406 lines
14 KiB
Python
406 lines
14 KiB
Python
import operator
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import warnings
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import torch
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import bitsandbytes.functional as F
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from dataclasses import dataclass
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from functools import reduce # Required in Python 3
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# math.prod not compatible with python < 3.8
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def prod(iterable):
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return reduce(operator.mul, iterable, 1)
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tensor = torch.Tensor
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"""
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This class pools outlier dimensions across layers.
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This is particularly important for small models where outlier features
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are less systematic and occur with low frequency.
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"""
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class GlobalOutlierPooler(object):
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_instance = None
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def __init__(self):
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raise RuntimeError("Call get_instance() instead")
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def initialize(self):
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self.outliers = set()
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self.model_dim = None
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@classmethod
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def get_instance(cls):
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if cls._instance is None:
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cls._instance = cls.__new__(cls)
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cls._instance.initialize()
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return cls._instance
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def add_outliers(self, outlier_idx, feature_dim):
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if self.model_dim is None:
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self.model_dim = feature_dim
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if feature_dim != self.model_dim:
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return # we do not encode outliers for the 2nd FFN layer
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self.outliers.update(outlier_idx.tolist())
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def get_current_outlier_idx(self):
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return torch.Tensor(list(self.outliers)).to(torch.int64)
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class MatMul8bit(torch.autograd.Function):
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@staticmethod
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def forward(ctx, A, B, out=None, quant_type="vector", precision=[8, 8, 8]):
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if precision[0] != 8:
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with torch.no_grad():
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output = torch.matmul(A, B)
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else:
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if len(B.shape) == 2:
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dim = 0
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else:
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dim = 1
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qA, SA = F.vectorwise_quant(A, dim=-1, quant_type=quant_type)
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qB, SB = F.vectorwise_quant(B, dim=dim, quant_type=quant_type)
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iout = F.igemm(qA, qB)
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output = F.vectorwise_mm_dequant(iout, SA, SB, A.dtype, quant_type)
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if A.requires_grad or B.requires_grad:
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ctx.save_for_backward(A, B)
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ctx.quant_type = quant_type
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ctx.precision = precision
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return output
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@staticmethod
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def backward(ctx, grad_output):
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A, B = ctx.saved_tensors
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quant_type = ctx.quant_type
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precision = ctx.precision
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grad_A = grad_B = None
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if B.requires_grad:
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if len(A.shape) == 3:
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dims = [0, 1]
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# bsi -> ibs
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permute_dim = [0, 2, 1]
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else:
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dims = [0]
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# bs -> sb
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permute_dim = [1, 0]
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if precision[1] != 8:
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with torch.no_grad():
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grad_B = torch.matmul(A.permute(permute_dim), grad_output)
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else:
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if len(B.shape) == 2 and len(A.shape) == 3:
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grad_output = grad_output.contiguous()
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if not grad_output.is_contiguous():
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grad_output.contiguous()
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qgrad_output, S1 = F.vectorwise_quant(
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grad_output.view(-1, grad_output.shape[2]),
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dim=0,
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quant_type=quant_type,
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)
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if not A.is_contiguous():
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A = A.contiguous()
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qA, S2 = F.vectorwise_quant(
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A.view(-1, A.shape[2]), dim=0, quant_type=quant_type
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)
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igrad_B = F.igemm(qA.t(), qgrad_output)
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grad_B = F.vectorwise_mm_dequant(
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igrad_B, S2.t(), S1, grad_output.dtype, quant_type
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)
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else:
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qgrad_output, S1 = F.vectorwise_quant(
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grad_output, dim=dims, quant_type=quant_type
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)
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qA, S2 = F.vectorwise_quant(
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A, dim=dims, quant_type=quant_type
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)
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igrad_B = F.igemm(qA.permute(permute_dim), qgrad_output)
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grad_B = F.vectorwise_mm_dequant(
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igrad_B,
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S2.permute(permute_dim),
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S1,
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grad_output.dtype,
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quant_type,
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)
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if A.requires_grad:
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if len(grad_output.shape) == 3:
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dims = [2]
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else:
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dims = [1]
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if len(B.shape) == 3:
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# bio -> boi
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permute_dim = [0, 2, 1]
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dim_B = dims
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else:
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# io -> oi
