CI job. Gpt awq 4 (#2665)
* add gptq and awq int4 support in intel platform Signed-off-by: Wang, Yi A <yi.a.wang@intel.com> * fix ci failure Signed-off-by: Wang, Yi A <yi.a.wang@intel.com> * set kv cache dtype Signed-off-by: Wang, Yi A <yi.a.wang@intel.com> * refine the code according to the review command Signed-off-by: Wang, Yi A <yi.a.wang@intel.com> * Simplifying conditionals + reverting integration tests values. * Unused import * Fix redundant import. * Revert change after rebase. * Upgrading the tests (TP>1 fix changes to use different kernels.) * Update server/text_generation_server/layers/gptq/__init__.py --------- Signed-off-by: Wang, Yi A <yi.a.wang@intel.com> Co-authored-by: Wang, Yi A <yi.a.wang@intel.com>
This commit is contained in:
Nicolas Patry
GitHub
parent
8ec57558cd
commit
153ff3740b
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@ -69,7 +69,7 @@
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@ -69,7 +69,7 @@
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"tokens": [
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"logprob": 0.0,
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"special": false,
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"text": "\n "
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},
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@ -0,0 +1,8 @@
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from text_generation_server.utils.import_utils import SYSTEM
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if SYSTEM == "ipex":
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from .ipex import WQLinear
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elif SYSTEM == "cuda":
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from .cuda import WQLinear
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__all__ = ["WQLinear"]
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@ -0,0 +1,48 @@
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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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import intel_extension_for_pytorch as ipex
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class WQLinear(nn.Module):
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def __init__(
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self, w_bit, group_size, qweight, qzeros, scales, bias: Optional[torch.Tensor]
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):
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super().__init__()
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if w_bit not in [4]:
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raise NotImplementedError("Only 4-bit are supported for now.")
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self.in_features = qweight.shape[0]
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self.out_features = qweight.shape[1] * 32 // w_bit
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self.w_bit = w_bit
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self.group_size = group_size if group_size != -1 else self.in_features
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# quick sanity check (make sure aligment)
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assert self.in_features % self.group_size == 0
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assert self.out_features % (32 // self.w_bit) == 0
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self.qweight = qweight
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self.qzeros = qzeros
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self.scales = scales
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self.bias = bias
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self.woq_linear = (
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ipex.llm.quantization.IPEXWeightOnlyQuantizedLinear.from_weight(
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self.qweight,
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self.scales,
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self.qzeros,
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self.in_features,
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self.out_features,
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bias=self.bias,
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group_size=self.group_size,
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quant_method=ipex.llm.quantization.QuantMethod.AWQ_GEMM,
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dtype=ipex.llm.quantization.QuantDtype.INT4,
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)
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)
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@torch.no_grad()
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def forward(self, x):
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out_shape = x.shape[:-1] + (self.out_features,)
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out = self.woq_linear(x.reshape(-1, x.shape[-1]))
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out = out + self.bias if self.bias is not None else out
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return out.reshape(out_shape)
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@ -8,6 +8,11 @@ from text_generation_server.utils.import_utils import SYSTEM
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from text_generation_server.utils.log import log_once
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from text_generation_server.utils.weights import Weight, Weights, WeightsLoader
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if SYSTEM == "ipex":
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from .ipex import QuantLinear
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elif SYSTEM == "cuda":
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from .cuda import QuantLinear
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@dataclass
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class GPTQWeight(Weight):
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@ -36,7 +41,7 @@ class GPTQWeight(Weight):
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"to use Exllama/GPTQ kernels for AWQ inference."
