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Commit a24aae30 authored by Jeongseok Kang's avatar Jeongseok Kang
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Merge branch 'main' into fix/libcuda-to-torch

parents 2b4cc256 4395d68c
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bitsandbytes/research/__init__.py 0 → 100644
View file @ a24aae30
from . import nn
from .autograd._functions import (
switchback_bnb,
matmul_fp8_global,
matmul_fp8_mixed,
)
bitsandbytes/research/autograd/_functions.py 0 → 100644
View file @ a24aae30
import operator
import warnings
from dataclasses import dataclass
from functools import reduce # Required in Python 3
import torch
import bitsandbytes.functional as F
from bitsandbytes.autograd._functions import MatmulLtState, GlobalOutlierPooler
# math.prod not compatible with python < 3.8
def prod(iterable):
return reduce(operator.mul, iterable, 1)
tensor = torch.Tensor
class MatMulFP8Mixed(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, fw_code=None, bw_code=None, bsz=1024, bsz2=1024):
# 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
B_shape = B.shape
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
cA, state = F.quantize_blockwise(A, code=fw_code, blocksize=bsz)
fp8A = F.dequantize_blockwise(cA, state, blocksize=bsz).to(A.dtype)
cB, state = F.quantize(B.float(), code=fw_code)
fp8B = F.dequantize(cB, state).to(B.dtype)
output = torch.matmul(fp8A, fp8B)
# output is half
# 3. Save state
ctx.fw_code = fw_code
ctx.bw_code = bw_code
ctx.bsz = bsz
ctx.bsz2 = bsz2
ctx.dtype_A, ctx.dtype_B = A.dtype, B.dtype
if any(ctx.needs_input_grad[:2]):
# NOTE: we send back A, and re-quant.
ctx.tensors = (A, fp8B)
else:
ctx.tensors = (None, None)
return output
@staticmethod
def backward(ctx, grad_output):
if ctx.is_empty:
return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, None, None, None, None
req_gradA, req_gradB, _, _, _, _, _ = ctx.needs_input_grad
A, B = ctx.tensors
grad_A, grad_B = None, None
# TODO: Fix blocksize to be output_dim
cgrad_out, state = F.quantize_blockwise(grad_output, code=ctx.bw_code, blocksize=ctx.bsz2)
fp8out = F.dequantize_blockwise(cgrad_out, state, blocksize=ctx.bsz2).to(grad_output.dtype)
# cgrad_output_2, state_2 = F.quantize(grad_output.float(), code=ctx.bw_code)
# fp8out_2 = F.dequantize(cgrad_output_2, state_2).to(grad_output.dtype)
# grad_output_reshape = grad_output.reshape(-1, grad_output.shape[-1]).contiguous()
# fp8grad_transpose, stategrad_transpose = F.vectorwise_quant(grad_output_reshape, dim=0, quant_type='vector')
# fp8out_transpose = (fp8grad_transpose / 7) * stategrad_transpose
# fp8out_transpose = fp8out_transpose.view(grad_output.shape[0], grad_output.shape[1], grad_output.shape[2])
# not supported by PyTorch. TODO: create work-around
if req_gradA:
grad_A = torch.matmul(fp8out, B.t().to(fp8out.dtype)).to(A.dtype)
if req_gradB:
if len(A.shape) == 3:
At = A.transpose(2, 1).contiguous()
else:
At = A.transpose(1, 0).contiguous()
# cA, state = F.quantize(At.float(), code=ctx.fw_code)
# fp8At = F.dequantize(cA, state).to(A.dtype)
grad_B = torch.matmul(At.to(grad_output.dtype), grad_output).to(B.dtype)
return grad_A, grad_B, None, None, None, None, None
class MatMulFP8Global(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, fw_code=None, bw_code=None, bsz=1024, bsz2=1024):
# 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
B_shape = B.shape
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
cA, state = F.quantize(A.float(), code=fw_code)
fp8A = F.dequantize(cA, state).to(A.dtype)
cB, state = F.quantize(B.float(), code=fw_code)
fp8B = F.dequantize(cB, state).to(B.dtype)
output = torch.matmul(fp8A, fp8B)
# output is half
# 3. Save state
ctx.fw_code = fw_code
ctx.bw_code = bw_code
ctx.bsz = bsz
ctx.bsz2 = bsz2
ctx.dtype_A, ctx.dtype_B = A.dtype, B.dtype
if any(ctx.needs_input_grad[:2]):
# NOTE: we send back A, and re-quant.
ctx.tensors = (A, fp8B)
else:
ctx.tensors = (None, None)
return output
@staticmethod
def backward(ctx, grad_output):
if ctx.is_empty:
return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, None, None, None, None
req_gradA, req_gradB, _, _, _, _, _ = ctx.needs_input_grad
A, B = ctx.tensors
grad_A, grad_B = None, None
# TODO: Fix blocksize to be output_dim
cgrad_out, state = F.quantize(grad_output.float(), code=ctx.bw_code)
fp8out = F.dequantize(cgrad_out, state).to(grad_output.dtype)
# cgrad_output_2, state_2 = F.quantize(grad_output.float(), code=ctx.bw_code)
# fp8out_2 = F.dequantize(cgrad_output_2, state_2).to(grad_output.dtype)
# grad_output_reshape = grad_output.reshape(-1, grad_output.shape[-1]).contiguous()
# fp8grad_transpose, stategrad_transpose = F.vectorwise_quant(grad_output_reshape, dim=0, quant_type='vector')
# fp8out_transpose = (fp8grad_transpose / 7) * stategrad_transpose
# fp8out_transpose = fp8out_transpose.view(grad_output.shape[0], grad_output.shape[1], grad_output.shape[2])
# not supported by PyTorch. TODO: create work-around
if req_gradA:
grad_A = torch.matmul(fp8out, B.t().to(fp8out.dtype)).to(A.dtype)
if req_gradB:
if len(A.shape) == 3:
At = A.transpose(2, 1).contiguous()
else:
At = A.transpose(1, 0).contiguous()
cA, state = F.quantize(At.float(), code=ctx.fw_code)
fp8At = F.dequantize(cA, state).to(A.dtype)
grad_B = torch.matmul(fp8At.to(fp8out.dtype), fp8out).to(B.dtype)
return grad_A, grad_B, None, None, None, None, None
class SwitchBackBnb(torch.autograd.Function):
@staticmethod
def forward(ctx, A, B, out=None, bias=None, state=MatmulLtState()):
# default to 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
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. Quantize A
# 2. Quantize B
# 3. Matmul
# 4. Mixed-precision decomposition matmul
# 5. Save state
formatB = state.formatB
input_shape = A.shape
if state.outlier_pool is None:
state.outlier_pool = GlobalOutlierPooler.get_instance()
# Cast A to fp16
if A.dtype != torch.float16:
warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization")
# 1. Quantize A
if len(A.shape) == 3:
A = A.view(-1, A.shape[-1]).contiguous()
CA, CAt, SCA, SCAt, coo_tensorA = F.double_quant(
A.to(torch.float16), threshold=state.threshold
)
if state.threshold > 0.0 and coo_tensorA is not None:
if state.has_fp16_weights:
idx = torch.unique(coo_tensorA.colidx).long()
CA[:, idx] = 0
CAt[:, idx] = 0
subA = A[:, idx]
state.subB = B[:, idx].t().contiguous()
state.idx = idx
else:
if state.CxB is None:
# B in in 8-bit row-major, we can transform it back to 16-bit to extract outlier dimensions
# we also need to convert it to the turing/ampere format
state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
else:
#print('A shape', A.shape)
if not state.has_fp16_weights and state.CxB is None:
state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
subA = None
# 2. Quantize B
if state.has_fp16_weights:
#print('B shape', B.shape)
has_grad = True if (getattr(B, "grad", None) is not None) else False
is_transposed = not B.is_contiguous() and B.shape[0] == B.stride(1)
if is_transposed:
B = B.contiguous()
if (state.is_training and not has_grad) or state.CxB is None:
state.reset_grads()
(
CB,
state.CBt,
state.SCB,
state.SCBt,
coo_tensorB,
) = F.double_quant(B.to(torch.float16))
state.CxB, state.SB = F.transform(CB, to_order=formatB)
else:
has_grad = False
if coo_tensorA is not None and not state.has_fp16_weights:
# extract outliers
outlier_idx = torch.unique(coo_tensorA.colidx)
state.idx = outlier_idx
# state.outlier_pool.add_outliers(outlier_idx, A.shape[-1])
# if state.use_pool and state.outlier_pool.model_dim == A.shape[-1]:
# # do not use pool for 2nd FFN layer
# state.idx = state.outlier_pool.get_current_outlier_idx().to(A.device)
# else:
# state.idx = outlier_idx
outliers = F.extract_outliers(state.CxB, state.SB, state.idx.int())
state.subB = (
(outliers * state.SCB.view(-1, 1) / 127.0)
.t()
.contiguous()
.to(A.dtype)
)
CA[:, state.idx.long()] = 0
CAt[:, state.idx.long()] = 0
subA = A[:, state.idx.long()]
shapeB = state.SB[0]
if len(input_shape) == 3:
output_shape = (input_shape[0], input_shape[1], shapeB[0])
else:
output_shape = (input_shape[0], shapeB[0])
# 3. Matmul
C32A, SA = F.transform(CA, "col32")
out32, Sout32 = F.igemmlt(C32A, state.CxB, SA, state.SB)
# we apply the fused bias here
if bias is None or bias.dtype == torch.float16:
output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=bias)
output = output.to(A.dtype)
else: # apply bias separately
output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=None)
output = output.to(A.dtype).add_(bias)
# 4. Mixed-precision decomposition matmul
if coo_tensorA is not None and subA is not None:
output += torch.matmul(subA, state.subB)
# 5. Save state
ctx.state = state
ctx.formatB = formatB
ctx.grad_shape = input_shape
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 = (CAt, subA, A)
ctx.tensor_states = (SCAt, state.idx)
else:
ctx.tensors = [None, None, None]
ctx.tensor_states = (None, None)
ctx.save_for_backward(None, None)
clone_func = torch.clone if len(output_shape) == 3 else lambda x : x
return clone_func(output.view(output_shape))
@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_gradB, _, req_gradBias, _ = ctx.needs_input_grad
CAt, subA, A = ctx.tensors
SCAt, idx = ctx.tensor_states
formatB = ctx.formatB
state = ctx.state
grad_A = grad_B = grad_bias = None
if req_gradBias:
# compute grad_bias first before changing grad_output dtype
grad_bias = grad_output.sum(0, dtype=ctx.dtype_bias)
# Cast grad_output to fp16
if len(grad_output.shape) == 3:
grad_output = grad_output.reshape(
-1, grad_output.shape[-1]
).contiguous()
Cgrad, Cgradt, SCgrad, SCgradt, coo_tensor = F.double_quant(grad_output.to(torch.float16))
if req_gradB:
# print('back A shape', A.shape)
# print('grad output t shape', grad_output.t().shape)
grad_B = torch.matmul(grad_output.t(), A)
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
)
# print('back B shape', state.CxBt.shape)
# print('back grad shape', C32grad.shape)
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. / 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 matrix for backward')
return grad_A, grad_B, None, grad_bias, None
def get_block_sizes(input_matrix, weight_matrix):
input_features = input_matrix.shape[-1]
output_features = (weight_matrix.shape[0] if weight_matrix.shape[1] == input_features else weight_matrix.shape[1])
array = [4096, 2048, 1024, 512, 256, 128, 64, 0]
bsz, bsz2 = 1024, 1024
for i, k in enumerate(array):
if input_features > array[i + 1]:
bsz = k
break
for i, k in enumerate(array):
if output_features > array[i + 1]:
bsz2 = k
break
return bsz, bsz2
def matmul_fp8_global(A: tensor, B: tensor, fw_code: tensor, bw_code: tensor, out: tensor = None, bsz : int = -1, bsz2 : int = -1):
if bsz == -1 or bsz2 == -1: bsz, bsz2 = get_block_sizes(A, B)
return MatMulFP8Global.apply(A, B, out, fw_code, bw_code, bsz, bsz2)
def matmul_fp8_mixed(A: tensor, B: tensor, fw_code: tensor, bw_code: tensor, out: tensor = None, bsz : int = -1, bsz2 : int = -1):
if bsz == -1 or bsz2 == -1: bsz, bsz2 = get_block_sizes(A, B)
return MatMulFP8Mixed.apply(A, B, out, fw_code, bw_code, bsz, bsz2)
def switchback_bnb(
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 SwitchBackBnb.apply(A, B, out, bias, state)
bitsandbytes/research/nn/__init__.py 0 → 100644
View file @ a24aae30
from .modules import LinearFP8Mixed, LinearFP8Global
bitsandbytes/research/nn/modules.py 0 → 100644
View file @ a24aae30
from typing import Optional, TypeVar, Union, overload
import torch
import torch.nn.functional as F
from torch import Tensor, device, dtype, nn
import bitsandbytes as bnb
from bitsandbytes.optim import GlobalOptimManager
from bitsandbytes.utils import OutlierTracer, find_outlier_dims
T = TypeVar("T", bound="torch.nn.Module")
class LinearFP8Mixed(nn.Linear):
def __init__(self, input_features, output_features, bias=True):
super().__init__(input_features, output_features, bias)
self.bw_code = None
self.fw_code = None
array = [4096, 2048, 1024, 512, 256, 128, 64, 0]
for i, k in enumerate(array):
if input_features > array[i + 1]:
self.bsz = k
break
for i, k in enumerate(array):
if output_features > array[i + 1]:
self.bsz2 = k
break
def forward(self, x: torch.Tensor):
if self.fw_code is None:
self.bw_code = bnb.functional.create_fp8_map(True, 5, 2, 8).to(x.device)
self.fw_code = bnb.functional.create_fp8_map(True, 4, 3, 8).to(x.device)
out = bnb.research.matmul_fp8_mixed(x, self.weight.t(), fw_code=self.fw_code, bw_code=self.bw_code, bsz=self.bsz, bsz2=self.bsz2)
if self.bias is not None:
out += self.bias
return out
class LinearFP8Global(nn.Linear):
def __init__(self, input_features, output_features, bias=True):
super().__init__(input_features, output_features, bias)
self.bw_code = None
self.fw_code = None
array = [4096, 2048, 1024, 512, 256, 128, 64, 0]
for i, k in enumerate(array):
if input_features > array[i + 1]:
self.bsz = k
break
for i, k in enumerate(array):
if output_features > array[i + 1]:
self.bsz2 = k
break
def forward(self, x: torch.Tensor):
if self.fw_code is None:
self.bw_code = bnb.functional.create_fp8_map(True, 5, 2, 8).to(x.device)
self.fw_code = bnb.functional.create_fp8_map(True, 4, 3, 8).to(x.device)
out = bnb.matmul_fp8_global(x, self.weight.t(), fw_code=self.fw_code, bw_code=self.bw_code, bsz=self.bsz, bsz2=self.bsz2)
if self.bias is not None:
out += self.bias
return out
bitsandbytes/triton/dequantize_rowwise.py 0 → 100644
View file @ a24aae30
import math
import torch
import time
from bitsandbytes.triton.triton_utils import is_triton_available
if not is_triton_available():
def dequantize_rowwise(x: torch.Tensor, state_x: torch.Tensor): return None
else:
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
# rowwise quantize
# TODO: autotune this better.
@triton.autotune(
configs=[
triton.Config({}, num_stages=1, num_warps=8),
triton.Config({}, num_stages=2, num_warps=8),
triton.Config({}, num_stages=4, num_warps=8),
triton.Config({}, num_stages=8, num_warps=8),
triton.Config({}, num_stages=1),
triton.Config({}, num_stages=2),
triton.Config({}, num_stages=4),
triton.Config({}, num_stages=8),
triton.Config({}, num_warps=1),
triton.Config({}, num_warps=2),
triton.Config({}, num_warps=4),
triton.Config({}, num_warps=8),
],
key=['n_elements']
)
@triton.jit
def _dequantize_rowwise(
x_ptr,
state_x,
output_ptr,
inv_127,
n_elements,
BLOCK_SIZE: tl.constexpr,
P2: tl.constexpr,
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
arange = tl.arange(0, P2)
offsets = block_start + arange
row_mask = arange < BLOCK_SIZE
x = tl.load(x_ptr + offsets, mask=row_mask)
max_val = tl.load(state_x + pid)
output = max_val * x * inv_127
tl.store(output_ptr + offsets, output, mask=row_mask)
def dequantize_rowwise(x: torch.Tensor, state_x: torch.Tensor):
output = torch.empty(*x.shape, device=x.device, dtype=torch.float16)
P2 = int(2 ** (math.ceil(math.log2(x.shape[1]))))
assert x.is_cuda and output.is_cuda
n_elements = output.numel()
grid = lambda meta: (x.shape[0],)
_dequantize_rowwise[grid](x, state_x, output, 1./127, n_elements, BLOCK_SIZE=x.shape[1], P2=P2)
return output
bitsandbytes/triton/int8_matmul_mixed_dequanitze.py 0 → 100644
View file @ a24aae30
import torch
from bitsandbytes.triton.triton_utils import is_triton_available
if not is_triton_available():
def int8_matmul_mixed_dequanitze(a, b, state_x, state_w, bias): return None
else:
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
# This is a matmul kernel based on triton.ops.matmul
# It is modified to support rowwise quantized input and global quantized weight
# It's purpose is fused matmul then dequantize
# It does support bias.
def init_to_zero(name):
return lambda nargs: nargs[name].zero_()
def get_configs_io_bound():
configs = []
for num_stages in [2, 3, 4, 5, 6]:
for block_m in [16, 32]:
for block_k in [32, 64]:
for block_n in [32, 64, 128, 256]:
num_warps = 2 if block_n <= 64 else 4
configs.append(
triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': 1},
num_stages=num_stages, num_warps=num_warps))
# split_k
for split_k in [2, 4, 8, 16]:
configs.append(triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': split_k},
num_stages=num_stages, num_warps=num_warps, pre_hook=init_to_zero('C')))
return configs
@triton.autotune(
configs=[
# basic configs for compute-bound matmuls
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
# good for int8
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
] + get_configs_io_bound(),
key=['M', 'N', 'K'],
prune_configs_by={
'early_config_prune': early_config_prune,
'perf_model': estimate_matmul_time,
'top_k': 10
},
)
@triton.heuristics({
'EVEN_K': lambda args: args['K'] % (args['BLOCK_K'] * args['SPLIT_K']) == 0,
})
@triton.jit
def _int8_matmul_mixed_dequantize(A, B, C, bias, state_x_ptr, state_w_ptr, M, N, K, divfactor: tl.constexpr, has_bias : tl.constexpr,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
GROUP_M: tl.constexpr, SPLIT_K: tl.constexpr, EVEN_K: tl.constexpr,
ACC_TYPE: tl.constexpr
):
# matrix multiplication
pid = tl.program_id(0)
pid_z = tl.program_id(1)
grid_m = tl.cdiv(M, BLOCK_M)
grid_n = tl.cdiv(N, BLOCK_N)
# re-order program ID for better L2 performance
width = GROUP_M * grid_n
group_id = pid // width
group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
pid_m = group_id * GROUP_M + (pid % group_size)
pid_n = (pid % width) // (group_size)
# do matrix multiplication
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
rk = pid_z * BLOCK_K + tl.arange(0, BLOCK_K)
# pointers
A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak)
B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn)
# rematerialize rm and rn to save registers
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
w_factor = tl.load(state_w_ptr)
x_factor = tl.load(state_x_ptr + ram)[:, None]
# acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)
acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.int32)
for k in range(0, tl.cdiv(K, BLOCK_K * SPLIT_K)):
if EVEN_K:
a = tl.load(A)
b = tl.load(B)
else:
k_remaining = K - k * (BLOCK_K * SPLIT_K)
a = tl.load(A, mask=rk[None, :] < k_remaining, other=0.)
