568 lines
21 KiB
Python
568 lines
21 KiB
Python
import operator
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import warnings
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from dataclasses import dataclass
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from functools import reduce # Required in Python 3
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from typing import Tuple, Optional, List
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import torch
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import bitsandbytes.functional as F
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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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# The inverse transformation for the colTuring and colAmpere format were contributed by Alex Borzunov:
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# https://github.com/bigscience-workshop/petals/blob/main/src/petals/utils/linear8bitlt_patch.py
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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:
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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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def get_inverse_transform_indices(transform_tile: callable, tile_size: Tuple[int, int]):
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"""
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Compute a permutation of indices that invert the specified (tiled) matrix transformation
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:param transform_tile: a function that applies forward transform to a tensor of shape [dim1, dim2]
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:param tile_size: higher-level tile dimensions, i.e. (8, 32) for Turing and (32, 32) for Ampere
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:note: we assume that tile_transform applies to a cpu-based int8 tensor of shape tile_size
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:example: transform_tile function for the turing layout (bitsandbytes.functional as F)
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:returns: indices
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"""
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d1, d2 = tile_size
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assert 0 < d1 * d2 < 2**64
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tile_indices = torch.arange(d1 * d2, dtype=torch.int64).view(d1, d2)
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# encode each position in tile as a tuple of <= 8 unique bytes
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permuted_tile_indices = torch.zeros_like(tile_indices)
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for i in range(8):
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# select i-th byte, apply transformation and trace where each index ended up
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ith_dim_indices = torch.div(tile_indices, 256**i, rounding_mode="trunc") % 256
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sample_tile_i = (ith_dim_indices - 128).to(torch.int8).contiguous()
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assert torch.all(sample_tile_i.int() + 128 == ith_dim_indices), "int overflow"
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permuted_tile_i = transform_tile(sample_tile_i)
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ith_permuted_indices = permuted_tile_i.to(tile_indices.dtype) + 128
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permuted_tile_indices += ith_permuted_indices * (256**i)
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if d1 * d2 < 256**i:
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break # if all indices fit in i bytes, stop early
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return permuted_tile_indices
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def undo_layout(permuted_tensor: torch.Tensor, tile_indices: torch.LongTensor) -> torch.Tensor:
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"""
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Undo a tiled permutation such as turing or ampere layout
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:param permuted_tensor: torch tensor in a permuted layout
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:param tile_indices: reverse transformation indices, from get_inverse_transform_indices
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:return: contiguous row-major tensor
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"""
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(rows, cols), (tile_rows, tile_cols) = permuted_tensor.shape, tile_indices.shape
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assert rows % tile_rows == cols % tile_cols == 0, "tensor must contain a whole number of tiles"
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tensor = permuted_tensor.reshape(-1, tile_indices.numel()).t()
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outputs = torch.empty_like(tensor) # note: not using .index_copy because it was slower on cuda
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outputs[tile_indices.flatten()] = tensor
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outputs = outputs.reshape(tile_rows, tile_cols, cols // tile_cols, rows // tile_rows)
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outputs = outputs.permute(3, 0, 2, 1) # (rows // tile_rows, tile_rows), (cols // tile_cols, tile_cols)
