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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 os
import time
from copy import deepcopy
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd.function import Function, InplaceFunction
from torch.cuda import amp
from .language_model.configuration_quantize import QuantizationConfig
from .qfunction import block_cut, block_quant, block_reshape
from .qutils import quant_get_local_rank
from .realquantize.division_transpose import fp8_division_transpose
from .realquantize.linear import fp8_linear_backward, fp8_linear_forward
from .realquantize.quantize_and_transpose import fp8_quantize_and_transpose
class QLinearTE(nn.Linear):
def __init__(self, in_features, out_features, bias=True, device=None, args=None, layer_idx=0):
super().__init__(in_features, out_features, bias, device)
try: # TODO: remove this try except (llama & qwen2)
self.args = QuantizationConfig(**deepcopy(args))
except:
self.args = deepcopy(args)
self.apply_quantize = min(self.weight.shape[0], self.weight.shape[1]) >= 3584
if quant_get_local_rank() == 0:
if self.apply_quantize:
print(f"[qlinear debug] Apply QLinear, {layer_idx}")
else:
print(f"[qlinear debug] Don't QLinear, {layer_idx} since the weight is too small: {self.weight.shape}")
self.layer_idx = layer_idx
self.layer_name = None
def forward(self, Input):
# if torch.randn(1) < 0.01:
# print(Input.shape, self.weight.shape)
if self.training and self.apply_quantize:
# if False:
output = QuantLinearTE.apply(Input, self.weight, self.bias, self.args, self.layer_name)
else:
output = F.linear(Input, self.weight, self.bias)
return output
# if int(os.environ.get("LOCAL_RANK")) == 0:
# import IPython
# IPython.embed()
# else:
# import time
# time.sleep(1000)
# class QuantLinearTE(Function):
# @staticmethod
# def forward(ctx, input, weight, bias, args, layer_type):
# ctx.saved = input, weight, bias, args, layer_type
# return F.linear(input, weight, bias)
# @staticmethod
# def backward(ctx, grad_output):
# input, weight, bias, args, layer_type = ctx.saved
# C_in = input.shape[-1]
# C_out = grad_output.shape[-1]
# grad_output_flatten = grad_output.reshape(-1, C_out)
# input_flatten = input.reshape(-1, C_in)
# if grad_output_flatten.dtype == input_flatten.dtype:
# grad_weight = grad_output_flatten.t().mm(input_flatten)
# else:
# grad_weight = grad_output_flatten.float().t().mm(input_flatten)
# if grad_output_flatten.dtype == weight.dtype:
# grad_input = grad_output_flatten.mm(weight)
# else:
# grad_input = grad_output_flatten.float().mm(weight)
# if bias is not None:
# grad_bias = grad_output_flatten.sum(0)
# else:
# grad_bias = None
# grad_input_transform = grad_input.reshape(input.size())
# return grad_input_transform, grad_weight, grad_bias, None, None
class QuantLinearTE(Function):
@staticmethod
@amp.custom_fwd(cast_inputs=torch.bfloat16)
def forward(ctx, input, weight, bias, args, layer_name):
time_bench = os.getenv("TIME_BENCH")
if time_bench:
start_1 = torch.cuda.Event(enable_timing=True)
start_1.record()
# Qinput, Iscale, Qinput_t = fp8_division_transpose(input, 16, args.fabit)
Qinput, Iscale, Qinput_t = fp8_quantize_and_transpose(input, 16, args.fabit, transpose_output_2d=True)
if time_bench:
end_1 = torch.cuda.Event(enable_timing=True)
end_1.record()
start_2 = torch.cuda.Event(enable_timing=True)
start_2.record()
