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# Copyright (c) 2025 NVIDIA CORPORATION.
# Licensed under the MIT license.
# Adapted from https://github.com/NVlabs/VILA/tree/main under the Apache 2.0 license.
# LICENSE is in incl_licenses directory.
import re
import torch
try:
from .FloatPointQuantizeTorch import *
from .FloatPointQuantizeTriton import *
except:
from FloatPointQuantizeTorch import *
from FloatPointQuantizeTriton import *
def block_cut(input, row_block, column_block, pad_block=False):
# print(input.shape)
original_shape = input.shape
# input tensor shape is M * N
if len(input.shape) > 2:
input = input.reshape(-1, input.shape[2])
elif len(input.shape) == 2:
pass
else:
raise ValueError(f"input shape {input.shape} does not match for block cut, {input}")
M, N = input.shape[0], input.shape[1]
if row_block == -1:
row_block = M
if column_block == -1:
column_block = N
if pad_block:
row_remainder, col_remainder = M % row_block, N % column_block
if row_remainder:
row_pad = row_block - row_remainder
else:
row_pad = 0
if col_remainder:
col_pad = column_block - col_remainder
else:
col_pad = 0
input = torch.nn.functional.pad(
input, (0, col_pad, 0, row_pad), "constant", 0
) # refer to torch's doc to see why
M, N = input.shape[0], input.shape[1]
row_num, column_num = M // row_block, N // column_block
else:
row_num, column_num = M // row_block, N // column_block
assert row_num * row_block == M, f"{row_num}, {row_block}, {M}, {original_shape}"
assert column_num * column_block == N, f"{column_num}, {column_block}, {N}, {original_shape}"
# print(input.shape)
input = (
input.reshape(row_num, row_block, column_num, column_block)
.permute(0, 2, 1, 3)
.reshape(row_num * column_num, row_block, column_block)
)
# print(input.shape)
return input
def block_reshape(input, origin_input, row_block, column_block, pad_block=False):
if len(origin_input.shape) > 2:
flatten_input = origin_input.reshape(-1, origin_input.shape[2])
elif len(origin_input.shape) == 2:
flatten_input = origin_input
else:
raise ValueError(f"input shape {input.shape} does not match for block cut")
M, N = flatten_input.shape[0], flatten_input.shape[1]
if row_block == -1:
row_block = M
if column_block == -1:
column_block = N
if pad_block:
row_remainder, col_remainder = M % row_block, N % column_block
if row_remainder:
row_pad = row_block - row_remainder
else:
row_pad = 0
if col_remainder:
col_pad = column_block - col_remainder
else:
col_pad = 0
pad_origin_input = torch.nn.functional.pad(origin_input, (0, col_pad, 0, row_pad), "constant", 0)
M, N = pad_origin_input.shape[0], pad_origin_input.shape[1]
row_num, column_num = M // row_block, N // column_block
else:
row_num, column_num = M // row_block, N // column_block
input = (
input.reshape(row_num, column_num, row_block, column_block)
.permute(0, 2, 1, 3)
.reshape(row_num * row_block, column_num * column_block)
)
M, N = flatten_input.shape[0], flatten_input.shape[1]
input = input[:M, :N]
if len(origin_input.shape) > 2:
input = input.reshape(origin_input.shape)
elif len(origin_input.shape) == 2:
pass
else:
raise ValueError(f"input shape {input.shape} does not match for block reshape")
return input
def block_verify_int8(input, row_block, column_block, layer_type, necessary=True):
Binput = block_cut(input, row_block, column_block)
Binput = Binput.to(torch.float32)
for n in range(Binput.shape[0]):
unique_values = len(torch.unique(Binput[n, :, :]))
if unique_values > 256:
if necessary:
raise ValueError(f"{layer_type} contains more than 256 unique values.")