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permute_dim = [1, 0]
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dim_B = [1]
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if precision[2] != 8:
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with torch.no_grad():
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grad_A = torch.matmul(grad_output, B.permute(permute_dim))
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else:
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qgrad_output, S1 = F.vectorwise_quant(
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grad_output, dim=dims, quant_type=quant_type
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)
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qB, S3 = F.vectorwise_quant(B, dim=dim_B, quant_type=quant_type)
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igrad_A = F.igemm(qgrad_output, qB.permute(permute_dim))
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grad_A = F.vectorwise_mm_dequant(
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igrad_A,
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S1,
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S3.permute(permute_dim),
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grad_output.dtype,
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quant_type,
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)
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return grad_A, grad_B, None, None, None
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mm_cublas = MatMul8bit.apply
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bmm_cublas = MatMul8bit.apply
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matmul_cublas = MatMul8bit.apply
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@dataclass
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class MatmulLtState:
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CB = None
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CxB = None
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SB = None
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SCB = None
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CxBt = None
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SBt = None
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CBt = None
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subB = None
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outlier_pool = None
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has_accumulated_gradients = False
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threshold = 0.0
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idx = None
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is_training = True
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has_fp16_weights = True
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memory_efficient_backward = False
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use_pool = False
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formatB = F.get_special_format_str()
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def reset_grads(self):
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self.CB = None
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self.CxB = None
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self.SB = None
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self.SCB = None
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self.CxBt = None
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self.SBt = None
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self.CBt = None
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class MatMul8bitLt(torch.autograd.Function):
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@staticmethod
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def forward(ctx, A, B, out=None, bias=None, state=MatmulLtState()):
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# default to pytorch behavior if inputs are empty
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ctx.is_empty = False
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if prod(A.shape) == 0:
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ctx.is_empty = True
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ctx.A = A
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ctx.B = B
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ctx.bias = bias
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if A.shape[-1] == B.shape[0]:
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return torch.empty(A.shape[:-1]+B.shape[1:], dtype=A.dtype, device=A.device)
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else:
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return torch.empty(A.shape[:-1]+B.shape[:1], dtype=A.dtype, device=A.device)
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# 1. Quantize A
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# 2. Quantize B
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# 3. Matmul
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# 4. Mixed-precision decomposition matmul
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# 5. Save state
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formatB = state.formatB
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input_shape = A.shape
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if state.outlier_pool is None:
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state.outlier_pool = GlobalOutlierPooler.get_instance()
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# Cast A to fp16
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if A.dtype != torch.float16:
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warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization")
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# 1. Quantize A
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if len(A.shape) == 3:
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A = A.view(-1, A.shape[-1]).contiguous()
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CA, CAt, SCA, SCAt, coo_tensorA = F.double_quant(
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A.to(torch.float16), threshold=state.threshold
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)
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if state.threshold > 0.0 and coo_tensorA is not None:
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if state.has_fp16_weights:
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idx = torch.unique(coo_tensorA.colidx).long()
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CA[:, idx] = 0
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CAt[:, idx] = 0
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subA = A[:, idx]
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state.subB = B[:, idx].t().contiguous()
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state.idx = idx
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else:
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if state.CxB is None:
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# B in in 8-bit row-major, we can transform it back to 16-bit to extract outlier dimensions
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# we also need to convert it to the turing/ampere format
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state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
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else:
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if not state.has_fp16_weights and state.CxB is None:
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state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
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subA = None
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# 2. Quantize B
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if state.has_fp16_weights:
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has_grad = True if (getattr(B, "grad", None) is not None) else False
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is_transposed = not B.is_contiguous() and B.shape[0] == B.stride(1)
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if is_transposed:
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B = B.contiguous()
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if (state.is_training and not has_grad) or state.CxB is None:
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state.reset_grads()
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(
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CB,
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state.CBt,
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state.SCB,
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state.SCBt,
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coo_tensorB,
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) = F.double_quant(B.to(torch.float16))
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state.CxB, state.SB = F.transform(CB, to_order=formatB)
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else:
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has_grad = False
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if coo_tensorA is not None and not state.has_fp16_weights:
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# extract outliers
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outlier_idx = torch.unique(coo_tensorA.colidx)
