wehub-resource-sync
153 lines
5.1 KiB
Python
153 lines
5.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import torch
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from vllm._custom_ops import scaled_fp4_quant
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from vllm.scalar_type import scalar_types
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FLOAT4_E2M1_MAX = scalar_types.float4_e2m1f.max()
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FLOAT8_E4M3_MAX = torch.finfo(torch.float8_e4m3fn).max
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kE2M1ToFloat = torch.tensor(
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[0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0], dtype=torch.float32
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)
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def convert_swizzled_to_linear(a_sf_swizzled: torch.Tensor, m, k, block_size):
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m_tiles = (m + 128 - 1) // 128
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f = block_size * 4
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k_tiles = (k + f - 1) // f
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tmp = torch.reshape(a_sf_swizzled, (1, m_tiles, k_tiles, 32, 4, 4))
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tmp = torch.permute(tmp, (0, 1, 4, 3, 2, 5))
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out = tmp.reshape(m_tiles * 128, k_tiles * f // block_size)
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return out[0:m, 0:k]
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def convert_swizzled_8x4_layout_to_linear(
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a_sf_swizzled: torch.Tensor, m, k, block_size
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):
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m_tiles = (m + 8 - 1) // 8
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f = block_size * 4
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k_tiles = (k + f - 1) // f
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tmp = torch.reshape(a_sf_swizzled, (1, m_tiles, k_tiles, 8, 4))
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tmp = torch.permute(tmp, (0, 1, 3, 2, 4))
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out = tmp.reshape(m_tiles * 8, k_tiles * f // block_size)
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return out[0:m, 0:k]
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def dequantize_nvfp4_to_dtype(
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tensor_fp4,
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tensor_sf,
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global_scale,
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dtype,
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device,
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block_size=16,
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is_sf_128x4_layout=True,
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):
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"""Dequantize the fp4 tensor back to high precision."""
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# Two fp4 values are packed into one uint8.
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assert tensor_fp4.dtype == torch.uint8
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m, packed_k = tensor_fp4.shape
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k = packed_k * 2
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tensor_f32 = break_fp4_bytes(tensor_fp4, dtype)
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tensor_f32 = tensor_f32.reshape(m, k // block_size, block_size)
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tensor_sf = tensor_sf.view(torch.float8_e4m3fn)
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if is_sf_128x4_layout:
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tensor_sf = convert_swizzled_to_linear(tensor_sf, m, k, block_size)
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else:
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tensor_sf = convert_swizzled_8x4_layout_to_linear(tensor_sf, m, k, block_size)
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tensor_sf_dtype = tensor_sf.to(torch.float32) / global_scale
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# scale the tensor
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out = (tensor_f32 * tensor_sf_dtype.unsqueeze(-1)).reshape(m, k)
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return out.to(dtype=dtype)
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def break_fp4_bytes(a, dtype):
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assert a.dtype == torch.uint8
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m, n = a.shape
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# Vectorized nibble processing
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a_flat = a.flatten()
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high = (a_flat & 0xF0) >> 4 # Upper nibbles
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low = a_flat & 0x0F # Lower nibbles
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# Combine nibbles for batch processing
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combined = torch.stack((low, high), dim=1).flatten()
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# Vectorized sign and magnitude extraction
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signs = (combined & 0x08).to(torch.bool) # Sign bits
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abs_vals = (combined & 0x07).to(torch.long) # Magnitude indices
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# Device-aware lookup and sign application
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kE2M1 = kE2M1ToFloat.to(device=a.device)
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values = kE2M1[abs_vals] * torch.where(signs, -1.0, 1.0)
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# Reshape to final form
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return values.reshape(m, n * 2).to(dtype=dtype)
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def dequant_nvfp4_kv_cache(
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fp4_data: torch.Tensor,
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block_scale: torch.Tensor,
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global_scale: float,
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head_size: int,
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block_size: int,
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) -> torch.Tensor:
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"""Dequantize an NVFP4 KV cache with 4x4-swizzled block scales.
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The input must be in HND layout so that the last two dims are
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(block_size, last_dim). For NHD caches, permute to HND first.
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Args:
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fp4_data: [..., num_heads, block_size, head_size//2] uint8 packed fp4.
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block_scale: [..., num_heads, block_size, head_size//16] fp8 block
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scales (as uint8 or float8_e4m3fn).
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global_scale: checkpoint dequant scale (k_scale or v_scale).
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head_size: head dimension.
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block_size: page size.
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Returns:
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[..., num_heads, block_size, head_size] float32.
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"""
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data_dim = head_size // 2
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scale_dim = head_size // 16
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fp4_packed = fp4_data
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sf_swizzled = block_scale.view(torch.uint8)
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# Unswizzle 4x4 block scales on (block_size, scale_dim) plane.
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# [..., T, S] → [..., T//4, 4, sg, 4] → permute → [..., T, S]
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batch_shape = sf_swizzled.shape[:-2]
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T, S = block_size, scale_dim
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sg = S // 4
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sf_reshape = sf_swizzled.reshape(*batch_shape, T // 4, 4, sg, 4)
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ndim = sf_reshape.ndim
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# Swap the last four dims: (..., T//4, 4, sg, 4) → (..., T//4, 4, 4, sg)
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perm = list(range(ndim - 4)) + [ndim - 4, ndim - 1, ndim - 3, ndim - 2]
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sf_linear = sf_reshape.permute(*perm).reshape(*batch_shape, T, S)
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sf_f32 = sf_linear.view(torch.float8_e4m3fn).to(torch.float32)
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# Unpack fp4
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shape = fp4_packed.shape # [..., T, data_dim]
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fp4_flat = fp4_packed.reshape(-1, data_dim)
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fp4_vals = break_fp4_bytes(fp4_flat, torch.float32)
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fp4_vals = fp4_vals.reshape(*shape[:-1], head_size)
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# Dequant: fp4_val * block_scale * global_scale per 16-element group
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return (
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fp4_vals.reshape(*shape[:-1], scale_dim, 16)
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* (sf_f32 * global_scale).unsqueeze(-1)
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).reshape(*shape[:-1], head_size)
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def get_nvfp4_global_scale(a: torch.Tensor):
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return (FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) / torch.abs(a).max().to(torch.float32)
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def quant_nvfp4_tensor(a: torch.Tensor):
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a_global_scale = get_nvfp4_global_scale(a)
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a_quant, a_block_scale = scaled_fp4_quant(a, a_global_scale)
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return a_quant, a_block_scale, a_global_scale
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