b = tl.load(B, mask=rk[:, None] < k_remaining, other=0.)
acc += tl.dot(a, b)
A += BLOCK_K * SPLIT_K * stride_ak
B += BLOCK_K * SPLIT_K * stride_bk
acc = (w_factor * (x_factor * (acc * divfactor)))
acc = acc.to(C.dtype.element_ty)
# conditionally add bias
if has_bias:
bias = tl.load(bias + rn).to(C.dtype.element_ty)
acc = acc + bias[None, :]
C = C + (rm[:, None] * stride_cm + rn[None, :] * stride_cn)
mask = (rm < M)[:, None] & (rn < N)[None, :]
# handles write-back with reduction-splitting
if SPLIT_K == 1:
tl.store(C, acc, mask=mask)
else:
tl.atomic_add(C, acc, mask=mask)
def int8_matmul_mixed_dequanitze(a, b, state_x, state_w, bias):
device = a.device
divfactor = 1. / (127. * 127.)
has_bias = 0 if bias is None else 1
# handle non-contiguous inputs if necessary
if a.stride(0) > 1 and a.stride(1) > 1:
a = a.contiguous()
if b.stride(0) > 1 and b.stride(1) > 1:
b = b.contiguous()
# checks constraints
assert a.shape[1] == b.shape[0], "incompatible dimensions"
M, K = a.shape
_, N = b.shape
# allocates output
c = torch.empty((M, N), device=device, dtype=torch.float16)
# accumulator types
ACC_TYPE = tl.float32 #if a.dtype in [torch.float16, torch.bfloat16, torch.float32] else tl.int32
# launch int8_matmul_mixed_dequantize kernel
grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']), META['SPLIT_K'])
_int8_matmul_mixed_dequantize[grid](a, b, c, bias, state_x, state_w, M, N, K, divfactor, has_bias,
a.stride(0), a.stride(1),
b.stride(0), b.stride(1),
c.stride(0), c.stride(1),
GROUP_M=8, ACC_TYPE=ACC_TYPE)
return c
bitsandbytes/triton/int8_matmul_rowwise_dequantize.py 0 → 100644
View file @ a24aae30
import torch
from bitsandbytes.triton.triton_utils import is_triton_available
if not is_triton_available():
def int8_matmul_rowwise_dequantize(a, b, state_x, state_w, bias): return None
else:
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
# This is a matmul kernel based on triton.ops.matmul
# It is modified to support rowwise quantized input and columnwise quantized weight
# It's purpose is fused matmul then dequantize
# It does support bias.
def init_to_zero(name):
return lambda nargs: nargs[name].zero_()
def get_configs_io_bound():
configs = []
for num_stages in [2, 3, 4, 5, 6]:
for block_m in [16, 32]:
for block_k in [32, 64]:
for block_n in [32, 64, 128, 256]:
num_warps = 2 if block_n <= 64 else 4
configs.append(
triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': 1},
num_stages=num_stages, num_warps=num_warps))
# split_k
for split_k in [2, 4, 8, 16]:
configs.append(triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': split_k},
num_stages=num_stages, num_warps=num_warps, pre_hook=init_to_zero('C')))
return configs
@triton.autotune(
configs=[
# basic configs for compute-bound matmuls
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
# good for int8
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
] + get_configs_io_bound(),
key=['M', 'N', 'K'],
prune_configs_by={
'early_config_prune': early_config_prune,
'perf_model': estimate_matmul_time,
'top_k': 10
},
)
@triton.heuristics({
'EVEN_K': lambda args: args['K'] % (args['BLOCK_K'] * args['SPLIT_K']) == 0,
})
@triton.jit
def _int8_matmul_rowwise_dequantize(A, B, C, bias, state_x_ptr, state_w_ptr, M, N, K, divfactor, has_bias : tl.constexpr,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
GROUP_M: tl.constexpr, SPLIT_K: tl.constexpr, EVEN_K: tl.constexpr,
ACC_TYPE: tl.constexpr
):
# matrix multiplication
pid = tl.program_id(0)
pid_z = tl.program_id(1)
grid_m = tl.cdiv(M, BLOCK_M)
grid_n = tl.cdiv(N, BLOCK_N)
# re-order program ID for better L2 performance
width = GROUP_M * grid_n
group_id = pid // width
group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
pid_m = group_id * GROUP_M + (pid % group_size)
pid_n = (pid % width) // (group_size)
# do matrix multiplication
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
rk = pid_z * BLOCK_K + tl.arange(0, BLOCK_K)
# pointers
A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak)
B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn)
# rematerialize rm and rn to save registers
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
w_factor = tl.load(state_w_ptr + rbn)[None, :]
x_factor = tl.load(state_x_ptr + ram)[:, None]
# acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)
acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.int32)
for k in range(0, tl.cdiv(K, BLOCK_K * SPLIT_K)):
if EVEN_K:
a = tl.load(A)
b = tl.load(B)
else:
k_remaining = K - k * (BLOCK_K * SPLIT_K)
a = tl.load(A, mask=rk[None, :] < k_remaining, other=0.)
b = tl.load(B, mask=rk[:, None] < k_remaining, other=0.)
acc += tl.dot(a, b)
A += BLOCK_K * SPLIT_K * stride_ak
B += BLOCK_K * SPLIT_K * stride_bk
acc = (w_factor * (x_factor * (acc * divfactor)))
acc = acc.to(C.dtype.element_ty)
if has_bias:
bias = tl.load(bias + rn).to(C.dtype.element_ty)
acc = acc + bias[None, :]
C = C + (rm[:, None] * stride_cm + rn[None, :] * stride_cn)
mask = (rm < M)[:, None] & (rn < N)[None, :]
# handles write-back with reduction-splitting
if SPLIT_K == 1:
tl.store(C, acc, mask=mask)
else:
tl.atomic_add(C, acc, mask=mask)
def int8_matmul_rowwise_dequantize(a, b, state_x, state_w, bias):
divfactor = 1. / (127. * 127.)
has_bias = 0 if bias is None else 1
device = a.device
# handle non-contiguous inputs if necessary
if a.stride(0) > 1 and a.stride(1) > 1:
a = a.contiguous()
if b.stride(0) > 1 and b.stride(1) > 1:
b = b.contiguous()
# checks constraints
assert a.shape[1] == b.shape[0], "incompatible dimensions"
M, K = a.shape
_, N = b.shape
# allocates output
c = torch.empty((M, N), device=device, dtype=torch.float16)
# accumulator types
ACC_TYPE = tl.float32 #if a.dtype in [torch.float16, torch.bfloat16, torch.float32] else tl.int32
# launch int8_matmul_rowwise_dequantize kernel
grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']), META['SPLIT_K'])
_int8_matmul_rowwise_dequantize[grid](a, b, c, bias, state_x, state_w, M, N, K, divfactor, has_bias,
a.stride(0), a.stride(1),
b.stride(0), b.stride(1),
c.stride(0), c.stride(1),
GROUP_M=8, ACC_TYPE=ACC_TYPE)
return c
bitsandbytes/triton/quantize_columnwise_and_transpose.py 0 → 100644
View file @ a24aae30
import math
import torch
import time
from bitsandbytes.triton.triton_utils import is_triton_available
if not is_triton_available():
def quantize_columnwise_and_transpose(x: torch.Tensor): return None
else:
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
# This kernel does fused columnwise quantization and transpose.
# TODO: autotune this better.
@triton.autotune(
configs=[
triton.Config({}, num_stages=1),
triton.Config({}, num_stages=2),
triton.Config({}, num_stages=4),
triton.Config({}, num_stages=8),
triton.Config({}, num_stages=16),
triton.Config({}, num_stages=1, num_warps=8),
triton.Config({}, num_stages=2, num_warps=8),
triton.Config({}, num_stages=4, num_warps=8),
triton.Config({}, num_stages=8, num_warps=8),
triton.Config({}, num_stages=16, num_warps=8),
triton.Config({}, num_warps=1),
triton.Config({}, num_warps=2),
triton.Config({}, num_warps=4),
triton.Config({}, num_warps=8),
],
key=['n_elements']
)
@triton.jit
def _quantize_columnwise_and_transpose(
x_ptr,
output_ptr,
output_maxs,
n_elements,
M : tl.constexpr, N : tl.constexpr,
BLOCK_SIZE: tl.constexpr,
P2: tl.constexpr,
):
pid = tl.program_id(axis=0)
block_start = pid
p2_arange = tl.arange(0, P2)
p2_arange_mask = p2_arange < M
arange = p2_arange * N
offsets = block_start + arange
x = tl.load(x_ptr + offsets, mask=p2_arange_mask)
abs_x = tl.abs(x)
max_val = tl.max(tl.where(p2_arange_mask, abs_x, 0), axis=0)
output = tl.libdevice.llrint(127. * (x / max_val))
new_start = pid * M
new_offsets = new_start + p2_arange
tl.store(output_ptr + new_offsets, output, mask=p2_arange_mask)
tl.store(output_maxs + pid, max_val)
def quantize_columnwise_and_transpose(x: torch.Tensor):
M, N = x.shape
output = torch.empty(N, M, device=x.device, dtype=torch.int8)
output_maxs = torch.empty(x.shape[1], device=x.device, dtype=torch.float16)
P2 = int(2 ** (math.ceil(math.log2(M))))
assert x.is_cuda and output.is_cuda
n_elements = output.numel()
grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
_quantize_columnwise_and_transpose[grid](x, output, output_maxs, n_elements, M, N, BLOCK_SIZE=M, P2=P2)
return output, output_maxs
bitsandbytes/triton/quantize_global.py 0 → 100644
View file @ a24aae30
import math
import torch
import time
from bitsandbytes.triton.triton_utils import is_triton_available
if not is_triton_available():
def quantize_global_transpose(input): return None
def quantize_global(x: torch.Tensor): return None
else:
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
# global quantize
@triton.autotune(
configs=[
triton.Config({'BLOCK_SIZE': 1024,}, num_warps=4),
triton.Config({'BLOCK_SIZE': 2048,}, num_stages=1),
],
key=['n_elements']
)
@triton.jit
def _quantize_global(
x_ptr,
absmax_inv_ptr,
output_ptr,
n_elements,
BLOCK_SIZE: tl.constexpr,
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
x = tl.load(x_ptr + offsets, mask=mask)
absmax_inv = tl.load(absmax_inv_ptr)
output = tl.libdevice.llrint(127. * (x * absmax_inv))
tl.store(output_ptr + offsets, output, mask=mask)
def quantize_global(x: torch.Tensor):
absmax = x.abs().max().unsqueeze(0)
absmax_inv = 1./ absmax
output = torch.empty(*x.shape, device='cuda', dtype=torch.int8)
assert x.is_cuda and output.is_cuda
n_elements = output.numel()
grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
_quantize_global[grid](x, absmax_inv, output, n_elements)
return output, absmax
# global quantize and transpose
@triton.autotune(
configs=[
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'GROUP_M': 8}, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'GROUP_M': 8}, num_warps=4),
# ...
],
key=['M', 'N']
)
@triton.jit
def _quantize_global_transpose(A, absmax_inv_ptr, B, stride_am, stride_an, stride_bn, stride_bm, M, N,
BLOCK_M : tl.constexpr,
BLOCK_N : tl.constexpr,
GROUP_M : tl.constexpr):
pid = tl.program_id(0)
grid_m = (M + BLOCK_M - 1) // BLOCK_M
grid_n = (N + BLOCK_N - 1) // BLOCK_N
width = GROUP_M * grid_n
group_id = pid // width
group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
pid_m = group_id * GROUP_M + (pid % group_size)
pid_n = (pid % width) // group_size
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
A = A + (rm[:, None] * stride_am + rn[None, :] * stride_an)
mask = (rm < M)[:, None] & (rn < N)[None, :]
a = tl.load(A, mask=mask)
absmax_inv = tl.load(absmax_inv_ptr)
# rematerialize to save registers
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
B = B + (rm[:, None] * stride_bm + rn[None, :] * stride_bn)
mask = (rm < M)[:, None] & (rn < N)[None, :]
output = tl.libdevice.llrint(127. * (a * absmax_inv))
tl.store(B, output, mask=mask)
def quantize_global_transpose(input):
absmax = input.abs().max().unsqueeze(0)
absmax_inv = 1./ absmax
M, N = input.shape
out = torch.empty(N, M, device='cuda', dtype=torch.int8)
assert out.size(0) == N and out.size(1) == M
assert input.stride(0) == 1 or input.stride(1) == 1
assert out.stride(0) == 1 or out.stride(1) == 1
grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']),)
_quantize_global_transpose[grid](input, absmax_inv, out, input.stride(0), input.stride(1), out.stride(0), out.stride(1), M, N)
return out, absmax
bitsandbytes/triton/quantize_rowwise.py 0 → 100644
View file @ a24aae30
import math
import torch
import time
from bitsandbytes.triton.triton_utils import is_triton_available
if not is_triton_available():
def quantize_rowwise(x: torch.Tensor): return None
else:
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
# rowwise quantize
# TODO: autotune this better.
@triton.autotune(
configs=[
triton.Config({}, num_stages=1, num_warps=8),
triton.Config({}, num_stages=2, num_warps=8),
triton.Config({}, num_stages=4, num_warps=8),
triton.Config({}, num_stages=8, num_warps=8),
triton.Config({}, num_stages=1),
triton.Config({}, num_stages=2),
triton.Config({}, num_stages=4),
triton.Config({}, num_stages=8),
triton.Config({}, num_warps=1),
triton.Config({}, num_warps=2),
triton.Config({}, num_warps=4),
triton.Config({}, num_warps=8),
],
key=['n_elements']
)
@triton.jit
def _quantize_rowwise(
x_ptr,
output_ptr,
output_maxs,
n_elements,
BLOCK_SIZE: tl.constexpr,
P2: tl.constexpr,
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
arange = tl.arange(0, P2)
offsets = block_start + arange
row_mask = arange < BLOCK_SIZE
x = tl.load(x_ptr + offsets, mask=row_mask)
abs_x = tl.abs(x)
max_val = tl.max(tl.where(row_mask, abs_x, 0), axis=0)
output = tl.libdevice.llrint(127. * (x / max_val))
tl.store(output_ptr + offsets, output, mask=row_mask)
tl.store(output_maxs + pid, max_val)
def quantize_rowwise(x: torch.Tensor):
output = torch.empty(*x.shape, device=x.device, dtype=torch.int8)
output_maxs = torch.empty(x.shape[0], device=x.device, dtype=torch.float16)
P2 = int(2 ** (math.ceil(math.log2(x.shape[1]))))
assert x.is_cuda and output.is_cuda
n_elements = output.numel()
grid = lambda meta: (x.shape[0],)
_quantize_rowwise[grid](x, output, output_maxs, n_elements, BLOCK_SIZE=x.shape[1], P2=P2)
return output, output_maxs
bitsandbytes/triton/triton_utils.py 0 → 100644
View file @ a24aae30
import importlib
def is_triton_available():
return importlib.util.find_spec("triton") is not None
bitsandbytes/utils.py
View file @ a24aae30
import shlex
import subprocess
import torch
from typing import Tuple
def outlier_hook(module, input):
assert isinstance(module, torch.nn.Linear)
tracer = OutlierTracer.get_instance()
hvalue = tracer.get_hvalue(module.weight)
if hvalue not in tracer.hvalue2outlier_idx:
outlier_idx = find_outlier_dims(module.weight)
tracer.outliers.append(outlier_idx)
tracer.hvalues.append(hvalue)
if len(tracer.outliers) > 1:
# assign the current layer the outlier idx found from the weight
# of the previous linear layer
if tracer.outliers[-1].numel() > 0:
assert tracer.outliers[-1].max() < module.weight.shape[1]
tracer.hvalue2outlier_idx[hvalue] = tracer.outliers[-1]
else:
# first layer, we cannot use the weight for outlier detection
# we follow a mixed approach:
# (1) zscore test of std of hidden dimension
# (2) magnitude > 6 test
merged = input[0].view(-1, input[0].shape[-1])
# (1) zscore test of std of hidden dimension
outlier_idx = find_outlier_dims(merged, reduction_dim=1, zscore=3)
# (2) magnitude > 6 test
dims = (torch.abs(input[0])> 6).sum(dim=list(range(len(input[0].shape)-1)))
outlier_idx2 = torch.where(dims > 0)[0]
outlier_idx = torch.cat([outlier_idx, outlier_idx2]).unique()
tracer.hvalue2outlier_idx[hvalue] = outlier_idx
else:
for hook in tracer.hooks:
hook.remove()
class OutlierTracer(object):
_instance = None
def __init__(self):
raise RuntimeError("Call get_instance() instead")
def initialize(self, model):
self.last_w = None
self.current_outlier_dims = None
self.hvalues = []
self.outliers = []
self.hvalue2outlier_idx = {}
self.initialized = True
self.hooks = []
for n, m in model.named_modules():
if isinstance(m, torch.nn.Linear):
self.hooks.append(m.register_forward_pre_hook(outlier_hook))
def is_initialized(self):
return getattr(self, 'initialized', False)
def get_hvalue(self, weight):
return weight.data.storage().data_ptr()
def get_outliers(self, weight):
if not self.is_initialized():
print('Outlier tracer is not initialized...')
