moss-moon-003-sft-int4 / quantization.py

Update quantization.py

e3f0d7e about 1 year ago

18.7 kB

	import numpy as np
	import torch
	import torch.nn as nn
	from torch.cuda.amp import custom_bwd, custom_fwd
	import math
	import triton
	import triton.language as tl
	from .custom_autotune import *


	def find_layers(module, layers=[nn.Conv2d, nn.Linear], name=''):
	if type(module) in layers:
	return {name: module}
	res = {}
	for name1, child in module.named_children():
	res.update(find_layers(
	child, layers=layers, name=name + '.' + name1 if name != '' else name1
	))
	return res


	# code based https://github.com/fpgaminer/GPTQ-triton
	@autotune(
	configs=[
	triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8},
	num_stages=4, num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8},
	num_stages=4, num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8},
	num_stages=4, num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8},
	num_stages=4, num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8},
	num_stages=4, num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8},
	num_stages=4, num_warps=4),
	# These provided a benefit on a 3090
	triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
	num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
	num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
	num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
	num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
	num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
	num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8},
	num_stages=4, num_warps=4),
	],
	key=['M', 'N'],
	nearest_power_of_two=True,
	)
	@triton.jit
	def matmul_248_kernel(a_ptr, b_ptr, c_ptr,
	scales_ptr, zeros_ptr, g_ptr,
	M, N, K, bits, maxq,
	stride_am, stride_ak,
	stride_bk, stride_bn,
	stride_cm, stride_cn,
	stride_scales, stride_zeros,
	BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
	GROUP_SIZE_M: tl.constexpr):
	"""
	Compute the matrix multiplication C = A x B.
	A is of shape (M, K) float16
	B is of shape (K//8, N) int32
	C is of shape (M, N) float16
	scales is of shape (G, N) float16
	zeros is of shape (G, N) float16
	g_ptr is of shape (K) int32
	"""
	infearure_per_bits = 32 // bits

	pid = tl.program_id(axis=0)
	num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
	num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
	num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
	num_pid_in_group = GROUP_SIZE_M * num_pid_n
	group_id = pid // num_pid_in_group
	first_pid_m = group_id * GROUP_SIZE_M
	group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
	pid_m = first_pid_m + (pid % group_size_m)
	pid_n = (pid % num_pid_in_group) // group_size_m

	offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
	offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
	offs_k = tl.arange(0, BLOCK_SIZE_K)
	a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
	a_mask = (offs_am[:, None] < M)
	# b_ptrs is set up such that it repeats elements along the K axis 8 times
	b_ptrs = b_ptr + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None,
	:] * stride_bn) # (BLOCK_SIZE_K, BLOCK_SIZE_N)
	g_ptrs = g_ptr + offs_k
	# shifter is used to extract the N bits of each element in the 32-bit word from B
	scales_ptrs = scales_ptr + offs_bn[None, :]
	zeros_ptrs = zeros_ptr + (offs_bn[None, :] // infearure_per_bits)

	shifter = (offs_k % infearure_per_bits) * bits
	zeros_shifter = (offs_bn % infearure_per_bits) * bits
	accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)

	for k in range(0, num_pid_k):
	g_idx = tl.load(g_ptrs)

	# Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
	scales = tl.load(scales_ptrs + g_idx[:, None] * stride_scales) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
	zeros = tl.load(zeros_ptrs + g_idx[:, None] * stride_zeros) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)

	zeros = (zeros >> zeros_shifter[None, :]) & maxq
	zeros = (zeros + 1)

	a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
	b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated

	# Now we need to unpack b (which is N-bit values) into 32-bit values
	b = (b >> shifter[:, None]) & maxq # Extract the N-bit values
	b = (b - zeros) * scales # Scale and shift

	accumulator += tl.dot(a, b)
	a_ptrs += BLOCK_SIZE_K
	b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
	g_ptrs += BLOCK_SIZE_K

	c = accumulator.to(tl.float16)
	c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
	c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
	tl.store(c_ptrs, accumulator, mask=c_mask)