else:
return False
return True
def block_quant(input, symm, bits, stochastic, epsilon, apply_quantize, layer_name):
Quant_fn = SymmQuantizer
return Quant_fn.apply(input, symm, bits, stochastic, epsilon, apply_quantize, layer_name)
def extract_bit(string):
match = re.match(r"INT(\d+)", string)
if match:
return "integer", int(match.group(1)), None
match = re.match(r"E(\d+)M(\d+)", string)
if match:
Ebit, Mbit = int(match.group(1)), int(match.group(2))
if Ebit == 1:
return "integer", Mbit + 1, None
if Mbit == 0:
return "floatExM0", int(match.group(1)), 0
return "floatExMy", int(match.group(1)), int(match.group(2))
match = re.match(r"DE(\d+)", string)
if match:
return "Dynamic", int(match.group(1)), None
match = re.match(r"ZeroD(\d+)", string)
if match:
return "ZeroDynamic", int(match.group(1)), None
raise ValueError(f"{string} data format is not supported")
class SymmQuantizer(torch.autograd.function.InplaceFunction):
@staticmethod
def forward(ctx, input, symm, bits, stochastic, epsilon, apply_quantize=True, layer_name=None):
with torch.no_grad():
absmax_per_block = input.abs().amax(dim=(1, 2)).unsqueeze(1).unsqueeze(2) + epsilon
if bits == "100" or not apply_quantize:
return input, input, torch.ones_like(absmax_per_block)
elif bits == "FP32":
return input.to(torch.float32), input.to(torch.float32), torch.ones_like(absmax_per_block)
elif bits == "FP16":
return input.to(torch.float16), input.to(torch.float16), torch.ones_like(absmax_per_block)
elif bits == "BF16":
return input.to(torch.bfloat16), input.to(torch.bfloat16), torch.ones_like(absmax_per_block)
else:
QuantType, bit1, bit2 = extract_bit(bits)
if not symm:
bit1 = bit1 + 1 # pretend to be asymmtric
if QuantType == "integer":
Qn, Qp = -(2 ** (bit1 - 1) - 1), 2 ** (bit1 - 1) - 1
elif QuantType == "floatExMy":
Qn, Qp = -(2 - 2 ** (-bit2)) * (2 ** (2 ** (bit1 - 1))), (2 - 2 ** (-bit2)) * (
2 ** (2 ** (bit1 - 1))
)
if bit1 == 4 and bit2 == 3:
Qn, Qp = -448, 448
if bit1 == 5 and bit2 == 2:
Qn, Qp = -57344, 57344
elif QuantType == "floatExM0":
Qn, Qp = -(2 ** (2 ** (bit1 - 1))) + 1, 2 ** (2 ** (bit1 - 1))
elif QuantType == "Dynamic":
Qn, Qp = -1, 1
elif QuantType == "ZeroDynamic":
Qn, Qp = -1, 1
else:
raise NotImplementedError(f"{bits} is not supported by quantization")
scale_per_block = (2 * absmax_per_block) / (Qp - Qn)
scale_per_block = scale_per_block.to(input)
Qinput = input / scale_per_block
if QuantType == "integer":
if stochastic:
noise = Qinput.new(Qinput.shape).uniform_(-0.5, 0.5)
Qinput.add_(noise)
Qinput.clamp_(Qn, Qp).round_()
elif QuantType == "floatExMy":
# Qinput = floatExMy_quantize_torch(Qinput, bit1, bit2, stochastic)
Qinput = floatExMy_quantize_triton(Qinput, bit1, bit2, stochastic)
elif QuantType == "floatExM0":
Qinput = floatExM0_quantize_torch(Qinput, bit1, stochastic)
elif QuantType == "Dynamic":
Qinput = Dynamic_quantize_torch(Qinput, bit1, stochastic)
elif QuantType == "ZeroDynamic":
Qinput = ZeroDynamic_quantize_torch(Qinput, bit1, stochastic)
else:
raise NotImplementedError(f"{bits} is not supported by quantization")
RQinput = Qinput * scale_per_block
# print(f'Layer Name: {layer_name}: {input, Qinput, scale_per_block, absmax_per_block}', file=open('debug.txt', 'a'))
if input.dtype != Qinput.dtype:
print(
f"Input type is {input.dtype}, Qinput type is {Qinput.dtype}, scale_per_block type is {scale_per_block.dtype}",
file=open("debug.txt", "a"),
)
import IPython
IPython.embed()
return RQinput, Qinput, scale_per_block
@staticmethod
def backward(ctx, grad_output):
return grad_output, None, None, None, None, None
if __name__ == "__main__":
import time
torch.manual_seed(0)
X = (torch.rand(2048, 4096).cuda() - 0.5) * 1000
X = X.to(torch.bfloat16)
B_X = block_cut(X, -1, -1)
RQ_X, Q_X, S_X = block_quant(
B_X, symm=True, bits="E2M0", stochastic=True, epsilon=1e-14, apply_quantize=True, layer_name=""
)
RQ_X = block_reshape(RQ_X, X, -1, -1)
Q_X = block_reshape(Q_X, X, -1, -1)
# start = time.time()
# for _ in range(10000):
# B_X = block_cut(X, 2, 2)
# RQ_X, Q_X, S_X = block_quant(B_X, symm=True, bits="E5M2", stochastic=True, epsilon=1e-14, apply_quantize=True, layer_name='')
# RQ_X = block_reshape(RQ_X, X, 2, 2)
# Q_X = block_reshape(Q_X, X, 2, 2)
# torch.cuda.synchronize()
# end = time.time()
# print(f"Time cost: {end - start}")
print(X.dtype)
import IPython
IPython.embed()
# X = torch.tensor([
# [1.1, 1, 1, 2, 2, 2, 3, 3, 3.01],
# [1.2, 1, 1, 2, 2, 2, 3, 3, 3.02],
# [4.1, 4, 4, 5, 5, 5, 6, 6, 6.01],
# [4.2, 4, 4, 5, 5, 5, 6, 6, 6.02],
# [7.1, 7, 7, 8, 8, 8, 9, 9, 9.01],
# [7.2, 7, 7, 8, 8, 8, 9, 9, 9.02],
# ])
# B_X = block_cut(X, 2, 3)
# print(B_X)
# print(B_X.shape)
# Q_X = block_quant(B_X, symm=True, bits=2, stochastic=False)
# print(Q_X)
# print(Q_X.shape)
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