return None
hvalue = self.get_hvalue(weight)
if hvalue in self.hvalue2outlier_idx:
return self.hvalue2outlier_idx[hvalue]
else:
return None
@classmethod
def get_instance(cls):
if cls._instance is None:
cls._instance = cls.__new__(cls)
return cls._instance
def find_outlier_dims(weight, reduction_dim=0, zscore=4.0, topk=None, rdm=False):
if rdm:
return torch.randint(0, weight.shape[1], size=(topk,), device=weight.device).long()
m = weight.mean(reduction_dim)
mm = m.mean()
mstd = m.std()
zm = (m-mm)/mstd
std = weight.std(reduction_dim)
stdm = std.mean()
stdstd = std.std()
zstd = (std-stdm)/stdstd
if topk is not None:
val, idx = torch.topk(std.abs(), k=topk, dim=0)
else:
idx = torch.where(zstd > zscore)[0]
return idx
def replace_linear(model, linear_replacement, skip_modules=["lm_head"], copy_weights=False, post_processing_function=None):
"""
Replace linear modules with a new Linear module.
Parameters:
model (`torch.nn.Module`):
Input model or `torch.nn.Module` as the function is run recursively.
linear_replacement (`torch.nn.Module`):
The linear module that replaces the old one. Only expects standard arguments.
If other arguments need to be passed, use a lambda.
skip_modules (`List[str]`, *optional*, defaults to `lm_head`):
List of modules names not to convert. Defaults to `lm_head`.
copy_weights (`bool`):
Copy the weights from the old linear module to the new one
post_processing_fun_name (`str`):
A function name of the replacement linear class that is called
after processing.
"""
for name, module in model.named_children():
if len(list(module.children())) > 0:
replace_linear(module, linear_replacement, skip_modules, copy_weights, post_processing_function)
if isinstance(module, torch.nn.Linear) and name not in skip_modules:
old_module = model._modules[name]
model._modules[name] = linear_replacement(
module.in_features,
module.out_features,
module.bias is not None,
)
if copy_weights:
model._modules[name].weight = old_module.weight
model._modules[name].bias = old_module.bias
if post_processing_function is not None:
func = getattr(module, post_processing_function, None)
if func is not None: func(module)
return model
def execute_and_return(command_string: str) -> Tuple[str, str]:
def _decode(subprocess_err_out_tuple):
......@@ -21,3 +157,43 @@ def execute_and_return(command_string: str) -> Tuple[str, str]:
std_out, std_err = execute_and_return_decoded_std_streams(command_string)
return std_out, std_err
def replace_linear(model, linear_replacement, skip_modules=["lm_head"], copy_weights=False, post_processing_function=None):
"""
Replace linear modules with a new Linear module.
Parameters:
model (`torch.nn.Module`):
Input model or `torch.nn.Module` as the function is run recursively.
linear_replacement (`torch.nn.Module`):
The linear module that replaces the old one. Only expects standard arguments.
If other arguments need to be passed, use a lambda.
skip_modules (`List[str]`, *optional*, defaults to `lm_head`):
List of modules names not to convert. Defaults to `lm_head`.
copy_weights (`bool`):
Copy the weights from the old linear module to the new one
post_processing_fun_name (`str`):
A function name of the replacement linear class that is called
after processing.
"""
for name, module in model.named_children():
if len(list(module.children())) > 0:
replace_linear(module, linear_replacement, skip_modules, copy_weights, post_processing_function)
if isinstance(module, torch.nn.Linear) and name not in skip_modules:
old_module = model._modules[name]
model._modules[name] = linear_replacement(
module.in_features,
module.out_features,
module.bias is not None,
)
if copy_weights:
model._modules[name].weight = old_module.weight
model._modules[name].bias = old_module.bias
if post_processing_function is not None:
func = getattr(module, post_processing_function, None)
if func is not None: func(module)
return model
compile_from_source.md
View file @ a24aae30
......@@ -33,3 +33,8 @@ You can set `CUDA_HOME` to `/usr/local/cuda-11.7`. For example, you might be abl
If you have problems compiling the library with these instructions from source, please open an issue.
## Compilation with Kepler
Since 0.39.1 bitsandbytes installed via pip no longer provides Kepler binaries and these need to be compiled from source. Follow the steps above and instead of `cuda11x_nomatmul` etc use `cuda11x_nomatmul_kepler`
csrc/kernels.cu
View file @ a24aae30
......@@ -12,12 +12,17 @@
#include <cub/block/block_reduce.cuh>
#include <cub/cub.cuh>
#include <math_constants.h>
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <mma.h>
#define HLF_MAX 65504
#define TH 1024
#define NUM 4
#define NUM_BLOCK 4096
// source: https://stackoverflow.com/questions/17399119/how-do-i-use-atomicmax-on-floating-point-values-in-cuda
__device__ float atomicMax(float* address, float val) {
int* address_as_i = reinterpret_cast<int*>(address);
......@@ -43,11 +48,289 @@ __device__ float atomicMin(float* address, float val) {
return __int_as_float(old);
}
__device__ float dDequantizeFP4(unsigned char val, float absmax)
{
float sign = (val & 0b1000) == 8 ? -1.0f : 1.0f;
if((val & 0b0110) == 0)
{
// subnormal
if((val & 0b0001) == 0)
return 0.0f;
else
return sign*0.0625f*absmax;
}
else
{
// normal
float exponent = ((val & 0b0100) == 4 ? 2.0f : 8.0f) + ((val & 0b0010) == 2 ? 0.0f : 2.0f);
float fraction = (val & 0b0001) == 1 ? 1.5f : 1.0f;
return sign*exponent*fraction*absmax;
}
}
__device__ float d2DequantizeFP4(unsigned char val)
{
float sign = (val & 0b1000) == 8 ? -1.0f : 1.0f;
if((val & 0b0110) == 0)
{
// subnormal
if((val & 0b0001) == 0)
return 0.0f;
else
return sign*0.0625f;
}
else
{
// normal
float exponent = ((val & 0b0100) == 4 ? 2.0f : 8.0f) + ((val & 0b0010) == 2 ? 0.0f : 2.0f);
float fraction = (val & 0b0001) == 1 ? 1.5f : 1.0f;
return sign*exponent*fraction;
}
}
__device__ float dDequantizeFP4Tree(unsigned char val, float absmax)
{
float sign = (val & 0b1000) == 8 ? -1.0f : 1.0f;
if((val & 0b0100) == 4) // 0
if((val & 0b0010) == 2) //01
if((val & 0b0001) == 1) // 111
return 0.25000000f*absmax*sign; // 1111
else
return 0.16666667f*absmax*sign; // 1110
else
if((val & 0b0001) == 1) // 110
return 0.50000000f*absmax*sign; // 1101
else
return 0.33333333f*absmax*sign; // 1100
else
if((val & 0b0010) == 2) //10
if((val & 0b0001) == 1) // 101
return 1.00000000f*absmax*sign; // 1011
else
return 0.66666667f*absmax*sign; // 1010
else
if((val & 0b0001) == 1) // 100
return 5.208333333e-03f*absmax*sign; // 1001
else
return 0.00000000f*absmax*sign; // 1000
}
__device__ unsigned char dQuantizeFP4(float x)
{
// FP4 with bias of 3
// first bit is a sign
// subnormals
// 0b000 = 0
// 0b001 = 0.0625
// 0b110 = 2
// 0b111 = 3
// 0b100 = 4
// 0b101 = 6
// 0b010 = 8
// 0b011 = 12
// we do a binary search
// the pivots are divided by 12 (the FP4 absmax)
// since we assum input data is in [-1.0, 1.0]
// !be careful here, its easy to make a mistake
// that is difficult to noice if you add an extra
// zero somewhere!
int sign = x < 0 ? 0b1000 : 0b0000;
x = fabsf(x);
if(x > 0.29166667f)
if( x > 0.583333f)
if( x > 0.8333333f)
return 0b0011+sign;
else
return 0b0010+sign;
else
if(x > 0.4166667f)
return 0b101+sign;
else
return 0b100+sign;
else
if(x > 0.0859375f)
if(x > 0.20833333f)
return 0b0111+sign;
else
return 0b0110+sign;
else
if(x > 0.00260417f)
return 0b0001+sign;
else
return 0b0000+sign;
}
__device__ half dhDequantizeNF4(unsigned char val)
{
// the values for this tree was generated by test_normal_map_tree
// in the file tests/test_functional.py
if((val & 0b1000) == 8)
if((val & 0b0100) == 4) // 1
if((val & 0b0010) == 2) // 11
if((val & 0b0001) == 1) // 111
return 1.0f;
else
return 0.7229568362236023f;
else
if((val & 0b0001) == 1) // 110
return 0.5626170039176941f;
else
return 0.44070982933044434f;
else
if((val & 0b0010) == 2) //10
if((val & 0b0001) == 1) // 101
return 0.33791524171829224f;
else
return 0.24611230194568634f;
else
if((val & 0b0001) == 1) // 100
return 0.16093020141124725f;
else
return 0.07958029955625534f;
else
if((val & 0b0100) == 4) // 0
if((val & 0b0010) == 2) //01
if((val & 0b0001) == 1) // 011
return 0.0f;
else
return -0.09105003625154495f;
else
if((val & 0b0001) == 1) // 010
return -0.18477343022823334f;
else
return -0.28444138169288635f;
else
if((val & 0b0010) == 2) //00
if((val & 0b0001) == 1) // 001
return -0.39491748809814453f;
else
return -0.5250730514526367f;
else
if((val & 0b0001) == 1) // 000
return -0.6961928009986877f;
else
return -1.0f;
}
__device__ float dDequantizeNF4(unsigned char val)
{
// the values for this tree was generated by test_normal_map_tree
// in the file tests/test_functional.py
if((val & 0b1000) == 8)
if((val & 0b0100) == 4) // 1
if((val & 0b0010) == 2) // 11
if((val & 0b0001) == 1) // 111
return 1.0f;
else
return 0.7229568362236023f;
else
if((val & 0b0001) == 1) // 110
return 0.5626170039176941f;
else
return 0.44070982933044434f;
else
if((val & 0b0010) == 2) //10
if((val & 0b0001) == 1) // 101
return 0.33791524171829224f;
else
return 0.24611230194568634f;
else
if((val & 0b0001) == 1) // 100
return 0.16093020141124725f;
else
return 0.07958029955625534f;
else
if((val & 0b0100) == 4) // 0
if((val & 0b0010) == 2) //01
if((val & 0b0001) == 1) // 011
return 0.0f;
else
return -0.09105003625154495f;
else
if((val & 0b0001) == 1) // 010
return -0.18477343022823334f;
else
return -0.28444138169288635f;
else
if((val & 0b0010) == 2) //00
if((val & 0b0001) == 1) // 001
return -0.39491748809814453f;
else
return -0.5250730514526367f;
else
if((val & 0b0001) == 1) // 000
return -0.6961928009986877f;
else
return -1.0f;
}
__device__ unsigned char dQuantizeNF4(float x)
{
// the values for this tree was generated by test_normal_map_tree
// in the file tests/test_functional.py
if(x > 0.03979014977812767f)
if(x > 0.3893125355243683f) // 1
if(x > 0.6427869200706482f) // 11
if(x > 0.8614784181118011f) // 111
return 0b1111;
else
return 0b1110;
else
if(x > 0.5016634166240692f) // 110
return 0b1101;
else
return 0b1100;
else
if(x > 0.2035212516784668f) // 10
if(x > 0.2920137718319893f) // 101
return 0b1011;
else
return 0b1010;
else
if(x > 0.1202552504837513f) // 100
return 0b1001;
else
return 0b1000;
else
if(x > -0.33967943489551544f) // 0
if(x > -0.13791173323988914f) // 01
if(x > -0.045525018125772476f) // 011
return 0b0111;
else
return 0b0110;
else
if(x > -0.23460740596055984f) // 010
return 0b0101;
else
return 0b0100;
else
if(x > -0.6106329262256622f) // 00
if(x > -0.4599952697753906f) // 001
return 0b0011;
else
return 0b0010;
else
if(x > -0.8480964004993439f) // 000
return 0b0001;
else
return 0b0000;
}
// sign function for lion
// taken from https://stackoverflow.com/a/4609795, but not sure if there's a proper way to do this in CUDA
template <typename T>
__device__ int sgn(T val) {
template <typename T> __device__ int sgn(T val)
{
return (T(0) < val) - (val < T(0));
}
......@@ -435,7 +718,7 @@ __global__ void kQuantize(float * code, float * __restrict__ const A, unsigned c
}
}
template<typename T, int BLOCK_SIZE, int NUM_PER_TH, int STOCHASTIC>
template<typename T, int BLOCK_SIZE, int NUM_PER_TH, int STOCHASTIC, int DATA_TYPE>
//__launch_bounds__(TH, 4)
__global__ void kQuantizeBlockwise(float * code, T * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n)
{
......@@ -445,13 +728,13 @@ __global__ void kQuantizeBlockwise(float * code, T * __restrict__ const A, float
T vals[NUM_PER_TH];
float rand_vals[NUM_PER_TH];
unsigned char qvals[NUM_PER_TH];
unsigned char qvals[(DATA_TYPE > 0) ? NUM_PER_TH/2 : NUM_PER_TH];
//float local_abs_max = -FLT_MAX;
float local_abs_max = 0.0f;
int local_rand_idx = 0;
typedef cub::BlockLoad<T, BLOCK_SIZE/NUM_PER_TH, NUM_PER_TH, cub::BLOCK_LOAD_WARP_TRANSPOSE> LoadT;
typedef cub::BlockStore<unsigned char, BLOCK_SIZE/NUM_PER_TH, NUM_PER_TH, cub::BLOCK_STORE_WARP_TRANSPOSE> StoreChar;
typedef cub::BlockStore<unsigned char, BLOCK_SIZE/NUM_PER_TH, (DATA_TYPE > 0) ? NUM_PER_TH/2 : NUM_PER_TH, cub::BLOCK_STORE_WARP_TRANSPOSE> StoreChar;
typedef cub::BlockReduce<float, BLOCK_SIZE/NUM_PER_TH> BlockReduce;
typedef cub::BlockLoad<float, BLOCK_SIZE/NUM_PER_TH, NUM_PER_TH, cub::BLOCK_LOAD_WARP_TRANSPOSE> LoadFloat;
......@@ -462,8 +745,9 @@ __global__ void kQuantizeBlockwise(float * code, T * __restrict__ const A, float
__shared__ float smem_code[256];
__shared__ float smem_absmax_value[1];
for(int i = threadIdx.x; i < 256; i+=blockDim.x)
smem_code[i] = code[i];
if(DATA_TYPE == General8bit)
for(int i = threadIdx.x; i < 256; i+=blockDim.x)
smem_code[i] = code[i];
for (unsigned int i = base_idx; i < n_full; i += gridDim.x*BLOCK_SIZE)
{
......@@ -503,62 +787,111 @@ __global__ void kQuantizeBlockwise(float * code, T * __restrict__ const A, float
LoadFloat(loadf).Load(&rand[local_rand_idx], rand_vals, BLOCK_SIZE, 0);
}
#pragma unroll NUM_PER_TH
for(int j = 0; j < NUM_PER_TH; j++)
unsigned char packed_4bit = 0;
switch(DATA_TYPE)
{
if(!STOCHASTIC)
qvals[j] = dQuantize<0>(smem_code, 0.0f, ((float)vals[j])*local_abs_max);
else
qvals[j] = dQuantize<1>(smem_code, rand_vals[j], ((float)vals[j])*local_abs_max);
case General8bit:
#pragma unroll NUM_PER_TH
for(int j = 0; j < NUM_PER_TH; j++)
{
if(!STOCHASTIC)
qvals[j] = dQuantize<0>(smem_code, 0.0f, ((float)vals[j])*local_abs_max);
else
qvals[j] = dQuantize<1>(smem_code, rand_vals[j], ((float)vals[j])*local_abs_max);
}
break;
case FP4:
#pragma unroll NUM_PER_TH
for(int j = 0; j < NUM_PER_TH/2; j++)
{
packed_4bit |= dQuantizeFP4(((float)vals[2*j])*local_abs_max) << 4;
packed_4bit |= dQuantizeFP4(((float)vals[2*j+1])*local_abs_max);
qvals[j] = packed_4bit;
}
break;
case NF4:
#pragma unroll NUM_PER_TH
for(int j = 0; j < NUM_PER_TH/2; j++)
{
packed_4bit |= dQuantizeNF4(((float)vals[2*j])*local_abs_max) << 4;
packed_4bit |= dQuantizeNF4(((float)vals[2*j+1])*local_abs_max);
qvals[j] = packed_4bit;
}
break;
}
__syncthreads();
StoreChar(storec).Store(&(out[i]), qvals, valid_items);
StoreChar(storec).Store(&(out[(DATA_TYPE > 0) ? i/2 : i]), qvals, (DATA_TYPE > 0) ? (valid_items+1)/2 : valid_items);
}
}
template<typename T, int BLOCK_SIZE, int THREADS, int NUM_PER_TH>