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return outputs.reshape(rows, cols).contiguous()
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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=None):
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if precision is None:
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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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def supports_igemmlt(device: torch.device) -> bool:
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"""check if this device supports the optimized int8 kernel"""
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if torch.cuda.get_device_capability(device=device) < (7, 5):
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return False
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device_name = torch.cuda.get_device_name(device=device)
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nvidia16_models = ('GTX 1630', 'GTX 1650', 'GTX 1660') # https://en.wikipedia.org/wiki/GeForce_16_series
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if any(model_name in device_name for model_name in nvidia16_models):
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return False # these devices are technically cuda 7.5-capable, but they lack tensor cores
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return True
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def _get_tile_size(format):
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assert format in (
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"col_turing",
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"col_ampere",
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), f"please find this assert and manually enter tile size for {format}"
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return (8, 32) if format == "col_turing" else (32, 32)
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def get_tile_inds(format, device):
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transform = lambda x: F.transform(x.to(device), from_order="row", to_order=format)[0].to(x.device)
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with torch.no_grad():
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return get_inverse_transform_indices(transform, _get_tile_size(format)).to(device)
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@dataclass
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class MatmulLtState:
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_tile_indices: Optional[torch.Tensor] = None
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force_no_igemmlt: bool = False
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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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@property
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def tile_indices(self):
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if self._tile_indices is None:
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self._tile_indices = get_tile_inds(self.formatB, self.CxB.device)
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return self._tile_indices
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class MatMul8bitLt(torch.autograd.Function):
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# forward is the same, but we added the fallback for pre-turing GPUs
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# backward is mostly the same, but adds one extra clause (see "elif state.CxB is not None")
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@staticmethod
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def forward(ctx, A, B, out=None, bias=None, state=MatmulLtState):
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using_igemmlt = supports_igemmlt(A.device) and not state.force_no_igemmlt
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# default of 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(A.to(torch.float16), threshold=state.threshold)
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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 and using_igemmlt:
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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 and using_igemmlt:
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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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if using_igemmlt:
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state.CxB, state.SB = F.transform(CB, to_order=formatB)
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else:
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state.CB = CB
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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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if state.CxB is not None:
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outliers = F.extract_outliers(state.CxB, state.SB, state.idx.int())
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else:
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outliers = state.CB[:, state.idx.long()].clone()
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state.subB = (outliers * state.SCB.view(-1, 1) / 127.0).t().contiguous().to(A.dtype)
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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] if state.SB else B.shape
