File size: 6,542 Bytes
dc9bb20 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 | import torch
import triton
import triton.language as tl
# @triton.autotune(
# configs=[
# # triton.Config({'SPLIT_SIZE': 64}),
# # triton.Config({'SPLIT_SIZE': 64, 'grf_mode': 'large'}, num_stages=2, num_warps=32),
# # triton.Config({'SPLIT_SIZE': 64, 'grf_mode': 'auto'}, num_stages=2, num_warps=32),
# # triton.Config({'SPLIT_SIZE': 64, 'grf_mode': 'large'}, num_stages=4, num_warps=32),
# # triton.Config({'SPLIT_SIZE': 64, 'grf_mode': 'auto'}, num_stages=4, num_warps=32),
# # triton.Config({'SPLIT_SIZE': 128}),
# # triton.Config({'SPLIT_SIZE': 128, 'grf_mode': 'large'}, num_stages=2, num_warps=32),
# # triton.Config({'SPLIT_SIZE': 128, 'grf_mode': 'auto'}, num_stages=2, num_warps=32),
# # triton.Config({'SPLIT_SIZE': 128, 'grf_mode': 'large'}, num_stages=4, num_warps=32),
# # triton.Config({'SPLIT_SIZE': 128, 'grf_mode': 'auto'}, num_stages=4, num_warps=32),
# triton.Config({"SPLIT_SIZE": 256}),
# # triton.Config({'SPLIT_SIZE': 256, 'grf_mode': 'large'}, num_stages=2, num_warps=32),
# # triton.Config({'SPLIT_SIZE': 256, 'grf_mode': 'auto'}, num_stages=2, num_warps=32),
# triton.Config({"SPLIT_SIZE": 512}),
# # triton.Config({'SPLIT_SIZE': 1024}),
# ],
# key=["num_paired_elements", "QUANT_BLOCK"],
# )
@triton.jit
def dequant_8bit_kernel(
a_ptr,
out_ptr,
code_ptr,
absmax_ptr,
n,
QUANT_BLOCK: tl.constexpr,
SPLIT_SIZE: tl.constexpr,
):
pid = tl.program_id(axis=0)
block_start = pid * SPLIT_SIZE
offsets = block_start + tl.arange(0, SPLIT_SIZE)
mask = offsets < n
out_dq = dequant_8bit_blockwise_kernel_util(a_ptr, offsets, code_ptr, absmax_ptr, mask, QUANT_BLOCK)
tl.store(out_ptr + offsets, out_dq, mask)
def dequant_8bit_blockwise(
a: torch.Tensor,
absmax: torch.Tensor,
quant_state_code: torch.Tensor,
quant_blocksize: int = 64,
dtype: torch.dtype = None,
out: torch.Tensor = None,
):
n = a.numel()
if out is None:
if dtype is None:
raise ValueError("If out is None, dtype must be specified")
out = torch.empty_like(a, dtype=dtype, device=a.device)
SPLIT_SIZE = 256
# grid = lambda META: (triton.cdiv(number_of_paired_elements, META["SPLIT_SIZE"]),)
grid = (triton.cdiv(n, SPLIT_SIZE),)
dequant_8bit_kernel[grid](
a,
out,
quant_state_code,
absmax,
n,
quant_blocksize,
SPLIT_SIZE,
)
return out
# @triton.autotune(
# configs=[
# triton.Config({"SPLIT_NUM_BLOCKS": 1, "grf_mode": "auto"}, num_stages=4, num_warps=32),
# triton.Config({"SPLIT_NUM_BLOCKS": 2, "grf_mode": "auto"}, num_stages=4, num_warps=32),
# triton.Config({"SPLIT_NUM_BLOCKS": 1}),
# triton.Config({"SPLIT_NUM_BLOCKS": 2}),
# ],
# key=["n_elements"],
# )
@triton.jit
def quantize_8bit_blockwise_kernel(
A_ptr,
code_ptr,
absmax_ptr,
out_ptr,
n_elements,
BLOCK_SIZE: tl.constexpr,
CODE_SIZE: tl.constexpr,
SPLIT_NUM_BLOCKS: tl.constexpr,
):
block_start_idx = tl.program_id(0) * SPLIT_NUM_BLOCKS
thread_idx = tl.arange(0, SPLIT_NUM_BLOCKS * BLOCK_SIZE)