# Qweight, Wscale, Qweight_t = fp8_division_transpose(weight, 16, args.fwbit)
Qweight, Wscale, Qweight_t = fp8_quantize_and_transpose(weight, 16, args.fwbit, transpose_output_2d=True)
if time_bench:
end_2 = torch.cuda.Event(enable_timing=True)
end_2.record()
start_3 = torch.cuda.Event(enable_timing=True)
start_3.record()
ctx.saved = Qinput_t, Iscale, Qweight_t, Wscale, bias, args, layer_name
fc_output = fp8_linear_forward(Qinput, Iscale, Qweight, Wscale, False, 0, bias)
if time_bench:
end_3 = torch.cuda.Event(enable_timing=True)
end_3.record()
start_4 = torch.cuda.Event(enable_timing=True)
start_4.record()
output = F.linear(input, weight, bias)
end_4 = torch.cuda.Event(enable_timing=True)
end_4.record()
torch.cuda.synchronize()
if quant_get_local_rank() == 0:
print(
f"[Forward] Part 1: {start_1.elapsed_time(end_1):.6f} ms | Part 2: {start_2.elapsed_time(end_2):.6f} ms | Part 3: {start_3.elapsed_time(end_3):.6f} ms | "
f"FP8: {start_1.elapsed_time(end_3):.6f} | BF16: {start_4.elapsed_time(end_4):.6f} | Input shape: {input.shape} | Weight shape: {weight.shape}"
)
return fc_output
@staticmethod
@amp.custom_bwd
def backward(ctx, grad_output):
Qinput_t, Iscale, Qweight_t, Wscale, bias, args, layer_name = ctx.saved
time_bench = os.getenv("TIME_BENCH")
if time_bench:
start_1 = torch.cuda.Event(enable_timing=True)
start_1.record()
# Qgrad_output, Gscale, Qgrad_output_t = fp8_division_transpose(grad_output, 16, args.bobit, stochastic=False)
Qgrad_output, Gscale, Qgrad_output_t = fp8_quantize_and_transpose(
grad_output, 16, args.bobit, stochastic=False, transpose_output_2d=True
)
if time_bench:
end_1 = torch.cuda.Event(enable_timing=True)
end_1.record()
start_2 = torch.cuda.Event(enable_timing=True)
start_2.record()
grad_input, grad_weight = fp8_linear_backward(
Qinput_t,
Iscale,
Qgrad_output,
Gscale,
Qgrad_output_t,
Qweight_t,
Wscale,
16,
bias,
stochastic=False,
dgrad_quantize=False,
)
if time_bench:
end_2 = torch.cuda.Event(enable_timing=True)
end_2.record()
start_3 = torch.cuda.Event(enable_timing=True)
start_3.record()
if bias is not None:
grad_bias = grad_output.reshape(-1, grad_output.shape[-1]).sum(0)
else:
grad_bias = None
if time_bench:
end_3 = torch.cuda.Event(enable_timing=True)
end_3.record()
# ========== BF16 ==========
C_in = Qinput_t.shape[0]
C_out = grad_output.shape[-1]
grad_output_flatten = grad_output.reshape(-1, C_out)
input_flatten = Qinput_t.t().reshape(-1, C_in).to(torch.bfloat16)
weight = Qweight_t.t().to(torch.bfloat16)
start_4 = torch.cuda.Event(enable_timing=True)
start_4.record()
if grad_output_flatten.dtype == input_flatten.dtype:
_grad_weight = grad_output_flatten.t().mm(input_flatten)
else:
_grad_weight = grad_output_flatten.float().t().mm(input_flatten)
if grad_output_flatten.dtype == weight.dtype:
_grad_input = grad_output_flatten.mm(weight)
else:
_grad_input = grad_output_flatten.float().mm(weight)
end_4 = torch.cuda.Event(enable_timing=True)
end_4.record()
torch.cuda.synchronize()
if quant_get_local_rank() == 0:
print(
f"[Backward] Part 1: {start_1.elapsed_time(end_1):.6f} ms | Part 2: {start_2.elapsed_time(end_2):.6f} ms | Part 3: {start_3.elapsed_time(end_3):.6f} ms | "
f"FP8: {start_1.elapsed_time(end_3):.6f} | BF16: {start_4.elapsed_time(end_4):.6f} | Input shape: {Qinput_t.shape} | Weight shape: {weight.shape}"
)
return grad_input, grad_weight, grad_bias, None, None
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