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)
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try:
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from text_generation_server.layers.awq.quantize.qmodule import WQLinear
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from text_generation_server.layers.awq.quantize import WQLinear
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return WQLinear(
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w_bit=self.bits,
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@ -60,8 +65,6 @@ class GPTQWeight(Weight):
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return ExllamaQuantLinear(self, bias)
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else:
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from text_generation_server.layers.gptq.quant_linear import QuantLinear
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return QuantLinear(
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self.qweight,
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self.qzeros,
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@ -298,6 +301,7 @@ class GPTQWeightsLoader(WeightsLoader):
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self._get_gptq_params(weights)
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use_exllama = True
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desc_act = self.desc_act
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if self.bits != 4:
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use_exllama = False
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@ -321,7 +325,8 @@ class GPTQWeightsLoader(WeightsLoader):
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if g_idx is not None:
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if (
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not torch.equal(
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g_idx.cpu(),
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# Remove g_idx[0] to adapt the check with TP>1.
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(g_idx - g_idx[0]).cpu(),
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torch.tensor(
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[i // self.groupsize for i in range(g_idx.shape[0])],
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dtype=torch.int32,
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@ -332,6 +337,7 @@ class GPTQWeightsLoader(WeightsLoader):
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# Exllama implementation does not support row tensor parallelism with act-order, as
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# it would require to reorder input activations that are split unto several GPUs
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use_exllama = False
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desc_act = True
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from text_generation_server.layers.gptq import (
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CAN_EXLLAMA,
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@ -350,16 +356,16 @@ class GPTQWeightsLoader(WeightsLoader):
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else:
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log_once(logger.info, f"Using exllama kernels v{HAS_EXLLAMA}")
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if use_exllama and self.groupsize != -1:
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if not desc_act and self.groupsize != -1:
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qzeros = weights.get_sharded(f"{prefix}.qzeros", dim=0)
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scales = weights.get_sharded(f"{prefix}.scales", dim=0)
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if g_idx is not None:
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# qzeros, scales sharded, and g_idx must be adjusted accordingly
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g_idx = g_idx - g_idx[0]
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else:
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qzeros = weights.get_tensor(f"{prefix}.qzeros")
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scales = weights.get_tensor(f"{prefix}.scales")
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if use_exllama and g_idx is not None:
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g_idx = g_idx - g_idx[0]
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if self.quantize == "gptq" and self.quant_method == "awq":
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log_once(
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logger.info, "Converting AWQ model to Exllama/GPTQ packing format."
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@ -0,0 +1,126 @@
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import math
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import numpy as np
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import torch
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import torch.nn as nn
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import intel_extension_for_pytorch as ipex
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class QuantLinear(nn.Module):
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def __init__(self, qweight, qzeros, scales, g_idx, bias, bits, groupsize):
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super().__init__()
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self.register_buffer("qweight", qweight)
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self.register_buffer("qzeros", qzeros)
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self.register_buffer("scales", scales)
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self.register_buffer("g_idx", g_idx)
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if bias is not None:
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self.register_buffer("bias", bias)
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else:
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self.bias = None
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if bits not in [4]:
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raise NotImplementedError("Only 4 bits are supported.")
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self.bits = bits
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self.maxq = 2**self.bits - 1
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self.groupsize = groupsize
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self.outfeatures = qweight.shape[1]
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self.infeatures = qweight.shape[0] * 32 // bits
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self.woq_linear = (
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ipex.llm.quantization.IPEXWeightOnlyQuantizedLinear.from_weight(
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self.qweight,
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self.scales,
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self.qzeros,
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self.infeatures,
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self.outfeatures,
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bias=self.bias,
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group_size=self.groupsize,
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g_idx=g_idx,
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quant_method=ipex.llm.quantization.QuantMethod.GPTQ_GEMM,
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dtype=ipex.llm.quantization.QuantDtype.INT4,
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)
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)
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@classmethod
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def new(cls, bits, groupsize, infeatures, outfeatures, bias):
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if bits not in [4]:
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raise NotImplementedError("Only 4 bits are supported.")