__global__ void kDequantizeBlockwise(float *code, unsigned char * A, float * absmax, T *out, const int n)
template<typename T, int TILE_SIZE, int THREADS, int NUM_PER_TH, int DATA_TYPE>
__global__ void kDequantizeBlockwise(float *code, unsigned char * A, float * absmax, T *out, const int blocksize, const int n)
{
const int n_full = gridDim.x * BLOCK_SIZE;
int valid_items = 0;
const int base_idx = (blockIdx.x * BLOCK_SIZE);
const int n_load = (gridDim.x * TILE_SIZE);
int valid_items_load = 0;
int valid_items_store = 0;
const int base_idx = (blockIdx.x * TILE_SIZE);
T vals[NUM_PER_TH];
T vals[NUM_PER_TH*((DATA_TYPE > 0) ? 2 : 1)];
unsigned char qvals[NUM_PER_TH];
float local_abs_max = -FLT_MAX;
typedef cub::BlockLoad<unsigned char, THREADS, NUM_PER_TH, cub::BLOCK_LOAD_WARP_TRANSPOSE> LoadChar;
typedef cub::BlockStore<T, THREADS, NUM_PER_TH, cub::BLOCK_STORE_WARP_TRANSPOSE> StoreT;
typedef cub::BlockStore<T, THREADS, NUM_PER_TH*((DATA_TYPE > 0) ? 2 : 1), cub::BLOCK_STORE_WARP_TRANSPOSE> StoreT;
__shared__ typename LoadChar::TempStorage loadchar;
__shared__ typename StoreT::TempStorage storet;
//__shared__ float smem_code[256];
//float local_code[16];
//if(threadIdx.x < 256)
//smem_code[threadIdx.x] = code[threadIdx.x];
for (unsigned int i = base_idx; i < n_full; i += gridDim.x*BLOCK_SIZE)
for (unsigned int i = base_idx; i < n_load; i += gridDim.x*TILE_SIZE)
{
valid_items = n - i > BLOCK_SIZE ? BLOCK_SIZE : n - i;
local_abs_max = absmax[i/BLOCK_SIZE];
if(DATA_TYPE > 0)
{
valid_items_load = (n+1)/2 - i > TILE_SIZE ? TILE_SIZE : (n+1)/2 - i;
valid_items_store = n - i*2 > TILE_SIZE*2 ? TILE_SIZE*2 : n - i*2;
}
else
{
valid_items_load = n - i > TILE_SIZE ? TILE_SIZE : n - i;
valid_items_store = n - i > TILE_SIZE ? TILE_SIZE : n - i;
}
local_abs_max = __ldg(&absmax[(i+threadIdx.x*NUM_PER_TH)/(blocksize)]);
__syncthreads();
LoadChar(loadchar).Load(&(A[i]), qvals, valid_items, 128);
__syncthreads();
LoadChar(loadchar).Load(&(A[i]), qvals, valid_items_load, 128);
// load code through read-only cache via __ldg
#pragma unroll NUM_PER_TH
for(int j = 0; j < NUM_PER_TH; j++)
vals[j] = __ldg(&code[qvals[j]])*local_abs_max;
switch(DATA_TYPE)
{
case General8bit:
// load code through read-only cache via __ldg
#pragma unroll NUM_PER_TH
for(int j = 0; j < NUM_PER_TH; j++)
vals[j] = __ldg(&code[qvals[j]])*local_abs_max;
break;
case FP4:
#pragma unroll NUM_PER_TH
for(int j = 0; j < NUM_PER_TH; j++)
{
vals[j*2] = dDequantizeFP4Tree(qvals[j] >> 4, local_abs_max);
vals[j*2 + 1] = dDequantizeFP4Tree(qvals[j] & 0x0F, local_abs_max);
}
break;
case NF4:
#pragma unroll NUM_PER_TH
for(int j = 0; j < NUM_PER_TH; j++)
{
vals[j*2] = dDequantizeNF4(qvals[j] >> 4)* local_abs_max;
vals[j*2 + 1] = dDequantizeNF4(qvals[j] & 0x0F)* local_abs_max;
}
break;
}
__syncthreads();
StoreT(storet).Store(&(out[i]), vals, valid_items);
__syncthreads();
StoreT(storet).Store(&(out[(DATA_TYPE > 0) ? i*2 : i]), vals, valid_items_store);
}
}
__global__ void kDequantize(float *code, unsigned char *A, float *out, const int n)
{
const unsigned int numThreads = blockDim.x * gridDim.x;
......@@ -1460,6 +1793,7 @@ kOptimizerStatic8bit2StateBlockwise(T* p, T* __restrict__ const g, unsigned char
unsigned char c1s[N_PER_TH];
unsigned char c2s[N_PER_TH];
T g_vals[N_PER_TH];
T p_vals[N_PER_TH];
typedef cub::BlockLoad<T, BLOCK_SIZE/N_PER_TH, N_PER_TH, cub::BLOCK_LOAD_WARP_TRANSPOSE> LoadT;
typedef cub::BlockLoad<unsigned char, BLOCK_SIZE/N_PER_TH, N_PER_TH, cub::BLOCK_LOAD_WARP_TRANSPOSE> LoadChar;
......@@ -1521,16 +1855,24 @@ kOptimizerStatic8bit2StateBlockwise(T* p, T* __restrict__ const g, unsigned char
# pragma unroll N_PER_TH
for(unsigned int j = 0; j < N_PER_TH; j++)
{
g_val = float(g_vals[j]);
g_val *= gnorm_scale;
if(!skip_zeros || (skip_zeros && ((float)g_vals[j] != 0.0f)))
if(!isnan((float)g_vals[j]) && !isinf((float)g_vals[j]))
{
s1_vals[j] = smem_quantiles1[lane_id][c1s[j]]*absmax1[i/BLOCK_SIZE];
s1_vals[j] = (s1_vals[j]*beta1) + (((1.0f-beta1)*g_val));
s2_vals[j] = smem_quantiles2[lane_id][c2s[j]]*absmax2[i/BLOCK_SIZE];
g_val = g_vals[j];
//float ratio = (g_val*g_val)/fmaxf(s2_vals[j], eps*eps);
//g_val = ratio > 2.0f ? 2.0f*g_val/ratio : g_val;
g_val *= gnorm_scale;
s2_vals[j] = (s2_vals[j]*beta2) + (((1.0f-beta2)*g_val*g_val));
s1_vals[j] = smem_quantiles1[lane_id][c1s[j]]*absmax1[i/BLOCK_SIZE];
s1_vals[j] = (s1_vals[j]*beta1) + (((1.0f-beta1)*g_val));
}
else
{
s1_vals[j] = 0.0f;
s2_vals[j] = 0.0f;
}
new_local_abs_max1 = fmaxf(new_local_abs_max1, fabsf(s1_vals[j]));
new_local_abs_max2 = fmaxf(new_local_abs_max2, fabsf(s2_vals[j]));
......@@ -1561,22 +1903,23 @@ kOptimizerStatic8bit2StateBlockwise(T* p, T* __restrict__ const g, unsigned char
}
__syncthreads();
LoadT(temp_storage.loadh).Load(&(p[i]), g_vals, valid_items, (T)0.0f);
LoadT(temp_storage.loadh).Load(&(p[i]), p_vals, valid_items, (T)0.0f);
// reduce: 2.67/1.69 -> 2.67/1.70
# pragma unroll N_PER_TH
for(unsigned int j = 0; j < N_PER_TH; j++)
{
if(!skip_zeros || (skip_zeros && ((float)g_vals[j] != 0.0f)))
//if(!skip_zeros || (skip_zeros && ((float)g_vals[j] != 0.0f)))
if(!isnan((float)g_vals[j]) && !isinf((float)g_vals[j]))
{
g_vals[j] = (T)(((float)g_vals[j]) + ((step_size*(__fdividef(s1_vals[j],(sqrtf(s2_vals[j])+(correction2*eps)))))));
p_vals[j] = (T)(((float)p_vals[j]) + ((step_size*(__fdividef(s1_vals[j],(sqrtf(s2_vals[j])+(correction2*eps)))))));
if(weight_decay > 0.0f)
g_vals[j] = ((float)g_vals[j])*(1.0f-(lr*weight_decay));
p_vals[j] = ((float)p_vals[j])*(1.0f-(lr*weight_decay));
}
}
// store: 0.85/1.44 -> 2.48/1.57
__syncthreads();
StoreT(temp_storage.storeh).Store(&(p[i]), g_vals, valid_items);
StoreT(temp_storage.storeh).Store(&(p[i]), p_vals, valid_items);
// quantizaztion: 2.67/1.70 -> 3.4/3.3
# pragma unroll N_PER_TH
......@@ -2496,7 +2839,7 @@ template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int T
}
}
#define C 1.0f/127.0f
#define DENORM 1.0f/127.0f
#define MAX_SPARSE_COUNT 32
#define SMEM_SIZE 8*256
template <typename T, int SPMM_ITEMS, int BITS>
......@@ -2575,7 +2918,6 @@ __global__ void kspmm_coo_very_sparse_naive(int *max_count, int *max_idx, int *o
// 4. Multiply the tile -> accumulate outputs in shared memory until 128 bytes it reached
int idx = idx_col_B + (warp_idx*SPMM_ITEMS) + j;
if(idx >= colsB){ break; }
//printf("%i %i\n", (row_offset+idx) % num_items, row_offset+idx);
if((idx+num_items < colsB))
{
if(BITS == 8)
......@@ -2595,15 +2937,13 @@ __global__ void kspmm_coo_very_sparse_naive(int *max_count, int *max_idx, int *o
#pragma unroll num_items
for(int k = 0; k < num_items; k++)
{
//if((float)local_valsB[k] != 0.0)
// printf("%f %i %i %i\n", (float)local_valsB[k], k, idx, colsB);
if(BITS == 8 && dequant_stats != NULL)
// we do texture cache reads (__ldg) on dequant_stats which should be super fast
{
float valB = local_valsB[k];
float valA = local_valA[i];
if(valB != 0.0 && valA != 0.0)
local_valC[j+k] = (float)local_valC[j+k] + ((float)smem_dequant_stats[idx+k-local_idx_col_B_offset])*C*valB*valA;
local_valC[j+k] = (float)local_valC[j+k] + ((float)smem_dequant_stats[idx+k-local_idx_col_B_offset])*DENORM*valB*valA;
}
else
local_valC[j+k] = (float)local_valC[j+k] + (float)local_valsB[k]*(float)local_valA[i];
......@@ -2709,10 +3049,587 @@ template <int FORMAT> __global__ void kExtractOutliers(char *A, int *idx, char *
}
}
//template <int QUANT_TYPE, typename INPT, typename COMPT, typename OUTT> __global__ void kMatmul_inference_4bit(INPT *A, unsigned char *B, OUTT *out, int lda, int ldb, int rowsA, int colsA, int colsB)
//{
//// element-wise kernel
//// 1. Load batch x k into registers
//// 2. Load k x k into registers
//// 3. dequantize and store in second pair of k x k
//// 4. matmul
//// 5. sum with cub
//// 6. store outputs
//// TC kernel
//// use k warps per thread block
//// 1. threadblock use read-only cache to read in register tile for A into shared memory
//// 2. each warp loops over shared memory tiles of A of size 8x16 and loads them into fragments
//// 3. each warp reads a segment of values 16x32 from B
//// 4. do dequantization from register of B into second pair of registers
//// 5. store (4) into fragment
//// 6. matmul aggregate into fragment C
//// 7. aggreecate files of C into shared memroy block C
//// 8. sum (7)
//// 9. write outputs to matmul output matrix
//}
template <typename T, typename TCAST, int ITEMS> __device__ inline void vector_load(T *local, T * __restrict__ const buffer, int idx, int limit_base, int limit, float zero_value = 0.0f)
{
if(limit_base + ITEMS <= limit)
reinterpret_cast<TCAST*>(local)[0] = reinterpret_cast<TCAST*>(buffer)[idx/ITEMS];
else
{
for(int k = 0; k < ITEMS; k++)
{
if(limit_base + k < limit)
local[k] = buffer[idx+k];
else
local[k] = (T)zero_value;
}
}
}
#define WARPS 5
template <typename T, int BITS, int THREADS> __global__ void gemm_device(int M, int N, int K, T * __restrict__ const A, T* B, T * out, int lda, int ldb, int ldc)
{
#if __CUDA_ARCH__ >= 750
using namespace nvcuda;
int col_offset = blockIdx.x *32;
const int warp_id = threadIdx.x / 32;
const int half_warp_id = threadIdx.x / 16;
const int half_warp_lane = threadIdx.x % 16;
const int batch_size_warps = (WARPS-1)*2;
const int val_per_iter = blockDim.x-32;
T local_A[4];
T local_B[128];
const int a_tile_offset = 16;
const int b_tile_offset = (16*32 + 16);
__shared__ T smem_A[8*16 + (2*16*(batch_size_warps-1))];
__shared__ T smem_B[2*batch_size_warps*16*32 + (2*16*(batch_size_warps-1))];
//__shared__ T smem_C[8*32];
wmma::fragment<wmma::matrix_a, 8, 32, 16, half, wmma::row_major> a_frag;
wmma::fragment<wmma::matrix_b, 8, 32, 16, half, wmma::col_major> b_frag;
wmma::fragment<wmma::accumulator, 8, 32, 16, half> c_frag;
wmma::fill_fragment(c_frag, 0.0f);
int ticktock = 0;
int idx = 0 + threadIdx.x;
int loaded_values = 0;
// prefetch
if(idx < K && warp_id < (WARPS-1))
{
if(loaded_values == 0)
{
local_A[0] = A[idx];
local_A[1] = A[idx+(1*val_per_iter)];
local_A[2] = A[idx+(2*val_per_iter)];
local_A[3] = A[idx+(3*val_per_iter)];
#pragma unroll 32
for(int col = 0; col < 32; col++)
{
local_B[col] = B[(col_offset+col)*ldb+idx];
local_B[col+32] = B[(col_offset+col)*ldb+idx+(1*val_per_iter)];
local_B[col+64] = B[(col_offset+col)*ldb+idx+(2*val_per_iter)];
local_B[col+96] = B[(col_offset+col)*ldb+idx+(3*val_per_iter)];
}
loaded_values = 3;
}
else
{
if(loaded_values == 3)
{
local_A[0] = local_A[1];
#pragma unroll 32
for(int col = 0; col < 32; col++)
local_B[col] = local_B[col+(32)];
}
else if(loaded_values == 2)
{
local_A[0] = local_A[2];
#pragma unroll 32
for(int col = 0; col < 32; col++)
local_B[col] = local_B[col+(64)];
}
else
{
local_A[0] = local_A[3];
#pragma unroll 32
for(int col = 0; col < 32; col++)
local_B[col] = local_B[col+(96)];
}
loaded_values--;
}
smem_A[half_warp_lane + (((batch_size_warps*ticktock)+half_warp_id)*a_tile_offset)] = local_A[0];
#pragma unroll 32
for(int col = 0; col < 32; col++)
smem_B[half_warp_lane + (((batch_size_warps*ticktock)+half_warp_id)*b_tile_offset) + (col*16)] = local_B[col];
}
else if(warp_id < (WARPS-1))
{
local_A[0] = T(0.0);
smem_A[half_warp_lane + (((batch_size_warps*ticktock)+half_warp_id)*a_tile_offset)] = 0.0f;
#pragma unroll 32
for(int col = 0; col < 32; col++)
local_B[col] = 0.0f;
#pragma unroll 32
for(int col = 0; col < 32; col++)
smem_B[half_warp_lane + (((batch_size_warps*ticktock)+half_warp_id)*b_tile_offset) + (col*16)] = 0.0f;
}
ticktock = ticktock == 0 ? 1 : 0;
//for(int base_idx = blockDim.x-32; base_idx < K; base_idx+=blockDim.x-32)
for(int base_idx = blockDim.x-32; base_idx < K; base_idx+=blockDim.x-32)
{
idx = base_idx + threadIdx.x;
__syncthreads();
if(idx < K && warp_id < (WARPS-1))
{
//local_A[0] = A[idx];
//#pragma unroll 32
//for(int col = 0; col < 32; col++)
// local_B[col] = B[(col_offset+col)*ldb+idx];
if(loaded_values == 0)
{
local_A[0] = A[idx];
local_A[1] = A[idx+(1*val_per_iter)];
local_A[2] = A[idx+(2*val_per_iter)];
local_A[3] = A[idx+(3*val_per_iter)];
#pragma unroll 32
for(int col = 0; col < 32; col++)
{
local_B[col] = B[(col_offset+col)*ldb+idx];
local_B[col+32] = B[(col_offset+col)*ldb+idx+(1*val_per_iter)];
local_B[col+64] = B[(col_offset+col)*ldb+idx+(2*val_per_iter)];
local_B[col+96] = B[(col_offset+col)*ldb+idx+(3*val_per_iter)];
}
loaded_values = 3;
}
else
{
if(loaded_values == 3)
{
local_A[0] = local_A[1];
#pragma unroll 32
for(int col = 0; col < 32; col++)
local_B[col] = local_B[col+(32)];
}
else if(loaded_values == 2)
{
local_A[0] = local_A[2];
#pragma unroll 32
for(int col = 0; col < 32; col++)
local_B[col] = local_B[col+(64)];
}
else
{
local_A[0] = local_A[3];
#pragma unroll 32
for(int col = 0; col < 32; col++)
local_B[col] = local_B[col+(96)];
}
loaded_values--;
}
smem_A[half_warp_lane + (((batch_size_warps*ticktock)+half_warp_id)*a_tile_offset)] = local_A[0];
#pragma unroll 32
for(int col = 0; col < 32; col++)
smem_B[half_warp_lane + (((batch_size_warps*ticktock)+half_warp_id)*b_tile_offset) + (col*16)] = local_B[col];
}
else if(warp_id < (WARPS-1))
{
local_A[0] = T(0.0);
smem_A[half_warp_lane + (((batch_size_warps*ticktock)+half_warp_id)*a_tile_offset)] = 0.0f;
#pragma unroll 32
for(int col = 0; col < 32; col++)
local_B[col] = 0.0f;
#pragma unroll 32
for(int col = 0; col < 32; col++)
smem_B[half_warp_lane + (((batch_size_warps*ticktock)+half_warp_id)*b_tile_offset) + (col*16)] = 0.0f;
}
ticktock = ticktock == 0 ? 1 : 0;
if(warp_id == (WARPS-1))
for(int k = 0; k < batch_size_warps; k++)
{
wmma::load_matrix_sync(a_frag, &(smem_A[(ticktock*batch_size_warps + k)*a_tile_offset]), 16); // 111 mu
wmma::load_matrix_sync(b_frag, &(smem_B[(ticktock*batch_size_warps + k)*b_tile_offset]), 16); // 35 mu
wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);
}
}
__syncthreads();
if(warp_id != (WARPS-1)){ return; }
// only warp_id == (WARPS-1) from here
int warp_lane = threadIdx.x % 32;
ticktock = ticktock == 0 ? 1 : 0;
for(int k = 0; k < batch_size_warps; k++)
{
wmma::load_matrix_sync(a_frag, &(smem_A[(ticktock*batch_size_warps + k)*a_tile_offset]), 16); // 111 mu
wmma::load_matrix_sync(b_frag, &(smem_B[(ticktock*batch_size_warps + k)*b_tile_offset]), 16); // 35 mu
wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);
}
// 129 mu
if(warp_id == (WARPS-1))
wmma::store_matrix_sync(&(smem_A[0]), c_frag, 32, wmma::mem_row_major);