	# code based https://github.com/fpgaminer/GPTQ-triton
	@autotune(
	configs=[
	triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8},
	num_stages=4, num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 256, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8},
	num_stages=4, num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_K': 128, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8},
	num_stages=4, num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8},
	num_stages=4, num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 128, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8},
	num_stages=4, num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_K': 32, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8},
	num_stages=4, num_warps=4),
	# These provided a benefit on a 3090
	triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
	num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
	num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 32, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
	num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
	num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
	num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 32, 'BLOCK_SIZE_N': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
	num_warps=4),
	triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 128, 'GROUP_SIZE_M': 8},
	num_stages=4, num_warps=4),
	],
	key=['M', 'K'],
	nearest_power_of_two=True,
	)
	@triton.jit
	def trans_matmul_248_kernel(a_ptr, b_ptr, c_ptr,
	scales_ptr, zeros_ptr, g_ptr,
	M, N, K, bits, maxq,
	stride_am, stride_ak,
	stride_bk, stride_bn,
	stride_cm, stride_cn,
	stride_scales, stride_zeros,
	BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
	GROUP_SIZE_M: tl.constexpr):
	"""
	Compute the matrix multiplication C = A x B.
	A is of shape (M, N) float16
	B is of shape (K//8, N) int32
	C is of shape (M, K) float16
	scales is of shape (G, N) float16
	zeros is of shape (G, N) float16
	g_ptr is of shape (K) int32
	"""
	infearure_per_bits = 32 // bits

	pid = tl.program_id(axis=0)
	num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
	num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
	num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
	num_pid_in_group = GROUP_SIZE_M * num_pid_k
	group_id = pid // num_pid_in_group
	first_pid_m = group_id * GROUP_SIZE_M
	group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
	pid_m = first_pid_m + (pid % group_size_m)
	pid_k = (pid % num_pid_in_group) // group_size_m

	offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
	offs_bk = pid_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
	offs_n = tl.arange(0, BLOCK_SIZE_N)
	a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_n[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_N)
	a_mask = (offs_am[:, None] < M)
	# b_ptrs is set up such that it repeats elements along the K axis 8 times
	b_ptrs = b_ptr + ((offs_bk[:, None] // infearure_per_bits) * stride_bk + offs_n[None,
	:] * stride_bn) # (BLOCK_SIZE_K, BLOCK_SIZE_N)
	g_ptrs = g_ptr + offs_bk
	g_idx = tl.load(g_ptrs)

	# shifter is used to extract the N bits of each element in the 32-bit word from B
	scales_ptrs = scales_ptr + offs_n[None, :] + g_idx[:, None] * stride_scales
	zeros_ptrs = zeros_ptr + (offs_n[None, :] // infearure_per_bits) + g_idx[:, None] * stride_zeros

	shifter = (offs_bk % infearure_per_bits) * bits
	zeros_shifter = (offs_n % infearure_per_bits) * bits
	accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_K), dtype=tl.float32)

	for k in range(0, num_pid_n):
	# Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
	scales = tl.load(scales_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
	zeros = tl.load(zeros_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)

	zeros = (zeros >> zeros_shifter[None, :]) & maxq
	zeros = (zeros + 1)

	a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_N)
	b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated

	# Now we need to unpack b (which is N-bit values) into 32-bit values
	b = (b >> shifter[:, None]) & maxq # Extract the N-bit values
	b = (b - zeros) * scales # Scale and shift
	b = tl.trans(b)

	accumulator += tl.dot(a, b)
	a_ptrs += BLOCK_SIZE_N
	b_ptrs += BLOCK_SIZE_N
	scales_ptrs += BLOCK_SIZE_N
	zeros_ptrs += (BLOCK_SIZE_N // infearure_per_bits)

	c = accumulator.to(tl.float16)
	c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bk[None, :]
	c_mask = (offs_am[:, None] < M) & (offs_bk[None, :] < K)
	tl.store(c_ptrs, accumulator, mask=c_mask)


	def matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq):
	output = torch.empty((input.shape[0], qweight.shape[1]), device='cuda', dtype=torch.float16)
	grid = lambda META: (
	triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']),)
	matmul_248_kernel[grid](input, qweight, output,
	scales, qzeros, g_idx,
	input.shape[0], qweight.shape[1], input.shape[1], bits, maxq,
	input.stride(0), input.stride(1),
	qweight.stride(0), qweight.stride(1),
	output.stride(0), output.stride(1),
	scales.stride(0), qzeros.stride(0))
	return output


	def transpose_matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq):
	output_dim = (qweight.shape[0] * 32) // bits
	output = torch.empty((input.shape[0], output_dim), device='cuda', dtype=torch.float16)
	grid = lambda META: (
	triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(output_dim, META['BLOCK_SIZE_K']),)
	transpose_matmul_248_kernel[grid](input, qweight, output,
	scales, qzeros, g_idx,
	input.shape[0], qweight.shape[1], output_dim, bits, maxq,
	input.stride(0), input.stride(1),
	qweight.stride(0), qweight.stride(1),
	output.stride(0), output.stride(1),
	scales.stride(0), qzeros.stride(0))
	return output


	class QuantLinearFunction(torch.autograd.Function):
	@staticmethod
	@custom_fwd(cast_inputs=torch.float16)
	def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq):
	output = matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq)
	ctx.save_for_backward(qweight, scales, qzeros, g_idx)
	ctx.bits, ctx.maxq = bits, maxq
	return output