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state.idx = outlier_idx
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# state.outlier_pool.add_outliers(outlier_idx, A.shape[-1])
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# if state.use_pool and state.outlier_pool.model_dim == A.shape[-1]:
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# # do not use pool for 2nd FFN layer
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# state.idx = state.outlier_pool.get_current_outlier_idx().to(A.device)
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# else:
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# state.idx = outlier_idx
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outliers = F.extract_outliers(state.CxB, state.SB, state.idx.int())
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state.subB = (
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(outliers * state.SCB.view(-1, 1) / 127.0)
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.t()
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.contiguous()
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.to(A.dtype)
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)
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CA[:, state.idx.long()] = 0
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CAt[:, state.idx.long()] = 0
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subA = A[:, state.idx.long()]
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shapeB = state.SB[0]
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if len(input_shape) == 3:
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output_shape = (input_shape[0], input_shape[1], shapeB[0])
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else:
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output_shape = (input_shape[0], shapeB[0])
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# 3. Matmul
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C32A, SA = F.transform(CA, "col32")
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out32, Sout32 = F.igemmlt(C32A, state.CxB, SA, state.SB)
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# we apply the fused bias here
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if bias is None or bias.dtype == torch.float16:
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output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=bias)
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output = output.to(A.dtype)
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else: # apply bias separately
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output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=None)
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output = output.to(A.dtype).add_(bias)
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# 4. Mixed-precision decomposition matmul
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if coo_tensorA is not None and subA is not None:
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output += torch.matmul(subA, state.subB)
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# 5. Save state
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ctx.state = state
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ctx.formatB = formatB
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ctx.grad_shape = input_shape
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ctx.dtype_A, ctx.dtype_B, ctx.dtype_bias = A.dtype, B.dtype, None if bias is None else bias.dtype
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if any(ctx.needs_input_grad[:2]):
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ctx.tensors = (CAt, subA)
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ctx.tensor_states = (SCAt, state.idx)
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else:
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ctx.tensors = [None, None]
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ctx.tensor_states = (None, None)
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ctx.save_for_backward(None, None)
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clone_func = torch.clone if len(output_shape) == 3 else lambda x : x
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return clone_func(output.view(output_shape))
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@staticmethod
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def backward(ctx, grad_output):
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if ctx.is_empty:
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bias_grad = (None if ctx.bias is None else torch.zeros_like(ctx.bias))
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return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, bias_grad, None
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req_gradA, req_gradB, _, req_gradBias, _ = ctx.needs_input_grad
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CAt, subA = ctx.tensors
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SCAt, idx = ctx.tensor_states
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formatB = ctx.formatB
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state = ctx.state
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grad_A = grad_B = grad_bias = None
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if req_gradBias:
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# compute grad_bias first before changing grad_output dtype
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grad_bias = grad_output.sum(0, dtype=ctx.dtype_bias)
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# Cast grad_output to fp16
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if len(grad_output.shape) == 3:
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grad_output = grad_output.reshape(
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-1, grad_output.shape[-1]
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).contiguous()
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Cgrad, Cgradt, SCgrad, SCgradt, coo_tensor = F.double_quant(grad_output.to(torch.float16))
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if req_gradB:
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CxAt, SAt = F.transform(CAt, formatB, transpose=True)
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C32grad, Sgrad = F.transform(Cgradt, "col32", transpose=True)
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gradB32, SgradB32 = F.igemmlt(C32grad, CxAt, Sgrad, SAt)
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grad_B = F.mm_dequant(gradB32, SgradB32, SCgradt, SCAt).to(ctx.dtype_B)
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if state.threshold > 0.0 and subA is not None:
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grad_B[:, idx] += torch.matmul(grad_output.t(), subA)
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if req_gradA:
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if state.CBt is not None:
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C32grad, Sgrad = F.transform(Cgrad, "col32")
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if state.CxBt is None:
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state.CxBt, state.SBt = F.transform(
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state.CBt, to_order=formatB, transpose=True
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)
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gradA32, SgradA32 = F.igemmlt(C32grad, state.CxBt, Sgrad, state.SBt)
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grad_A = F.mm_dequant(gradA32, SgradA32, SCgrad, state.SCBt).view(ctx.grad_shape).to(ctx.dtype_A)
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elif state.CB is not None:
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raise NotImplementedError("WIP")
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CB = state.CB.to(ctx.dtype_B)
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CB.mul_(state.SCB.unsqueeze(1).div_(127.0).to(CB.dtype))
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grad_A = torch.matmul(grad_output, CB).view(ctx.grad_shape).to(ctx.dtype_A)
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else:
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raise Exception('State must contain either CBt or CB matrix for backward')
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return grad_A, grad_B, None, grad_bias, None
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def matmul(
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A: tensor,
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B: tensor,
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out: tensor = None,
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state: MatmulLtState = None,
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threshold=0.0,
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bias=None
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):
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state = state or MatmulLtState()
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if threshold > 0.0:
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state.threshold = threshold
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return MatMul8bitLt.apply(A, B, out, bias, state)
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