if(col_offset + warp_lane < M)
out[col_offset + warp_lane] = smem_A[warp_lane];
#endif
}
template <typename T, int THREADS> __global__ void kgemm_4bit_inference(int M, int N, int K, T * __restrict__ const A, unsigned char *B, float *absmax, T * out, int lda, int ldb, int ldc, int blocksize)
{
#if __CUDA_ARCH__ >= 750
using namespace nvcuda;
int col_offset = blockIdx.x *32;
const int warp_id = threadIdx.x / 32;
const int half_warp_id = threadIdx.x / 16;
const int half_warp_lane = threadIdx.x % 16;
const int batch_size_warps = (WARPS-1)*2;
T local_A[2];
T local_B[64];
unsigned char local_B_4bit[32];
const int a_tile_offset = 16;
const int b_tile_offset = (16*32 + 16);
__shared__ T smem_A[8*16 + (2*16*(batch_size_warps-1))];
__shared__ T smem_B[2*batch_size_warps*16*32 + (2*16*(batch_size_warps-1))];
//__shared__ T smem_C[8*32];
wmma::fragment<wmma::matrix_a, 8, 32, 16, half, wmma::row_major> a_frag;
wmma::fragment<wmma::matrix_b, 8, 32, 16, half, wmma::col_major> b_frag;
wmma::fragment<wmma::accumulator, 8, 32, 16, half> c_frag;
wmma::fill_fragment(c_frag, 0.0f);
int ticktock = 0;
int idx = 0 + threadIdx.x;
int loaded_values = 0;
// prefetch
if(idx < K && warp_id < (WARPS-1))
{
if(loaded_values == 0)
{
local_A[0] = A[idx];
local_A[1] = A[idx+blockDim.x-32];
#pragma unroll 32
for(int col = 0; col < 32; col++)
local_B_4bit[col] = B[(col_offset+col)*ldb+idx];
loaded_values = 1;
}
else
{
local_A[0] = local_A[1];
loaded_values--;
#pragma unroll 64
for(int col = 0; col < 64; col+=2)
{
local_B[col] = dhDequantizeNF4(local_B_4bit[col/2] >> 4)*T(1.0f);
local_B[col+1] = dhDequantizeNF4(local_B_4bit[col/2] & 0x0F)*T(1.0f);
}
}
smem_A[half_warp_lane + (((batch_size_warps*ticktock)+half_warp_id)*a_tile_offset)] = local_A[0];
#pragma unroll 32
for(int col = 0; col < 32; col++)
smem_B[half_warp_lane + (((batch_size_warps*ticktock)+half_warp_id)*b_tile_offset) + (col*16)] = local_B[col];
}
else if(warp_id < (WARPS-1))
{
local_A[0] = T(0.0);
smem_A[half_warp_lane + (((batch_size_warps*ticktock)+half_warp_id)*a_tile_offset)] = 0.0f;
#pragma unroll 32
for(int col = 0; col < 32; col++)
local_B[col] = 0.0f;
#pragma unroll 32
for(int col = 0; col < 32; col++)
smem_B[half_warp_lane + (((batch_size_warps*ticktock)+half_warp_id)*b_tile_offset) + (col*16)] = 0.0f;
}
ticktock = ticktock == 0 ? 1 : 0;
//for(int base_idx = blockDim.x-32; base_idx < K; base_idx+=blockDim.x-32)
for(int base_idx = blockDim.x-32; base_idx < K; base_idx+=blockDim.x-32)
{
idx = base_idx + threadIdx.x;
__syncthreads();
if(idx < K && warp_id < (WARPS-1))
{
if(loaded_values == 0)
{
local_A[0] = A[idx];
local_A[1] = A[idx+blockDim.x-32];
#pragma unroll 32
for(int col = 0; col < 32; col++)
{
local_B_4bit[col] = B[(col_offset+col)*ldb+idx];
local_B_4bit[col+16] = B[(col_offset+col)*ldb+idx];
}
loaded_values = 1;
}
else
{
local_A[0] = local_A[1];
loaded_values--;
int absidx = (idx + col_offset)/blocksize;
half local_absmax = __ldg(&(absmax[absidx]));
#pragma unroll 64
for(int col = 0; col < 64; col+=2)
{
local_B[col] = dhDequantizeNF4(local_B_4bit[col/2] >> 4)*T(absidx);
local_B[col+1] = dhDequantizeNF4(local_B_4bit[col/2] & 0x0F)*T(absidx);
}
}
smem_A[half_warp_lane + (((batch_size_warps*ticktock)+half_warp_id)*a_tile_offset)] = local_A[0];
#pragma unroll 32
for(int col = 0; col < 32; col++)
smem_B[half_warp_lane + (((batch_size_warps*ticktock)+half_warp_id)*b_tile_offset) + (col*16)] = local_B[col];
}
else if(warp_id < (WARPS-1))
{
local_A[0] = T(0.0);
smem_A[half_warp_lane + (((batch_size_warps*ticktock)+half_warp_id)*a_tile_offset)] = 0.0f;
#pragma unroll 32
for(int col = 0; col < 32; col++)
local_B[col] = 0.0f;
#pragma unroll 32
for(int col = 0; col < 32; col++)
smem_B[half_warp_lane + (((batch_size_warps*ticktock)+half_warp_id)*b_tile_offset) + (col*16)] = 0.0f;
}
ticktock = ticktock == 0 ? 1 : 0;
if(warp_id == (WARPS-1))
for(int k = 0; k < batch_size_warps; k++)
{
wmma::load_matrix_sync(a_frag, &(smem_A[(ticktock*batch_size_warps + k)*a_tile_offset]), 16); // 111 mu
wmma::load_matrix_sync(b_frag, &(smem_B[(ticktock*batch_size_warps + k)*b_tile_offset]), 16); // 35 mu
wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);
}
}
__syncthreads();
if(warp_id != (WARPS-1)){ return; }
// only warp_id == (WARPS-1) from here
int warp_lane = threadIdx.x % 32;
ticktock = ticktock == 0 ? 1 : 0;
for(int k = 0; k < batch_size_warps; k++)
{
wmma::load_matrix_sync(a_frag, &(smem_A[(ticktock*batch_size_warps + k)*a_tile_offset]), 16); // 111 mu
wmma::load_matrix_sync(b_frag, &(smem_B[(ticktock*batch_size_warps + k)*b_tile_offset]), 16); // 35 mu
wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);
}
// 129 mu
if(warp_id == (WARPS-1))
wmma::store_matrix_sync(&(smem_A[0]), c_frag, 32, wmma::mem_row_major);
if(col_offset + warp_lane < M)
out[col_offset + warp_lane] = smem_A[warp_lane];
#endif
}
//#define ROWS 2
//template <typename T, int ITEMS, int THREADS> __global__ void gemm_device(int M, int N, int K, T const* A, T* B, T * out, int lda, int ldb, int ldc)
//{
//// 0. We want to fill a 8x128 tile for a thread block so we have 8x16 tile for each warp
//// 1. Load dataB into register
//// 2. Dequantize B
//// 3. Fetch data from A and multiply
//
// typedef cub::BlockLoad<T, THREADS , ITEMS, cub::BLOCK_LOAD_WARP_TRANSPOSE> LoadA;
// //__shared__ typename LoadA::TempStorage loada;
// typedef cub::BlockLoad<T, THREADS , ITEMS, cub::BLOCK_LOAD_WARP_TRANSPOSE> LoadB;
// //__shared__ typename LoadB::TempStorage loadb;
// typedef cub::BlockReduce<T, THREADS> BlockReduce;
// // Allocate shared memory for BlockReduce
// //__shared__ typename BlockReduce::TempStorage reduce;
//
// __shared__ union {
// typename BlockReduce::TempStorage reduce;
// typename LoadB::TempStorage loadb;
// typename LoadA::TempStorage loada;
// } temp_storage;
//
//
// T dataA[ITEMS];
// T local_B[ITEMS];
// T local_accC[ROWS];
// int valid_items = 0;
// const int col_offset = blockIdx.x * 8;
//
// __shared__ T tileA[ROWS*THREADS*ITEMS];
// __shared__ T accumulatorC[ROWS*8];
//
// //#pragma unroll 8
// //for(int i = 0; i < 8; i++)
// // tileA[threadIdx.x + (i*256)] = 0.0f;
// //__syncthreads();
// if(threadIdx.x < 64)
// accumulatorC[threadIdx.x] = 0.0f;
// __syncthreads();
//
//
// for(int inner_idx = 0; inner_idx < K; inner_idx+= THREADS*ITEMS)
// {
// valid_items = K - inner_idx > THREADS*ITEMS ? THREADS*ITEMS : K - inner_idx;
// int baserow = 0;
// for(int row = baserow; row < (baserow+ROWS) && row < N; row++)
// {
// LoadA(temp_storage.loada).Load(&(A[(row*K) + inner_idx]), dataA, valid_items, 0.0f);
//
// #pragma unroll ITEMS
// for(int k = 0; k < ITEMS; k++)
// tileA[row*THREADS*ITEMS + threadIdx.x + (k*THREADS)] = dataA[k];
//
// __syncthreads();
// }
// baserow += ROWS;
//
// // load 16 columns from B at a time. B is transposed, so its like loading rows
// // each warp loads one row
// // each thread loads 128 byte
//
// // col: inner_idx + warp_lane
// // row: ldb*(offset + warp_id)
// for(int col = 0; col < 8 && (col_offset + col) < M; col++)
// {
// int colB = col_offset + col;
//
// for(int k = 0; k < ROWS; k++)
// local_accC[k] = 0.0f;
//
// int base_idxB = ldb*colB;
// valid_items = K - inner_idx > THREADS*ITEMS ? THREADS*ITEMS : K - inner_idx;
// LoadB(temp_storage.loadb).Load(&(B[base_idxB + inner_idx]), local_B, valid_items, 0.0f);
// __syncthreads();
//
// for(int row = 0; row < ROWS && row < N; row++)
// {
// #pragma unroll ITEMS
// for(int k = 0; k < ITEMS; k++)
// {
// int idxA = row*THREADS*ITEMS + threadIdx.x + (THREADS*k);
// local_accC[row] += tileA[idxA]*local_B[k];
// }
//
// local_accC[row] = BlockReduce(temp_storage.reduce).Reduce(local_accC[row], cub::Sum());
// if(threadIdx.x == 0)
// atomicAdd(&accumulatorC[row*8 + col], local_accC[row]);
// }
// }
// }
//
// for(int row = 0; row < ROWS && row < N; row++)
// {
// int out_idx = ldc*row + col_offset;
//
// //if(threadIdx.x < 8)
// // if(accumulatorC[row*8 + threadIdx.x] != 0.0)
// // printf("%i %i %i %i %f idx %i %i %i\n", row, col_offset, threadIdx.x, N, accumulatorC[row*8 + threadIdx.x], ldc, out_idx, blockIdx.x);
//
// if(threadIdx.x < 8 && (col_offset + threadIdx.x) < M)
// {
// //printf("%i %i %i %i %f idx %i %i\n", row, col_offset, threadIdx.x, N, accumulatorC[row*8 + threadIdx.x], ldc, out_idx);
// out[out_idx + threadIdx.x] = accumulatorC[row*8 + threadIdx.x];
// }
// }
//
//
//
//}
template <typename T, int FUNC> __global__ void kfunc(T *A, T *B, T value, long n)
{
for(long i = (blockDim.x*blockIdx.x) + threadIdx.x; i < n; i+=(blockDim.x*gridDim.x))
{
switch(FUNC)
{
case FILL:
A[i] = (T)value;
break;
case ARANGE:
A[i] = (T)i;
break;
case _MUL:
A[i] = A[i]*B[i];
break;
}
}
}
//==============================================================
// TEMPLATE DEFINITIONS
//==============================================================
template __global__ void kfunc<float, FILL>(float *A, float *B, float value, long n);
template __global__ void kfunc<unsigned char, FILL>(unsigned char *A, unsigned char *B, unsigned char value, long n);
template __global__ void kfunc<float, ARANGE>(float *A, float *B, float value, long n);
template __global__ void kfunc<float, _MUL>(float *A, float *B, float value, long n);
// these are not used and make no sense, but the compiler needs them
//template __global__ void gemm_device<float, 16, 128>(int M, int N, int K, float * __restrict__ const A, float* B, float * out, int lda, int ldb, int ldc);
template __global__ void gemm_device<half, 32, 256>(int M, int N, int K, half * __restrict__ const A, half* B, half * out, int lda, int ldb, int ldc);
template __global__ void gemm_device<half, 32, 192>(int M, int N, int K, half * __restrict__ const A, half* B, half * out, int lda, int ldb, int ldc);
template __global__ void gemm_device<half, 32, 160>(int M, int N, int K, half * __restrict__ const A, half* B, half * out, int lda, int ldb, int ldc);
template __global__ void gemm_device<half, 32, 128>(int M, int N, int K, half * __restrict__ const A, half* B, half * out, int lda, int ldb, int ldc);
//template __global__ void gemm_device<float, 16, 32>(int M, int N, int K, float * __restrict__ const A, float* B, float * out, int lda, int ldb, int ldc);
template __global__ void gemm_device<half, 32, 32>(int M, int N, int K, half * __restrict__ const A, half* B, half * out, int lda, int ldb, int ldc);
template __global__ void gemm_device<half, 32, 64>(int M, int N, int K, half * __restrict__ const A, half* B, half * out, int lda, int ldb, int ldc);
template __global__ void gemm_device<half, 32, 96>(int M, int N, int K, half * __restrict__ const A, half* B, half * out, int lda, int ldb, int ldc);
// these are not used and make no sense, but the compiler needs them
//template __global__ void gemm_device<float, 32, 128>(int M, int N, int K, float * __restrict__ const A, float* B, float * out, int lda, int ldb, int ldc);
template __global__ void gemm_device<half, 16, 256>(int M, int N, int K, half * __restrict__ const A, half* B, half * out, int lda, int ldb, int ldc);
template __global__ void gemm_device<half, 16, 192>(int M, int N, int K, half * __restrict__ const A, half* B, half * out, int lda, int ldb, int ldc);
template __global__ void gemm_device<half, 16, 160>(int M, int N, int K, half * __restrict__ const A, half* B, half * out, int lda, int ldb, int ldc);
template __global__ void gemm_device<half, 16, 128>(int M, int N, int K, half * __restrict__ const A, half* B, half * out, int lda, int ldb, int ldc);
//template __global__ void gemm_device<float, 32, 32>(int M, int N, int K, float * __restrict__ const A, float* B, float * out, int lda, int ldb, int ldc);
template __global__ void gemm_device<half, 16, 32>(int M, int N, int K, half * __restrict__ const A, half* B, half * out, int lda, int ldb, int ldc);
template __global__ void gemm_device<half, 16, 64>(int M, int N, int K, half * __restrict__ const A, half* B, half * out, int lda, int ldb, int ldc);
template __global__ void gemm_device<half, 16, 96>(int M, int N, int K, half * __restrict__ const A, half* B, half * out, int lda, int ldb, int ldc);
template __global__ void kgemm_4bit_inference<half, 128>(int M, int N, int K, half * __restrict__ const A, unsigned char *B, float *absmax, half * out, int lda, int ldb, int ldc, int blocksize);
template __global__ void kgemm_4bit_inference<half, 160>(int M, int N, int K, half * __restrict__ const A, unsigned char *B, float *absmax, half * out, int lda, int ldb, int ldc, int blocksize);
template __global__ void kExtractOutliers<COL_TURING>(char *A, int *idx, char *out, int idx_size, int rowsA, int colsA, int tiledRowsA, int tiledColsA);
template __global__ void kExtractOutliers<COL_AMPERE>(char *A, int *idx, char *out, int idx_size, int rowsA, int colsA, int tiledRowsA, int tiledColsA);
......@@ -2753,6 +3670,7 @@ MAKE_PreconditionOptimizer32bit1State(RMSPROP, half)
MAKE_PreconditionOptimizer32bit1State(RMSPROP, float)
MAKE_PreconditionOptimizer32bit1State(LION, half)
MAKE_PreconditionOptimizer32bit1State(LION, float)
MAKE_PreconditionOptimizer32bit1State(LION, __nv_bfloat16)
MAKE_PreconditionOptimizer32bit1State(ADAGRAD, half)
MAKE_PreconditionOptimizer32bit1State(ADAGRAD, float)
......@@ -2766,6 +3684,7 @@ MAKE_Optimizer32bit1State(RMSPROP, half)
MAKE_Optimizer32bit1State(RMSPROP, float)
MAKE_Optimizer32bit1State(LION, half)
MAKE_Optimizer32bit1State(LION, float)
MAKE_Optimizer32bit1State(LION, __nv_bfloat16)
MAKE_Optimizer32bit1State(ADAGRAD, half)
MAKE_Optimizer32bit1State(ADAGRAD, float)
......@@ -2775,12 +3694,15 @@ template __global__ void kPreconditionOptimizer32bit2State<gtype, oname, 4096, 8
const float beta1, const float beta2, const float eps, const float weight_decay, \
const int step, const float lr, const float gnorm_scale, const int n); \
MAKE_PreconditionOptimizer32bit2State(ADAM, half)
MAKE_PreconditionOptimizer32bit2State(ADAM, float)
MAKE_PreconditionOptimizer32bit2State(ADAM, half)
MAKE_PreconditionOptimizer32bit2State(ADAM, __nv_bfloat16)
template __global__ void kOptimizer32bit2State<float, ADAM>(float* g, float* p, float* state1, float* state2, float *unorm, const float max_unorm, const float param_norm,