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qweight = torch.zeros((infeatures // 32 * bits, outfeatures), dtype=torch.int32)
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qzeros = torch.zeros(
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(math.ceil(infeatures / groupsize), outfeatures // 32 * bits),
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dtype=torch.int32,
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)
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scales = torch.zeros(
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(math.ceil(infeatures / groupsize), outfeatures), dtype=torch.float16
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)
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g_idx = torch.tensor(
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[i // groupsize for i in range(infeatures)], dtype=torch.int32
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)
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if bias:
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bias = torch.zeros((outfeatures), dtype=torch.float16)
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else:
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bias = None
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return cls(qweight, qzeros, scales, g_idx, bias, bits, groupsize)
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def pack(self, linear, scales, zeros, g_idx=None):
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self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx
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scales = scales.t().contiguous()
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zeros = zeros.t().contiguous()
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scale_zeros = zeros * scales
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self.scales = scales.clone().half()
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if linear.bias is not None:
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self.bias = linear.bias.clone().half()
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intweight = []
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for idx in range(self.infeatures):
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intweight.append(
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torch.round(
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(linear.weight.data[:, idx] + scale_zeros[self.g_idx[idx]])
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/ self.scales[self.g_idx[idx]]
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).to(torch.int)[:, None]
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)
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intweight = torch.cat(intweight, dim=1)
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intweight = intweight.t().contiguous()
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intweight = intweight.numpy().astype(np.uint32)
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qweight = np.zeros(
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(intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32
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)
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i = 0
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row = 0
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while row < qweight.shape[0]:
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if self.bits in [4]:
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for j in range(i, i + (32 // self.bits)):
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qweight[row] |= intweight[j] << (self.bits * (j - i))
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i += 32 // self.bits
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row += 1
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else:
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raise NotImplementedError("Only 4 bits are supported.")
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qweight = qweight.astype(np.int32)
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self.qweight = torch.from_numpy(qweight)
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zeros -= 1
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zeros = zeros.numpy().astype(np.uint32)
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qzeros = np.zeros(
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(zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32
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)
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i = 0
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col = 0
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while col < qzeros.shape[1]:
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if self.bits in [4]:
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for j in range(i, i + (32 // self.bits)):
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qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i))
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i += 32 // self.bits
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col += 1
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else:
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raise NotImplementedError("Only 4 bits are supported.")
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qzeros = qzeros.astype(np.int32)
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self.qzeros = torch.from_numpy(qzeros)
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def forward(self, x):
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out_shape = x.shape[:-1] + (self.outfeatures,)
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out = self.woq_linear(x.reshape(-1, x.shape[-1]))
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out = out + self.bias if self.bias is not None else out
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return out.reshape(out_shape)
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@ -12,7 +12,7 @@ from huggingface_hub import HfApi
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from accelerate import init_empty_weights
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from text_generation_server.utils import initialize_torch_distributed, Weights
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from text_generation_server.utils.hub import weight_files
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from text_generation_server.layers.gptq.quant_linear import QuantLinear
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from text_generation_server.layers.gptq import QuantLinear
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from loguru import logger
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from typing import Optional
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from text_generation_server.layers.gptq.utils import torch_snr_error
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@ -400,6 +400,9 @@ def get_model(
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if dtype is None:
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if quantize in ["awq", "exl2", "gptq", "marlin"]:
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if SYSTEM == "ipex" and not hasattr(torch, "xpu"):
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dtype = torch.bfloat16
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else:
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# These quantizers only work with float16 params.
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dtype = torch.float16
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elif quantize == "fp8":
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|
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@ -1122,7 +1122,6 @@ class FlashCausalLM(Model):
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dtype = default_dtype if dtype is None else dtype
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else:
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device = torch.device("cpu")
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# Float16 doesn't exist on target.
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dtype = torch.bfloat16 if dtype is None else dtype
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init_cpu_threads_env(rank_id=rank, world_size=world_size)
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else:
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@ -1602,8 +1601,6 @@ class FlashCausalLM(Model):
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max_s = batch.max_current_length
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lm_head_indices = batch.prefill_head_indices
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print(slots)
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if cu_seqlen_prefill is None and self.max_past() is not None:
|
||||
# In decode, not prefill, we're actually overwriting the KV-cache
|
||||
# in a circular buffer mode.
|
||||
|
|
|
|||
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Reference in New Issue