69 lines
2.3 KiB
Python
69 lines
2.3 KiB
Python
import math
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import torch
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import time
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from bitsandbytes.triton.triton_utils import is_triton_available
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if not is_triton_available():
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def quantize_rowwise(x: torch.Tensor): return None
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else:
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import triton
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import triton.language as tl
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from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
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# rowwise quantize
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# TODO: autotune this better.
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@triton.autotune(
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configs=[
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triton.Config({}, num_stages=1, num_warps=8),
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triton.Config({}, num_stages=2, num_warps=8),
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triton.Config({}, num_stages=4, num_warps=8),
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triton.Config({}, num_stages=8, num_warps=8),
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triton.Config({}, num_stages=1),
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triton.Config({}, num_stages=2),
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triton.Config({}, num_stages=4),
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triton.Config({}, num_stages=8),
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triton.Config({}, num_warps=1),
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triton.Config({}, num_warps=2),
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triton.Config({}, num_warps=4),
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triton.Config({}, num_warps=8),
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],
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key=['n_elements']
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)
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@triton.jit
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def _quantize_rowwise(
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x_ptr,
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output_ptr,
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output_maxs,
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n_elements,
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BLOCK_SIZE: tl.constexpr,
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P2: tl.constexpr,
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):
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pid = tl.program_id(axis=0)
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block_start = pid * BLOCK_SIZE
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arange = tl.arange(0, P2)
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offsets = block_start + arange
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row_mask = arange < BLOCK_SIZE
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x = tl.load(x_ptr + offsets, mask=row_mask)
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abs_x = tl.abs(x)
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max_val = tl.max(tl.where(row_mask, abs_x, 0), axis=0)
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output = tl.libdevice.llrint(127. * (x / max_val))
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tl.store(output_ptr + offsets, output, mask=row_mask)
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tl.store(output_maxs + pid, max_val)
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def quantize_rowwise(x: torch.Tensor):
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output = torch.empty(*x.shape, device=x.device, dtype=torch.int8)
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output_maxs = torch.empty(x.shape[0], device=x.device, dtype=torch.float16)
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P2 = int(2 ** (math.ceil(math.log2(x.shape[1]))))
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assert x.is_cuda and output.is_cuda
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n_elements = output.numel()
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grid = lambda meta: (x.shape[0],)
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_quantize_rowwise[grid](x, output, output_maxs, n_elements, BLOCK_SIZE=x.shape[1], P2=P2)
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return output, output_maxs
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