const float beta1, const float beta2, const float eps, const float weight_decay,const int step, const float lr, const float gnorm_scale, const bool skip_zeros, const int n);
template __global__ void kOptimizer32bit2State<half, ADAM>(half* g, half* p, float* state1, float* state2, float *unorm, const float max_unorm, const float param_norm,
const float beta1, const float beta2, const float eps, const float weight_decay,const int step, const float lr, const float gnorm_scale, const bool skip_zeros, const int n);
template __global__ void kOptimizer32bit2State<float, ADAM>(float* g, float* p, float* state1, float* state2, float *unorm, const float max_unorm, const float param_norm,
template __global__ void kOptimizer32bit2State<__nv_bfloat16, ADAM>(__nv_bfloat16* g, __nv_bfloat16* p, float* state1, float* state2, float *unorm, const float max_unorm, const float param_norm,
const float beta1, const float beta2, const float eps, const float weight_decay,const int step, const float lr, const float gnorm_scale, const bool skip_zeros, const int n);
#define MAKE_PreconditionStatic8bit1State(oname, gtype) \
......@@ -2851,39 +3773,60 @@ MAKE_optimizerStatic8bit2State(ADAM, float)
template __global__ void kPercentileClipping<float, 2048, 4>(float * __restrict__ g, float *gnorm_vec, int step, const int n);
template __global__ void kPercentileClipping<half, 2048, 4>(half * __restrict__ g, float *gnorm_vec, int step, const int n);
template __global__ void kQuantizeBlockwise<half, 4096, 4, 0>(float * code, half * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
template __global__ void kQuantizeBlockwise<float, 4096, 4, 0>(float * code, float * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
template __global__ void kQuantizeBlockwise<half, 4096, 4, 1>(float * code, half * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
template __global__ void kQuantizeBlockwise<float, 4096, 4, 1>(float * code, float * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
template __global__ void kQuantizeBlockwise<half, 2048, 4, 0>(float * code, half * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
template __global__ void kQuantizeBlockwise<float, 2048, 4, 0>(float * code, float * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
template __global__ void kQuantizeBlockwise<half, 1024, 4, 0>(float * code, half * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
template __global__ void kQuantizeBlockwise<float, 1024, 4, 0>(float * code, float * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
template __global__ void kQuantizeBlockwise<half, 512, 2, 0>(float * code, half * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
template __global__ void kQuantizeBlockwise<float, 512, 2, 0>(float * code, float * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
template __global__ void kQuantizeBlockwise<half, 256, 2, 0>(float * code, half * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
template __global__ void kQuantizeBlockwise<float, 256, 2, 0>(float * code, float * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
template __global__ void kQuantizeBlockwise<half, 128, 2, 0>(float * code, half * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
template __global__ void kQuantizeBlockwise<float, 128, 2, 0>(float * code, float * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
template __global__ void kQuantizeBlockwise<half, 64, 1, 0>(float * code, half * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
template __global__ void kQuantizeBlockwise<float, 64, 1, 0>(float * code, float * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
template __global__ void kDequantizeBlockwise<half, 4096, 1024, 4>(float *code, unsigned char * A, float * absmax, half *out, const int n);
template __global__ void kDequantizeBlockwise<float, 4096, 1024, 4>(float *code, unsigned char * A, float * absmax, float *out, const int n);
template __global__ void kDequantizeBlockwise<half, 2048, 512, 4>(float *code, unsigned char * A, float * absmax, half *out, const int n);
template __global__ void kDequantizeBlockwise<float, 2048, 512, 4>(float *code, unsigned char * A, float * absmax, float *out, const int n);
template __global__ void kDequantizeBlockwise<half, 1024, 256, 4>(float *code, unsigned char * A, float * absmax, half *out, const int n);
template __global__ void kDequantizeBlockwise<float, 1024, 256, 4>(float *code, unsigned char * A, float * absmax, float *out, const int n);
template __global__ void kDequantizeBlockwise<half, 512, 256, 2>(float *code, unsigned char * A, float * absmax, half *out, const int n);
template __global__ void kDequantizeBlockwise<float, 512, 256, 2>(float *code, unsigned char * A, float * absmax, float *out, const int n);
template __global__ void kDequantizeBlockwise<half, 256, 128, 2>(float *code, unsigned char * A, float * absmax, half *out, const int n);
template __global__ void kDequantizeBlockwise<float, 256, 128, 2>(float *code, unsigned char * A, float * absmax, float *out, const int n);
template __global__ void kDequantizeBlockwise<half, 128, 64, 2>(float *code, unsigned char * A, float * absmax, half *out, const int n);
template __global__ void kDequantizeBlockwise<float, 128, 64, 2>(float *code, unsigned char * A, float * absmax, float *out, const int n);
template __global__ void kDequantizeBlockwise<half, 64, 64, 1>(float *code, unsigned char * A, float * absmax, half *out, const int n);
template __global__ void kDequantizeBlockwise<float, 64, 64, 1>(float *code, unsigned char * A, float * absmax, float *out, const int n);
#define MAKE_kQuantizeBlockwise(dtype, blocksize, num_per_thread, stochastic, data_type_name) \
template __global__ void kQuantizeBlockwise<dtype, blocksize, num_per_thread, stochastic, data_type_name>(float * code, dtype * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n); \
MAKE_kQuantizeBlockwise(half, 4096, 4, 0, General8bit)
MAKE_kQuantizeBlockwise(half, 4096, 4, 1, General8bit)
MAKE_kQuantizeBlockwise(half, 2048, 4, 0, General8bit)
MAKE_kQuantizeBlockwise(half, 1024, 4, 0, General8bit)
MAKE_kQuantizeBlockwise(half, 512, 2, 0, General8bit)
MAKE_kQuantizeBlockwise(half, 256, 2, 0, General8bit)
MAKE_kQuantizeBlockwise(half, 128, 2, 0, General8bit)
MAKE_kQuantizeBlockwise(half, 64, 2, 0, General8bit)
MAKE_kQuantizeBlockwise(float, 4096, 4, 0, General8bit)
MAKE_kQuantizeBlockwise(float, 4096, 4, 1, General8bit)
MAKE_kQuantizeBlockwise(float, 2048, 4, 0, General8bit)
MAKE_kQuantizeBlockwise(float, 1024, 4, 0, General8bit)
MAKE_kQuantizeBlockwise(float, 512, 2, 0, General8bit)
MAKE_kQuantizeBlockwise(float, 256, 2, 0, General8bit)
MAKE_kQuantizeBlockwise(float, 128, 2, 0, General8bit)
MAKE_kQuantizeBlockwise(float, 64, 2, 0, General8bit)
MAKE_kQuantizeBlockwise(half, 4096, 4, 0, FP4)
MAKE_kQuantizeBlockwise(half, 2048, 4, 0, FP4)
MAKE_kQuantizeBlockwise(half, 1024, 4, 0, FP4)
MAKE_kQuantizeBlockwise(half, 512, 2, 0, FP4)
MAKE_kQuantizeBlockwise(half, 256, 2, 0, FP4)
MAKE_kQuantizeBlockwise(half, 128, 2, 0, FP4)
MAKE_kQuantizeBlockwise(half, 64, 2, 0, FP4)
MAKE_kQuantizeBlockwise(float, 4096, 4, 0, FP4)
MAKE_kQuantizeBlockwise(float, 2048, 4, 0, FP4)
MAKE_kQuantizeBlockwise(float, 1024, 4, 0, FP4)
MAKE_kQuantizeBlockwise(float, 512, 2, 0, FP4)
MAKE_kQuantizeBlockwise(float, 256, 2, 0, FP4)
MAKE_kQuantizeBlockwise(float, 128, 2, 0, FP4)
MAKE_kQuantizeBlockwise(float, 64, 2, 0, FP4)
MAKE_kQuantizeBlockwise(half, 4096, 4, 0, NF4)
MAKE_kQuantizeBlockwise(half, 2048, 4, 0, NF4)
MAKE_kQuantizeBlockwise(half, 1024, 4, 0, NF4)
MAKE_kQuantizeBlockwise(half, 512, 2, 0, NF4)
MAKE_kQuantizeBlockwise(half, 256, 2, 0, NF4)
MAKE_kQuantizeBlockwise(half, 128, 2, 0, NF4)
MAKE_kQuantizeBlockwise(half, 64, 2, 0, NF4)
MAKE_kQuantizeBlockwise(float, 4096, 4, 0, NF4)
MAKE_kQuantizeBlockwise(float, 2048, 4, 0, NF4)
MAKE_kQuantizeBlockwise(float, 1024, 4, 0, NF4)
MAKE_kQuantizeBlockwise(float, 512, 2, 0, NF4)
MAKE_kQuantizeBlockwise(float, 256, 2, 0, NF4)
MAKE_kQuantizeBlockwise(float, 128, 2, 0, NF4)
MAKE_kQuantizeBlockwise(float, 64, 2, 0, NF4)
template __global__ void kDequantizeBlockwise<half, 512, 64, 8, FP4>(float *code, unsigned char * A, float * absmax, half *out, const int blocksize, const int n);
template __global__ void kDequantizeBlockwise<float, 512, 64, 8, FP4>(float *code, unsigned char * A, float * absmax, float *out, const int blocksize, const int n);
template __global__ void kDequantizeBlockwise<half, 512, 64, 8, General8bit>(float *code, unsigned char * A, float * absmax, half *out, const int blocksize, const int n);
template __global__ void kDequantizeBlockwise<float, 512, 64, 8, General8bit>(float *code, unsigned char * A, float * absmax, float *out, const int blocksize, const int n);
template __global__ void kDequantizeBlockwise<half, 512, 64, 8, NF4>(float *code, unsigned char * A, float * absmax, half *out, const int blocksize, const int n);
template __global__ void kDequantizeBlockwise<float, 512, 64, 8, NF4>(float *code, unsigned char * A, float * absmax, float *out, const int blocksize, const int n);
#define MAKE_OptimizerStatic8bit2StateBlockwise(oname, gtype, block_size, num_per_thread) \
template __global__ void kOptimizerStatic8bit2StateBlockwise<gtype, oname, block_size, num_per_thread>(gtype* p, gtype* __restrict__ const g, unsigned char* state1, unsigned char* state2, \
......@@ -2896,6 +3839,8 @@ template __global__ void kOptimizerStatic8bit2StateBlockwise<gtype, oname, block
MAKE_OptimizerStatic8bit2StateBlockwise(ADAM, float, 2048, 8)
MAKE_OptimizerStatic8bit2StateBlockwise(ADAM, half, 2048, 8)
MAKE_OptimizerStatic8bit2StateBlockwise(ADAM, __nv_bfloat16, 2048, 8)
#define MAKE_OptimizerStatic8bit1StateBlockwise(oname, gtype, block_size, num_per_thread) \
template __global__ void kOptimizerStatic8bit1StateBlockwise<gtype, oname, block_size, num_per_thread>( \
......@@ -2913,5 +3858,6 @@ MAKE_OptimizerStatic8bit1StateBlockwise(RMSPROP, float, 2048, 8)
MAKE_OptimizerStatic8bit1StateBlockwise(RMSPROP, half, 2048, 8)
MAKE_OptimizerStatic8bit1StateBlockwise(LION, float, 2048, 8)
MAKE_OptimizerStatic8bit1StateBlockwise(LION, half, 2048, 8)
MAKE_OptimizerStatic8bit1StateBlockwise(LION, __nv_bfloat16, 2048, 8)
MAKE_OptimizerStatic8bit1StateBlockwise(ADAGRAD, float, 2048, 8)
MAKE_OptimizerStatic8bit1StateBlockwise(ADAGRAD, half, 2048, 8)
csrc/kernels.cuh
View file @ a24aae30
......@@ -9,13 +9,15 @@
#ifndef kernels
#define kernels
//template <int QUANT_TYPE, typename INP_TYPE, typename COMP_TYPE, typename OUT_TYPE>__global__ void kMatmul_inference_4bit(INP_TYPE *A, unsigned char *B, OUT_TYPE *out, int lda, int ldb, int rowsA, int colsA, int colsB);
template<typename T>__global__ void kEstimateQuantiles(T *__restrict__ const A, float *code, const float offset, const T max_val, const int n);
__global__ void kQuantize(float * code, float * __restrict__ const A, unsigned char *out, const int n);
__global__ void kDequantize(float *code, unsigned char *A, float *out, const int n);
template<typename T, int BLOCK_SIZE, int NUM_PER_TH, int STOCHASTIC> __global__ void kQuantizeBlockwise(float * code, T * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
template<typename T, int BLOCK_SIZE, int THREADS, int NUM_PER_TH> __global__ void kDequantizeBlockwise(float *code, unsigned char * A, float * absmax, T *out, const int n);
template<typename T, int BLOCK_SIZE, int NUM_PER_TH, int STOCHASTIC, int DATA_TYPE> __global__ void kQuantizeBlockwise(float * code, T * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
template<typename T, int BLOCK_SIZE, int THREADS, int NUM_PER_TH, int DATA_TYPE> __global__ void kDequantizeBlockwise(float *code, unsigned char * A, float * absmax, T *out, const int blocksize, const int n);
template<typename T, int OPTIMIZER, int BLOCK_SIZE, int NUM_VALS>
__global__ void kPreconditionOptimizer32bit2State(T* g, T* p,
......@@ -120,4 +122,9 @@ template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int T
template <int FORMAT> __global__ void kExtractOutliers(char *A, int *idx, char *out, int idx_size, int rowsA, int colsA, int tiledRowsA, int tiledColsA);
template <typename T, int BITS, int THREADS> __global__ void gemm_device(int M, int N, int K, T * __restrict__ const A, T* B, T * out, int lda, int ldb, int ldc);
template <typename T, int THREADS> __global__ void kgemm_4bit_inference(int M, int N, int K, T * __restrict__ const A, unsigned char *B, float *absmax, T * out, int lda, int ldb, int ldc, int blocksize);
template <typename T, int FUNC> __global__ void kfunc(T *A, T *B, T value, long n);
#endif
csrc/ops.cu
View file @ a24aae30
......@@ -50,54 +50,53 @@ void dequantize(float *code, unsigned char *A, float *out, int n)
CUDA_CHECK_RETURN(cudaPeekAtLastError());
}
template <typename T, int STOCHASTIC> void quantizeBlockwise(float * code, T *A, float *absmax, unsigned char *out, float *rand, int rand_offset, int blocksize, const int n)
template <typename T, int STOCHASTIC, int DATA_TYPE> void quantizeBlockwise(float * code, T *A, float *absmax, unsigned char *out, float *rand, int rand_offset, int blocksize, const int n)
{
int num_blocks = n/blocksize;
num_blocks = n % blocksize == 0 ? num_blocks : num_blocks + 1;
if(STOCHASTIC == 1)
assert(blocksize == 4096);
if(blocksize == 4096)
kQuantizeBlockwise<T, 4096, 4, STOCHASTIC><<<num_blocks, 1024>>>(code, A, absmax, out, rand, rand_offset, n);
kQuantizeBlockwise<T, 4096, 4, STOCHASTIC, 0><<<num_blocks, 1024>>>(code, A, absmax, out, rand, rand_offset, n);
else if(blocksize == 2048)
kQuantizeBlockwise<T, 2048, 4, 0><<<num_blocks, 512>>>(code, A, absmax, out, rand, rand_offset, n);
kQuantizeBlockwise<T, 2048, 4, 0, DATA_TYPE><<<num_blocks, 512>>>(code, A, absmax, out, rand, rand_offset, n);
else if(blocksize == 1024)
kQuantizeBlockwise<T, 1024, 4, 0><<<num_blocks, 256>>>(code, A, absmax, out, rand, rand_offset, n);
kQuantizeBlockwise<T, 1024, 4, 0, DATA_TYPE><<<num_blocks, 256>>>(code, A, absmax, out, rand, rand_offset, n);
else if(blocksize == 512)
kQuantizeBlockwise<T, 512, 2, 0><<<num_blocks, 256>>>(code, A, absmax, out, rand, rand_offset, n);