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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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if using_igemmlt:
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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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if bias is None or bias.dtype == torch.float16:
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# we apply the fused bias here
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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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else:
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A_wo_outliers = A.clone()
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if state.idx is not None:
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A_wo_outliers[:, state.idx.long()] = 0
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output = torch.nn.functional.linear(A_wo_outliers, state.CB.to(A.dtype))
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output = output.mul_(state.SCB.unsqueeze(0).mul(1.0 / 127.0))
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if bias is not None:
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output = output.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, A)
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ctx.tensor_states = (SCAt, state.idx)
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else:
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ctx.tensors = [None, None, A]
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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, A = 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(-1, grad_output.shape[-1]).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)
|
|
C32grad, Sgrad = F.transform(Cgradt, "col32", transpose=True)
|
|
gradB32, SgradB32 = F.igemmlt(C32grad, CxAt, Sgrad, SAt)
|
|
grad_B = F.mm_dequant(gradB32, SgradB32, SCgradt, SCAt)
|
|
if state.threshold > 0.0 and subA is not None:
|
|
grad_B[:, idx] += torch.matmul(grad_output.t(), subA)
|
|
|
|
if req_gradA:
|
|
if state.CBt is not None:
|
|
C32grad, Sgrad = F.transform(Cgrad, "col32")
|
|
if state.CxBt is None:
|
|
state.CxBt, state.SBt = F.transform(state.CBt, to_order=formatB, transpose=True)
|
|
gradA32, SgradA32 = F.igemmlt(C32grad, state.CxBt, Sgrad, state.SBt)
|
|
grad_A = F.mm_dequant(gradA32, SgradA32, SCgrad, state.SCBt).view(ctx.grad_shape).to(ctx.dtype_A)
|
|
|
|
elif state.CB is not None:
|
|
CB = state.CB.to(ctx.dtype_A, copy=True).mul_(state.SCB.unsqueeze(1).mul(1.0 / 127.0))
|
|
grad_A = torch.matmul(grad_output, CB).view(ctx.grad_shape).to(ctx.dtype_A)
|
|
elif state.CxB is not None:
|
|
CB = (
|
|
undo_layout(state.CxB, state.tile_indices)
|
|
.to(ctx.dtype_A)
|
|
.mul_(state.SCB.unsqueeze(1).mul(1.0 / 127.0))
|
|
)
|
|
grad_A = torch.matmul(grad_output, CB).view(ctx.grad_shape).to(ctx.dtype_A)
|
|
else:
|
|
raise Exception("State must contain either CBt or CB or CxB matrix for backward")
|
|
|
|
return grad_A, grad_B, None, grad_bias, None
|
|
|
|
|
|
class MatMul4Bit(torch.autograd.Function):
|
|
# forward is the same, but we added the fallback for pre-turing GPUs
|
|
# backward is mostly the same, but adds one extra clause (see "elif state.CxB is not None")
|
|
|
|
@staticmethod
|
|
def forward(ctx, A, B, out=None, bias=None, state=None):
|
|
# default of pytorch behavior if inputs are empty
|
|
ctx.is_empty = False
|
|
if prod(A.shape) == 0:
|
|
ctx.is_empty = True
|
|
ctx.A = A
|
|
ctx.B = B
|
|
ctx.bias = bias
|
|
B_shape = state[1]
|
|
if A.shape[-1] == B_shape[0]:
|
|
return torch.empty(A.shape[:-1] + B_shape[1:], dtype=A.dtype, device=A.device)
|
|
else:
|
|
return torch.empty(A.shape[:-1] + B_shape[:1], dtype=A.dtype, device=A.device)
|
|
|
|
|
|
# 1. Dequantize
|
|
# 2. MatmulnN
|
|
output = torch.nn.functional.linear(A, F.dequantize_fp4(B, state).to(A.dtype).t(), bias)
|
|
|
|
# 3. Save state
|
|
ctx.state = state
|
|
ctx.dtype_A, ctx.dtype_B, ctx.dtype_bias = A.dtype, B.dtype, None if bias is None else bias.dtype
|
|
|
|
if any(ctx.needs_input_grad[:2]):
|
|
ctx.tensors = (A, B)
|
|
else:
|
|
ctx.tensors = (None, None)
|
|
|
|
return output
|
|
|
|
@staticmethod
|
|
def backward(ctx, grad_output):
|
|
if ctx.is_empty:
|
|
bias_grad = None if ctx.bias is None else torch.zeros_like(ctx.bias)
|
|
return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, bias_grad, None
|
|
|
|
req_gradA, _, _, req_gradBias, _= ctx.needs_input_grad
|
|
A, B = ctx.tensors
|
|
state = ctx.state
|
|
|
|
grad_A, grad_B, grad_bias = None, None, None
|
|
|
|
if req_gradBias:
|
|
# compute grad_bias first before changing grad_output dtype
|
|
grad_bias = grad_output.sum(0, dtype=ctx.dtype_bias)
|
|
|
|
# not supported by PyTorch. TODO: create work-around
|
|
#if req_gradB: grad_B = torch.matmul(grad_output.t(), A)
|
|
if req_gradA: grad_A = torch.matmul(grad_output, F.dequantize_fp4(B, ctx.state).to(grad_output.dtype).t())
|
|
|
|
return grad_A, grad_B, None, grad_bias, None
|
|
|
|
|
|
def matmul(
|
|
A: tensor,
|
|
B: tensor,
|
|
out: tensor = None,
|
|
state: MatmulLtState = None,
|
|
threshold=0.0,
|
|
bias=None
|
|
):
|
|
state = state or MatmulLtState()
|
|
if threshold > 0.0:
|
|
state.threshold = threshold
|
|
return MatMul8bitLt.apply(A, B, out, bias, state)
|
|
|
|
|
|
def matmul_4bit(A: tensor, B: tensor, quant_state: List, out: tensor = None, bias=None):
|
|
assert quant_state is not None
|
|
return MatMul4Bit.apply(A, B, out, bias, quant_state)
|