	@staticmethod
	@custom_bwd
	def backward(ctx, grad_output):
	qweight, scales, qzeros, g_idx = ctx.saved_tensors
	bits, maxq = ctx.bits, ctx.maxq
	grad_input = None

	if ctx.needs_input_grad[0]:
	grad_input = transpose_matmul248(grad_output, qweight, scales, qzeros, g_idx, bits, maxq)
	return grad_input, None, None, None, None, None, None

	class QuantLinear(nn.Module):
	def __init__(self, bits, groupsize, infeatures, outfeatures, bias):
	super().__init__()
	if bits not in [2, 4, 8]:
	raise NotImplementedError("Only 2,4,8 bits are supported.")
	self.infeatures = infeatures
	self.outfeatures = outfeatures
	self.bits = bits
	self.maxq = 2 ** self.bits - 1
	self.groupsize = groupsize if groupsize != -1 else infeatures

	self.register_buffer('qweight', torch.zeros((infeatures // 32 * self.bits, outfeatures), dtype=torch.int32))
	self.register_buffer('qzeros', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures // 32 * self.bits), dtype=torch.int32))
	self.register_buffer('scales', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures), dtype=torch.float16))
	self.register_buffer('g_idx', torch.tensor([i // self.groupsize for i in range(infeatures)], dtype=torch.int32))
	if bias:
	self.register_buffer('bias', torch.zeros((outfeatures), dtype=torch.float16))
	else:
	self.bias = None

	def pack(self, linear, scales, zeros, g_idx=None):
	self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx

	scales = scales.t().contiguous()
	zeros = zeros.t().contiguous()
	scale_zeros = zeros * scales
	self.scales = scales.clone().half()
	if linear.bias is not None:
	self.bias = linear.bias.clone().half()

	intweight = []
	for idx in range(self.infeatures):
	intweight.append(torch.round(
	(linear.weight.data[:, idx] + scale_zeros[self.g_idx[idx]]) / self.scales[self.g_idx[idx]]).to(
	torch.int)[:, None])
	intweight = torch.cat(intweight, dim=1)
	intweight = intweight.t().contiguous()
	intweight = intweight.numpy().astype(np.uint32)
	qweight = np.zeros((intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32)
	i = 0
	row = 0
	while row < qweight.shape[0]:
	if self.bits in [2, 4, 8]:
	for j in range(i, i + (32 // self.bits)):
	qweight[row] \|= intweight[j] << (self.bits * (j - i))
	i += 32 // self.bits
	row += 1
	else:
	raise NotImplementedError("Only 2,4,8 bits are supported.")

	qweight = qweight.astype(np.int32)
	self.qweight = torch.from_numpy(qweight)

	zeros -= 1
	zeros = zeros.numpy().astype(np.uint32)
	qzeros = np.zeros((zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32)
	i = 0
	col = 0
	while col < qzeros.shape[1]:
	if self.bits in [2, 4, 8]:
	for j in range(i, i + (32 // self.bits)):
	qzeros[:, col] \|= zeros[:, j] << (self.bits * (j - i))
	i += 32 // self.bits
	col += 1
	else:
	raise NotImplementedError("Only 2,4,8 bits are supported.")

	qzeros = qzeros.astype(np.int32)
	self.qzeros = torch.from_numpy(qzeros)

	def forward(self, x):
	out_shape = x.shape[:-1] + (self.outfeatures,)
	out = QuantLinearFunction.apply(x.reshape(-1, x.shape[-1]), self.qweight, self.scales,
	self.qzeros, self.g_idx, self.bits, self.maxq)
	out = out + self.bias if self.bias is not None else out
	return out.reshape(out_shape)

	def make_quant(module, names, bits, groupsize, name=''):
	if isinstance(module, QuantLinear):
	return
	for attr in dir(module):
	tmp = getattr(module, attr)
	name1 = name + '.' + attr if name != '' else attr
	if name1 in names:
	delattr(module, attr)
	setattr(module, attr, QuantLinear(bits, groupsize, tmp.in_features, tmp.out_features, tmp.bias is not None))
	for name1, child in module.named_children():
	make_quant(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1)


	def quantize_with_gptq(model, wbits, groupsize):
	model = model.eval()
	layers = find_layers(model)
	for name in ['lm_head']:
	if name in layers:
	del layers[name]
	make_quant(model, layers, wbits, groupsize)
	# model.load_state_dict(torch.load(checkpoint))
	return model