kQuantizeBlockwise<T, 512, 2, 0, DATA_TYPE><<<num_blocks, 256>>>(code, A, absmax, out, rand, rand_offset, n);
else if(blocksize == 256)
kQuantizeBlockwise<T, 256, 2, 0><<<num_blocks, 128>>>(code, A, absmax, out, rand, rand_offset, n);
kQuantizeBlockwise<T, 256, 2, 0, DATA_TYPE><<<num_blocks, 128>>>(code, A, absmax, out, rand, rand_offset, n);
else if(blocksize == 128)
kQuantizeBlockwise<T, 128, 2, 0><<<num_blocks, 64>>>(code, A, absmax, out, rand, rand_offset, n);
kQuantizeBlockwise<T, 128, 2, 0, DATA_TYPE><<<num_blocks, 64>>>(code, A, absmax, out, rand, rand_offset, n);
else if(blocksize == 64)
kQuantizeBlockwise<T, 64, 1, 0><<<num_blocks, 64>>>(code, A, absmax, out, rand, rand_offset, n);
kQuantizeBlockwise<T, 64, 2, 0, DATA_TYPE><<<num_blocks, 32>>>(code, A, absmax, out, rand, rand_offset, n);
CUDA_CHECK_RETURN(cudaPeekAtLastError());
}
template<typename T> void dequantizeBlockwise(float *code, unsigned char *A, float *absmax, T *out, int blocksize, const int n)
template<typename T, int DATA_TYPE> void dequantizeBlockwise(float *code, unsigned char *A, float *absmax, T *out, int blocksize, const int n)
{
int num_blocks = n/blocksize;
num_blocks = n % blocksize == 0 ? num_blocks : num_blocks + 1;
if(blocksize == 4096)
kDequantizeBlockwise<T, 4096, 1024, 4><<<num_blocks, 4096/4>>>(code, A, absmax, out, n);
else if(blocksize == 2048)
kDequantizeBlockwise<T, 2048, 512, 4><<<num_blocks, 2048/4>>>(code, A, absmax, out, n);
else if(blocksize == 1024)
kDequantizeBlockwise<T, 1024, 256, 4><<<num_blocks, 1024/4>>>(code, A, absmax, out, n);
else if(blocksize == 512)
kDequantizeBlockwise<T, 512, 256, 2><<<num_blocks, 512/2>>>(code, A, absmax, out, n);
else if(blocksize == 256)
kDequantizeBlockwise<T, 256, 128, 2><<<num_blocks, 256/2>>>(code, A, absmax, out, n);
else if(blocksize == 128)
kDequantizeBlockwise<T, 128, 64, 2><<<num_blocks, 128/2>>>(code, A, absmax, out, n);
else if(blocksize == 64)
kDequantizeBlockwise<T, 64, 64, 1><<<num_blocks, 64/1>>>(code, A, absmax, out, n);
int tile_size = (DATA_TYPE > 0) ? 1024 : 512;
if(DATA_TYPE > 0)
kDequantizeBlockwise<T, 512, 64, 8, DATA_TYPE><<<(n+tile_size-1)/tile_size, 64>>>(code, A, absmax, out, blocksize/2, n);
else
kDequantizeBlockwise<T, 512, 64, 8, DATA_TYPE><<<(n+tile_size-1)/tile_size, 64>>>(code, A, absmax, out, blocksize, n);
CUDA_CHECK_RETURN(cudaPeekAtLastError());
}
//void matmul4bite(half *A, unsigned char *B, half*out, int lda, int ldb, int rowsA, int colsA, int colsB)
//{
// int num_blocks = (colsB+32-1)/32;
// kMatmul_inference_4bit<NF4, half, half, half><<<num_blocks, 256>>>(A, B, out, lda, ldb, rowsA, colsA, colsB);
// CUDA_CHECK_RETURN(cudaPeekAtLastError());
//}
template<typename T, int OPTIMIZER> void optimizer32bit(T* g, T* p,
float* state1, float* state2, float *unorm, float max_unorm, float param_norm,
const float beta1, const float beta2, const float eps, const float weight_decay,
......@@ -683,10 +682,73 @@ template <int FORMAT> void extractOutliers(char * A, int *idx, char *out, int id
CUDA_CHECK_RETURN(cudaPeekAtLastError());
}
template <typename T> void gemm_host(int m, int n, int k, T * A, T* B, T * out, int lda, int ldb, int ldc, int bits)
{
int num_blocks = (m+31)/32;
//cout << num_blocks << endl;
//cout << lda << endl;
//cout << ldb << endl;
//cout << ldc << endl;
//cout << m << endl;
//cout << n << endl;
//cout << k << endl;
//if(bits == 32)
//gemm_device<T, 32, 128><<< num_blocks, 128, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
//gemm_device<T, 32, 32><<< num_blocks, 32, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
if(bits == 16)
//gemm_device<T, 16, 256><<< num_blocks, 256, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
gemm_device<T, 16, 160><<< num_blocks, 160, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
//gemm_device<T, 16, 128><<< num_blocks, 128, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
//gemm_device<T, 16, 96><<< num_blocks, 96, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
//gemm_device<T, 16, 32><<< num_blocks, 32, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
//gemm_device<T, 16, 64><<< num_blocks, 64, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
}
template <typename T> void gemm_4bit_inference(int m, int n, int k, T * A, unsigned char* B, float *absmax, T * out, int lda, int ldb, int ldc, int blocksize)
{
int num_blocks = (m+31)/32;
//cout << num_blocks << endl;
//cout << lda << endl;
//cout << ldb << endl;
//cout << ldc << endl;
//cout << m << endl;
//cout << n << endl;
//cout << k << endl;
kgemm_4bit_inference<T, 160><<< num_blocks, 160, 0, 0 >>>(m, n, k, A, B, absmax, out, lda, ldb, ldc, blocksize);
//kgemm_4bit_inference<T, 32><<< num_blocks, 32, 0, 0 >>>(m, n, k, A, B, absmax, out, lda, ldb, ldc, blocksize);
}
template <typename T, int FUNC> void func(T *A, T *B, T value, long n)
{
int threads = 512;
int blocks = n/threads;
blocks = n % threads == 0 ? blocks : blocks + 1;
blocks = blocks > 65535 ? 65535 : blocks;
kfunc<T, FUNC><<<blocks, 512>>>(A, B, value, n);
CUDA_CHECK_RETURN(cudaPeekAtLastError());
}
//==============================================================
// TEMPLATE DEFINITIONS
//==============================================================
template void func<float, FILL>(float *A, float *B, float value, long n);
template void func<unsigned char, FILL>(unsigned char *A, unsigned char *B, unsigned char value, long n);
template void func<float, ARANGE>(float *A, float *B, float value, long n);
template void func<float, _MUL>(float *A, float *B, float value, long n);
template void gemm_4bit_inference<half>(int m, int n, int k, half * A, unsigned char* B, float *absmax, half * out, int lda, int ldb, int ldc, int blocksize);
//template void gemm_host<float>(int m, int n, int k, float * A, float* B, float * out, int lda, int ldb, int ldc, int bits);
template void gemm_host<half>(int m, int n, int k, half * A, half* B, half * out, int lda, int ldb, int ldc, int bits);
template void extractOutliers<COL_TURING>(char * A, int *idx, char *out, int idx_size, int rows, int cols);
template void extractOutliers<COL_AMPERE>(char * A, int *idx, char *out, int idx_size, int rows, int cols);
......@@ -710,12 +772,20 @@ template void transformRowToFormat<COL_AMPERE, 1>(char * A, char *out, int rows,
template void estimateQuantiles(half *A, float *code, float offset, int n);
template void estimateQuantiles(float *A, float *code, float offset, int n);
template void quantizeBlockwise<half, 0>(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void quantizeBlockwise<float, 0>(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void quantizeBlockwise<half, 1>(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void quantizeBlockwise<float, 1>(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void dequantizeBlockwise<half>(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n);
template void dequantizeBlockwise<float>(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n);
template void quantizeBlockwise<half, 1, General8bit>(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void quantizeBlockwise<float, 1, General8bit>(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void quantizeBlockwise<half, 0, General8bit>(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void quantizeBlockwise<float, 0, General8bit>(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void quantizeBlockwise<half, 0, FP4>(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void quantizeBlockwise<float, 0, FP4>(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void quantizeBlockwise<half, 0, NF4>(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void quantizeBlockwise<float, 0, NF4>(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void dequantizeBlockwise<half, General8bit>(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n);
template void dequantizeBlockwise<float, General8bit>(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n);
template void dequantizeBlockwise<half, FP4>(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n);
template void dequantizeBlockwise<float, FP4>(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n);
template void dequantizeBlockwise<half, NF4>(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n);
template void dequantizeBlockwise<float, NF4>(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n);
#define MAKE_optimizer32bit(name, gtype) \
template void optimizer32bit<gtype, name>(gtype* g, gtype* p, \
......@@ -725,12 +795,14 @@ template void optimizer32bit<gtype, name>(gtype* g, gtype* p, \
MAKE_optimizer32bit(ADAM, half)
MAKE_optimizer32bit(ADAM, float)
MAKE_optimizer32bit(ADAM, __nv_bfloat16)
MAKE_optimizer32bit(MOMENTUM, half)
MAKE_optimizer32bit(MOMENTUM, float)
MAKE_optimizer32bit(RMSPROP, half)
MAKE_optimizer32bit(RMSPROP, float)
MAKE_optimizer32bit(LION, half)
MAKE_optimizer32bit(LION, float)
MAKE_optimizer32bit(LION, __nv_bfloat16)
MAKE_optimizer32bit(ADAGRAD, half)
MAKE_optimizer32bit(ADAGRAD, float)
......@@ -766,8 +838,11 @@ MAKE_optimizerStatic8bitBlockwise(half, RMSPROP);
MAKE_optimizerStatic8bitBlockwise(float, RMSPROP);
MAKE_optimizerStatic8bitBlockwise(half, LION);
MAKE_optimizerStatic8bitBlockwise(float, LION);
MAKE_optimizerStatic8bitBlockwise(__nv_bfloat16, LION);
MAKE_optimizerStatic8bitBlockwise(half, ADAGRAD);
MAKE_optimizerStatic8bitBlockwise(float, ADAGRAD);
template void percentileClipping(float * g, float *gnorm_vec, int step, const int n);
template void percentileClipping(half * g, float *gnorm_vec, int step, const int n);
MAKE_optimizerStatic8bitBlockwise(__nv_bfloat16, ADAM);
csrc/ops.cuh
View file @ a24aae30
......@@ -20,6 +20,11 @@
#include <vector>
#include <functional>
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#define CUDA_CHECK_RETURN(value) { \
cudaError_t _m_cudaStat = value; \
if (_m_cudaStat != cudaSuccess) { \
......@@ -82,6 +87,20 @@ typedef enum Transform_t
COL_AMPERE = 4,
} Transform_t;
typedef enum DataType_t
{
General8bit = 0,
FP4 = 1,
NF4 = 2,
} DataType_t;
typedef enum Funcs_t
{
FILL = 0,
ARANGE = 1,
_MUL = 2,
} Funcs_t;
class Context
{
public:
......@@ -129,8 +148,8 @@ template <typename T> void estimateQuantiles(T *A, float *code, float offset, in
void quantize(float *code, float *A, unsigned char *out, int n);
void dequantize(float *code, unsigned char *A, float *out, int n);
template <typename T, int STOCHASTIC> void quantizeBlockwise(float * code, T *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template<typename T> void dequantizeBlockwise(float *code, unsigned char *A, float *absmax, T *out, int block_size, const int n);
template <typename T, int STOCHASTIC, int DATA_TYPE> void quantizeBlockwise(float * code, T *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template<typename T, int DATA_TYPE> void dequantizeBlockwise(float *code, unsigned char *A, float *absmax, T *out, int block_size, const int n);
template<typename T, int OPTIMIZER> void optimizer32bit(T* g, T* p,
float* state1, float* state2, float *unorm, float max_unorm, float param_norm,
......@@ -177,4 +196,11 @@ template <typename T, int BITS> void spmm_coo_very_sparse_naive(int *max_count,
template <int FORMAT> void extractOutliers(char * A, int *idx, char *out, int idx_size, int rows, int cols);
void matmul4bite(half *A, unsigned char *B, half*out, int lda, int ldb, int rowsA, int colsA, int colsB);
template <typename T> void gemm_host(int m, int n, int k, T * A, T* B, T * out, int lda, int ldb, int ldc, int bits);
template <typename T> void gemm_4bit_inference(int m, int n, int k, T * A, unsigned char* B, float *absmax, T * out, int lda, int ldb, int ldc, int blocksize);
template <typename T, int FUNC> void func(T *A, T *B, T value, long n);
#endif
csrc/pythonInterface.c
View file @ a24aae30
......@@ -20,8 +20,25 @@ void estimateQuantiles_fp32(float *A, float *code, float offset, int n){ estimat
void estimateQuantiles_fp16(half *A, float *code, float offset, int n){ estimateQuantiles<half>(A, code, offset, n); }
//void gemm_host_fp32(int M, int N, int K, float * A, float* B, float * out, int lda, int ldb, int ldc)
//{ gemm_host<float>(M, N, K, A, B, out, lda, ldb, ldc, 32); }
void gemm_host_fp16(int M, int N, int K, half * A, half* B, half * out, int lda, int ldb, int ldc)
{ gemm_host<half>(M, N, K, A, B, out, lda, ldb, ldc, 16); }
void gemm_4bit_inference(int m, int n, int k, half * A, unsigned char* B, float *absmax, half * out, int lda, int ldb, int ldc, int blocksize)
{ gemm_4bit_inference<half>(m, n, k, A, B, absmax, out, lda, ldb, ldc, blocksize); }
#define MAKE_ELEMENTWISE_FUNC(fname, type_name, ctype, FUNC) \
void fname##_##type_name(ctype *A, ctype *B, ctype value, long n){ func<ctype, FUNC>(A, B, value, n); } \
MAKE_ELEMENTWISE_FUNC(fill, fp32, float, FILL)
MAKE_ELEMENTWISE_FUNC(fill, uint8, unsigned char, FILL)
MAKE_ELEMENTWISE_FUNC(arange, fp32, float, ARANGE)
MAKE_ELEMENTWISE_FUNC(_mul, fp32, float, _MUL)
#define MAKE_FUNC32(fname, oname, gtype, gbits) \
void fname##32bit_g##gbits(gtype *g, gtype *p, \
void fname##32bit_grad_##gbits(gtype *g, gtype *p, \
float* state1, float* state2, float *unorm, float max_unorm, float param_norm, \
const float beta1, const float beta2, const float eps, const float weight_decay, \
const int step, const float lr, float gnorm_scale, bool skip_zeros, const int n) \
......@@ -29,17 +46,19 @@ void fname##32bit_g##gbits(gtype *g, gtype *p, \
MAKE_FUNC32(momentum, MOMENTUM, float, 32)
MAKE_FUNC32(momentum, MOMENTUM, half, 16)
MAKE_FUNC32(adam, ADAM, float, 32)
MAKE_FUNC32(adam, ADAM, half, 16)
MAKE_FUNC32(adam, ADAM, float, fp32)
MAKE_FUNC32(adam, ADAM, half, fp16)
MAKE_FUNC32(adam, ADAM, __nv_bfloat16, bf16)
MAKE_FUNC32(rmsprop, RMSPROP, float, 32)
MAKE_FUNC32(rmsprop, RMSPROP, half, 16)
MAKE_FUNC32(lion, LION, float, 32)
MAKE_FUNC32(lion, LION, half, 16)
MAKE_FUNC32(lion, LION, float, fp32)
MAKE_FUNC32(lion, LION, half, fp16)
MAKE_FUNC32(lion, LION, __nv_bfloat16, bf16)
MAKE_FUNC32(adagrad, ADAGRAD, float, 32)
MAKE_FUNC32(adagrad, ADAGRAD, half, 16)
#define MAKE_FUNC8(fname, oname, gtype, gbits) \
void fname##_static_8bit_g##gbits(gtype* p, gtype* g, unsigned char* state1, unsigned char* state2, \
