = process_weights_after_loading
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GPTQLinearMethod.apply = apply
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gptq.GPTQLinearMethod = KunlunGPTQLinearMethod
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print(
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"[Monkey Patch Applied] >>> vllm.model_executor.layers.quantization.gptq.GPTQLinearMethod \
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--> vllm_kunlun.ops.quantization.gptq.KunlunGPTQLinearMethod"
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)
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