49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from math import prod
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from typing import List, Optional, Tuple
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import torch
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from vllm import _custom_ops as ops
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from vllm.model_executor.layers.quantization.utils.fp8_utils import (
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per_token_group_quant_fp8)
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from vllm.utils import cdiv
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def _resize_cache(x: torch.Tensor, v: Tuple[int, ...]) -> torch.Tensor:
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"""
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Shrink the given tensor and apply the given view to it. This is
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used to resize the intermediate fused_moe caches.
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"""
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assert prod(v) <= x.numel()
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return x.flatten()[:prod(v)].view(*v)
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def _fp8_quantize(
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A: torch.Tensor,
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A_scale: Optional[torch.Tensor],
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block_shape: Optional[List[int]],
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Perform fp8 quantization on the inputs. If a block_shape
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is provided, the output will be blocked.
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"""
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if block_shape is None:
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A, A_scale = ops.scaled_fp8_quant(A, A_scale)
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else:
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assert len(block_shape) == 2
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_, block_k = block_shape[0], block_shape[1]
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A, A_scale = per_token_group_quant_fp8(A, block_k)
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assert cdiv(A.shape[-1], block_k) == A_scale.shape[-1]
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return A, A_scale
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def _fp8_perm(m: torch.Tensor, idx: torch.Tensor) -> torch.Tensor:
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"""
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A permutation routine that works on fp8 types.
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"""
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if torch.is_floating_point(m) and torch.finfo(m.dtype).bits == 8:
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return m.view(dtype=torch.uint8)[idx, ...].view(dtype=m.dtype)
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else:
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return m[idx, ...]
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