offsets = block_start_idx * BLOCK_SIZE + thread_idx
mask = offsets < n_elements
A = tl.load(A_ptr + offsets, mask=mask, other=0.0)
quantized, absmax = quantize_8bit_blockwise_kernel_util(A, code_ptr, CODE_SIZE, BLOCK_SIZE, SPLIT_NUM_BLOCKS)
tl.store(absmax_ptr + block_start_idx + tl.arange(0, SPLIT_NUM_BLOCKS), absmax)
tl.store(out_ptr + offsets, quantized, mask=mask)
def quantize_blockwise_triton(A, code, blocksize, absmax=None, out=None):
n = A.numel()
blocks = -(n // -blocksize)
if absmax is None:
absmax = torch.empty((blocks,), device=A.device, dtype=A.dtype)
if out is None:
out = torch.empty_like(A.flatten(), dtype=torch.uint8)
split_num_blocks = 1
grid = (triton.cdiv(blocks, split_num_blocks),)
# grid = lambda META: (triton.cdiv(blocks, META["SPLIT_NUM_BLOCKS"]),)
quantize_8bit_blockwise_kernel[grid](
A_ptr=A,
code_ptr=code,
absmax_ptr=absmax,
out_ptr=out,
n_elements=n,
BLOCK_SIZE=blocksize,
CODE_SIZE=code.numel(),
SPLIT_NUM_BLOCKS=split_num_blocks,
# num_warps=1,
# num_stages=2,
)
out = out.reshape(A.shape)
return out, absmax
@triton.jit
def quantize_8bit_blockwise_kernel_util(
a,
code_ptr,
CODE_SIZE: tl.constexpr,
BLOCK_SIZE: tl.constexpr,
N_PER_TH: tl.constexpr,
):
# To be able process several blocks -> (BLOCK_SIZE, SPLIT_NUM_BLOCKS)
a_reshaped = tl.reshape(a, (N_PER_TH, BLOCK_SIZE))
# Calculating absmax for each block
absmax = tl.max(tl.abs(a_reshaped), axis=1)
a_normalized = a_reshaped / absmax[:, None]
a_normalized = tl.clamp(a_normalized, -1.0, 1.0)
lower_pivot = tl.zeros((N_PER_TH, BLOCK_SIZE), dtype=tl.int32)
upper_pivot = tl.full((N_PER_TH, BLOCK_SIZE), CODE_SIZE - 1, dtype=tl.int32)
# ceil(log2(code_size)) = 8, actually, in general case should be input parameter
for _ in range(8):
pivot = (lower_pivot + upper_pivot) // 2
val = tl.load(code_ptr + pivot)
is_higher = a_normalized > val # code[pivot]
lower_pivot = tl.where(is_higher, pivot, lower_pivot)
upper_pivot = tl.where(is_higher, upper_pivot, pivot)
# Choose closest level
lower_val = tl.load(code_ptr + lower_pivot)
upper_val = tl.load(code_ptr + upper_pivot)
lower_dist = tl.abs(a_normalized - lower_val)
upper_dist = tl.abs(a_normalized - upper_val)
quantized = tl.where(lower_dist <= upper_dist, lower_pivot, upper_pivot).to(tl.uint8)
# too slow approach
# diff = tl.abs(A_normalized[:, :, None] - code[None, None, :])
# quantized = tl.argmin(diff, axis=2).to(tl.uint8)
quantized_flat = tl.reshape(quantized, (BLOCK_SIZE * N_PER_TH,))
return quantized_flat, absmax
@triton.jit
def dequant_8bit_blockwise_kernel_util(
a_ptr,
offsets,
code_ptr,
absmax_ptr,
mask,
BLOCK_SIZE: tl.constexpr,
):
a = tl.load(a_ptr + offsets, mask, other=0).to(tl.uint8)
scaled_int8 = tl.load(code_ptr + a, mask)
# Load scales
absmax_offsets = offsets // BLOCK_SIZE
absmax = tl.load(absmax_ptr + absmax_offsets, mask=mask, other=0.0, eviction_policy="evict_last")
# Apply scales
out_dq = scaled_int8 * absmax
return out_dq
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