void fname##_static_8bit_grad_##gbits(gtype* p, gtype* g, unsigned char* state1, unsigned char* state2, \
float *unorm, float max_unorm, float param_norm, \
float beta1, float beta2, \
float eps, int step, float lr, \
......@@ -61,33 +80,42 @@ MAKE_FUNC8(lion, LION, float, 32)
MAKE_FUNC8(lion, LION, half, 16)
#define MAKE_BLOCKWISE8(fname, optim_name, gtype, gbits) \
void fname##_8bit_blockwise_fp##gbits(gtype* p, gtype* g, \
void fname##_8bit_blockwise_grad_##gbits(gtype* p, gtype* g, \
unsigned char* state1, unsigned char* state2, float beta1, float beta2, float eps, int step, float lr, \
float* quantiles1, float* quantiles2, float* absmax1, float* absmax2, float weight_decay, const float gnorm_scale, bool skip_zeros, int n)\
{ optimizerStatic8bitBlockwise<gtype, optim_name>(p, g, state1, state2, beta1, beta2, eps, step, lr, quantiles1, quantiles2, absmax1, absmax2, weight_decay, gnorm_scale, skip_zeros, n); }\
MAKE_BLOCKWISE8(adam, ADAM, half, 16)
MAKE_BLOCKWISE8(adam, ADAM, float, 32)
MAKE_BLOCKWISE8(momentum, MOMENTUM, half, 16)
MAKE_BLOCKWISE8(momentum, MOMENTUM, float, 32)
MAKE_BLOCKWISE8(rmsprop, RMSPROP, half, 16)
MAKE_BLOCKWISE8(rmsprop, RMSPROP, float, 32)
MAKE_BLOCKWISE8(lion, LION, half, 16)
MAKE_BLOCKWISE8(lion, LION, float, 32)
MAKE_BLOCKWISE8(adagrad, ADAGRAD, half, 16)
MAKE_BLOCKWISE8(adagrad, ADAGRAD, float, 32)
MAKE_BLOCKWISE8(adam, ADAM, half, fp16)
MAKE_BLOCKWISE8(adam, ADAM, float, fp32)
MAKE_BLOCKWISE8(momentum, MOMENTUM, half, fp16)
MAKE_BLOCKWISE8(momentum, MOMENTUM, float, fp32)
MAKE_BLOCKWISE8(rmsprop, RMSPROP, half, fp16)
MAKE_BLOCKWISE8(rmsprop, RMSPROP, float, fp32)
MAKE_BLOCKWISE8(adagrad, ADAGRAD, half, fp16)
MAKE_BLOCKWISE8(adagrad, ADAGRAD, float, fp32)
MAKE_BLOCKWISE8(adam, ADAM, __nv_bfloat16, bf16)
MAKE_BLOCKWISE8(lion, LION, half, fp16)
MAKE_BLOCKWISE8(lion, LION, float, fp32)
MAKE_BLOCKWISE8(lion, LION, __nv_bfloat16, bf16)
void percentileClipping_g32(float * g, float *gnorm_vec, int step, const int n){ percentileClipping<float>(g, gnorm_vec, step, n); }
void percentileClipping_g16(half * g, float *gnorm_vec, int step, const int n){ percentileClipping<half>(g, gnorm_vec, step, n); }
void quantizeBlockwise_fp16(float * code, half *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<half, 0>(code, A, absmax, out, NULL, 0, blocksize, n); }
void quantizeBlockwise_fp32(float * code, float *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<float, 0>(code, A, absmax, out, NULL, 0, blocksize, n); }
void quantizeBlockwise_stochastic_fp16(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, const int n){ quantizeBlockwise<half, 1>(code, A, absmax, out, rand, rand_offset, 4096, n); }
void quantizeBlockwise_stochastic_fp32(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, const int n){ quantizeBlockwise<float, 1>(code, A, absmax, out, rand, rand_offset, 4096, n); }
void quantizeBlockwise_fp16(float * code, half *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<half, 0, General8bit>(code, A, absmax, out, NULL, 0, blocksize, n); }
void quantizeBlockwise_fp32(float * code, float *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<float, 0, General8bit>(code, A, absmax, out, NULL, 0, blocksize, n); }
void quantizeBlockwise_fp16_fp4(float * code, half *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<half, 0, FP4>(NULL, A, absmax, out, NULL, 0, blocksize, n); }
void quantizeBlockwise_fp32_fp4(float * code, float *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<float, 0, FP4>(NULL, A, absmax, out, NULL, 0, blocksize, n); }
void quantizeBlockwise_fp16_nf4(float * code, half *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<half, 0, NF4>(NULL, A, absmax, out, NULL, 0, blocksize, n); }
void quantizeBlockwise_fp32_nf4(float * code, float *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<float, 0, NF4>(NULL, A, absmax, out, NULL, 0, blocksize, n); }
void dequantizeBlockwise_fp16(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n){ dequantizeBlockwise<half, General8bit>(code, A, absmax, out, blocksize, n); } \
void dequantizeBlockwise_fp32(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n){ dequantizeBlockwise<float, General8bit>(code, A, absmax, out, blocksize, n); }
void dequantizeBlockwise_fp16_fp4(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n){ dequantizeBlockwise<half, FP4>(NULL, A, absmax, out, blocksize, n); } \
void dequantizeBlockwise_fp32_fp4(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n){ dequantizeBlockwise<float, FP4>(NULL, A, absmax, out, blocksize, n); }
void dequantizeBlockwise_fp16_nf4(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n){ dequantizeBlockwise<half, NF4>(NULL, A, absmax, out, blocksize, n); } \
void dequantizeBlockwise_fp32_nf4(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n){ dequantizeBlockwise<float, NF4>(NULL, A, absmax, out, blocksize, n); }
void dequantizeBlockwise_fp16(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n){ dequantizeBlockwise<half>(code, A, absmax, out, blocksize, n); } \
void dequantizeBlockwise_fp32(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n){ dequantizeBlockwise<float>(code, A, absmax, out, blocksize, n); }
#define MAKE_FUNC_TRANSFORM(fbits, fsrc, ftrgt, ftranspose, dtype, src, target, transpose, bits) \
void transform_##fbits##_##fsrc##_to_##ftrgt##_##ftranspose(cublasLtHandle_t ltHandle, dtype *A, dtype *out, int dim1, int dim2) \
......@@ -148,32 +176,41 @@ extern "C"
void cdequantize(float *code, unsigned char *A, float *out, int n){ dequantize(code, A, out, n); }
void cquantize_blockwise_fp16(float * code, half *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise_fp16(code, A, absmax, out, blocksize, n); }
void cquantize_blockwise_fp32(float * code, float *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise_fp32(code, A, absmax, out, blocksize, n); }
void cquantize_blockwise_stochastic_fp16(float * code, half *A, float *absmax, unsigned char *out, float *rand, int rand_offset, const int n){ quantizeBlockwise_stochastic_fp16(code, A, absmax, out, rand, rand_offset, n); }
void cquantize_blockwise_stochastic_fp32(float * code, float *A, float *absmax, unsigned char *out, float *rand, int rand_offset, const int n){ quantizeBlockwise_stochastic_fp32(code, A, absmax, out, rand, rand_offset, n); }
void cdequantize_blockwise_fp16(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n){ dequantizeBlockwise_fp16(code, A, absmax, out, blocksize, n); }
void cdequantize_blockwise_fp32(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n){ dequantizeBlockwise_fp32(code, A, absmax, out, blocksize, n); }
void cquantize_blockwise_fp16_fp4(float * code, half *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise_fp16_fp4(code, A, absmax, out, blocksize, n); }
void cquantize_blockwise_fp32_fp4(float * code, float *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise_fp32_fp4(code, A, absmax, out, blocksize, n); }
void cdequantize_blockwise_fp16_fp4(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n){ dequantizeBlockwise_fp16_fp4(code, A, absmax, out, blocksize, n); }
void cdequantize_blockwise_fp32_fp4(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n){ dequantizeBlockwise_fp32_fp4(code, A, absmax, out, blocksize, n); }
void cquantize_blockwise_fp16_nf4(float * code, half *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise_fp16_nf4(code, A, absmax, out, blocksize, n); }
void cquantize_blockwise_fp32_nf4(float * code, float *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise_fp32_nf4(code, A, absmax, out, blocksize, n); }
void cdequantize_blockwise_fp16_nf4(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n){ dequantizeBlockwise_fp16_nf4(code, A, absmax, out, blocksize, n); }
void cdequantize_blockwise_fp32_nf4(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n){ dequantizeBlockwise_fp32_nf4(code, A, absmax, out, blocksize, n); }
#define MAKE_CFUNC32(name, gtype, gbits) \
void c##name##32bit_g##gbits(gtype *g, gtype *p, \
void c##name##32bit_grad_##gbits(gtype *g, gtype *p, \
float* state1, float* state2, float *unorm, float max_unorm, float param_norm, \
const float beta1, const float beta2, const float eps, const float weight_decay, \
const int step, const float lr, const float gnorm_scale, bool skip_zeros, const int n) \
{ name##32bit_g##gbits(g, p, state1, state2, unorm, max_unorm, param_norm, beta1, beta2, eps, weight_decay, step, lr, gnorm_scale, skip_zeros, n); } \
{ name##32bit_grad_##gbits(g, p, state1, state2, unorm, max_unorm, param_norm, beta1, beta2, eps, weight_decay, step, lr, gnorm_scale, skip_zeros, n); } \
MAKE_CFUNC32(adam, float, 32)
MAKE_CFUNC32(adam, half, 16)
MAKE_CFUNC32(adam, float, fp32)
MAKE_CFUNC32(adam, half, fp16)
MAKE_CFUNC32(adam, __nv_bfloat16, bf16)
MAKE_CFUNC32(momentum, float, 32)
MAKE_CFUNC32(momentum, half, 16)
MAKE_CFUNC32(rmsprop, float, 32)
MAKE_CFUNC32(rmsprop, half, 16)
MAKE_CFUNC32(lion, float, 32)
MAKE_CFUNC32(lion, half, 16)
MAKE_CFUNC32(lion, float, fp32)
MAKE_CFUNC32(lion, half, fp16)
MAKE_CFUNC32(lion, __nv_bfloat16, bf16)
MAKE_CFUNC32(adagrad, float, 32)
MAKE_CFUNC32(adagrad, half, 16)
#define MAKE_CFUNC8(name, gtype, gbits) \
void c##name##_static_8bit_g##gbits(gtype* p, gtype* g, unsigned char* state1, unsigned char* state2, \
void c##name##_static_8bit_grad_##gbits(gtype* p, gtype* g, unsigned char* state1, unsigned char* state2, \
float *unorm, float max_unorm, float param_norm, \
float beta1, float beta2, \
float eps, int step, float lr, \
......@@ -181,7 +218,7 @@ extern "C"
float* max1, float* max2, float* new_max1, float* new_max2, \
float weight_decay, float gnorm_scale, int n) \
{ \
name##_static_8bit_g##gbits(g, p, state1, state2, unorm, max_unorm, param_norm, beta1, beta2, eps, step, lr, \
name##_static_8bit_grad_##gbits(g, p, state1, state2, unorm, max_unorm, param_norm, beta1, beta2, eps, step, lr, \
quantiles1, quantiles2, max1, max2, new_max1, new_max2, weight_decay, gnorm_scale, n); \
} \
......@@ -195,22 +232,23 @@ extern "C"
MAKE_CFUNC8(lion, half, 16)
#define MAKE_CBLOCKWISE8(fname, optim_name, gtype, gbits) \
void c##fname##_8bit_blockwise_fp##gbits(gtype* p, gtype* g, \
void c##fname##_8bit_blockwise_grad_##gbits(gtype* p, gtype* g, \
unsigned char* state1, unsigned char* state2, float beta1, float beta2, float eps, int step, float lr, \
float* quantiles1, float* quantiles2, float* absmax1, float* absmax2, float weight_decay, const float gnorm_scale, bool skip_zeros, int n) \
{ fname##_8bit_blockwise_fp##gbits(p, g, state1, state2, beta1, beta2, eps, step, lr, quantiles1, quantiles2, absmax1, absmax2, weight_decay, gnorm_scale, skip_zeros, n); } \
MAKE_CBLOCKWISE8(adam, ADAM, half, 16)
MAKE_CBLOCKWISE8(adam, ADAM, float, 32)
MAKE_CBLOCKWISE8(momentum, MOMENTUM, half, 16)
MAKE_CBLOCKWISE8(momentum, MOMENTUM, float, 32)
MAKE_CBLOCKWISE8(rmsprop, RMSPROP, half, 16)
MAKE_CBLOCKWISE8(rmsprop, RMSPROP, float, 32)
MAKE_CBLOCKWISE8(lion, LION, half, 16)
MAKE_CBLOCKWISE8(lion, LION, float, 32)
MAKE_CBLOCKWISE8(adagrad, ADAGRAD, half, 16)
MAKE_CBLOCKWISE8(adagrad, ADAGRAD, float, 32)
{ fname##_8bit_blockwise_grad_##gbits(p, g, state1, state2, beta1, beta2, eps, step, lr, quantiles1, quantiles2, absmax1, absmax2, weight_decay, gnorm_scale, skip_zeros, n); } \
MAKE_CBLOCKWISE8(adam, ADAM, half, fp16)
MAKE_CBLOCKWISE8(adam, ADAM, float, fp32)
MAKE_CBLOCKWISE8(momentum, MOMENTUM, half, fp16)
MAKE_CBLOCKWISE8(momentum, MOMENTUM, float, fp32)
MAKE_CBLOCKWISE8(rmsprop, RMSPROP, half, fp16)
MAKE_CBLOCKWISE8(rmsprop, RMSPROP, float, fp32)
MAKE_CBLOCKWISE8(adagrad, ADAGRAD, half, fp16)
MAKE_CBLOCKWISE8(adagrad, ADAGRAD, float, fp32)
MAKE_CBLOCKWISE8(adam, ADAM, __nv_bfloat16, bf16)
MAKE_CBLOCKWISE8(lion, LION, half, fp16)
MAKE_CBLOCKWISE8(lion, LION, float, fp32)
MAKE_CBLOCKWISE8(lion, LION, __nv_bfloat16, bf16)
void cpercentile_clipping_g32(float * g, float *gnorm_vec, int step, const int n){ percentileClipping_g32(g, gnorm_vec, step, n); }
void cpercentile_clipping_g16(half * g, float *gnorm_vec, int step, const int n){ percentileClipping_g16(g, gnorm_vec, step, n); }
......@@ -298,6 +336,38 @@ extern "C"
void cextractOutliers_turing(char * A, int *idx, char *out, int idx_size, int rows, int cols){ extractOutliers_turing(A, idx, out, idx_size, rows, cols); }
void cextractOutliers_ampere(char * A, int *idx, char *out, int idx_size, int rows, int cols){ extractOutliers_ampere(A, idx, out, idx_size, rows, cols); }
//void cgemm_host_fp32(int M, int N, int K, float * A, float* B, float * out, int lda, int ldb, int ldc)
//{ gemm_host_fp32(M, N, K, A, B, out, lda, ldb, ldc); }
void cgemm_host_fp16(int M, int N, int K, half * A, half* B, half * out, int lda, int ldb, int ldc)
{ gemm_host_fp16(M, N, K, A, B, out, lda, ldb, ldc); }
void cgemm_4bit_inference(int m, int n, int k, half * A, unsigned char* B, float *absmax, half * out, int lda, int ldb, int ldc, int blocksize)
{ gemm_4bit_inference(m, n, k, A, B, absmax, out, lda, ldb, ldc, blocksize); }
void *cget_managed_ptr(size_t bytes)
{
void *ptr;
CUDA_CHECK_RETURN(cudaMallocManaged(&ptr, bytes, cudaMemAttachHost));
CUDA_CHECK_RETURN(cudaPeekAtLastError());
return ptr;
}
void cprefetch(void *ptr, size_t bytes, int device)
{
CUDA_CHECK_RETURN(cudaMemPrefetchAsync(ptr, bytes, device, 0));
CUDA_CHECK_RETURN(cudaPeekAtLastError());
}
#define CMAKE_ELEMENTWISE_FUNC(fname, type_name, ctype, FUNC) \
void c##fname##_##type_name(ctype *A, ctype *B, ctype value, long n){ fname##_##type_name(A, B, value, n); } \
CMAKE_ELEMENTWISE_FUNC(fill, fp32, float, FILL)
CMAKE_ELEMENTWISE_FUNC(fill, uint8, unsigned char, FILL)
CMAKE_ELEMENTWISE_FUNC(arange, fp32, float, ARANGE)
CMAKE_ELEMENTWISE_FUNC(_mul, fp32, float, _MUL)
#endif
void cquantize_blockwise_cpu_fp32(float *code, float *A, float *absmax, unsigned char *out, long long blocksize, long long n){ quantize_cpu(code, A, absmax, out, blocksize, n); }
void cdequantize_blockwise_cpu_fp32(float *code, unsigned char *A, float *absmax, float *out, long long blocksize, long long n){ dequantize_cpu(code, A, absmax, out, blocksize, n); }
......
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