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https://github.com/wassname/vllm.git
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343 lines
11 KiB
Python
343 lines
11 KiB
Python
"""This file is used for /tests and /benchmarks"""
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import random
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from typing import Optional
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import numpy
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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.format_24 import (
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mask_creator, sparse_semi_structured_from_dense_cutlass)
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from vllm.model_executor.layers.quantization.utils.marlin_24_perms import (
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marlin_24_perm, marlin_24_scale_perm, marlin_24_scale_perm_single)
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from vllm.model_executor.layers.quantization.utils.marlin_perms import (
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marlin_perm, marlin_scale_perm, marlin_scale_perm_single)
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from vllm.model_executor.layers.quantization.utils.quant_utils import (
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get_pack_factor, quantize_weights, sort_weights)
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from vllm.platforms import current_platform
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from vllm.utils import print_warning_once
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GPTQ_MARLIN_TILE = 16
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GPTQ_MARLIN_MIN_THREAD_N = 64
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GPTQ_MARLIN_MIN_THREAD_K = 128
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GPTQ_MARLIN_MAX_PARALLEL = 16
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GPTQ_MARLIN_SUPPORTED_NUM_BITS = [4, 8]
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GPTQ_MARLIN_SUPPORTED_GROUP_SIZES = [-1, 32, 64, 128]
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GPTQ_MARLIN_SUPPORTED_SYM = [True]
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def is_marlin_supported():
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capability = current_platform.get_device_capability()
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return capability[0] >= 8
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def apply_fp8_marlin_linear(
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input: torch.Tensor,
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weight: torch.Tensor,
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weight_scale: torch.Tensor,
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workspace: torch.Tensor,
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size_n: int,
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size_k: int,
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bias: Optional[torch.Tensor],
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) -> torch.Tensor:
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# For GPUs that lack FP8 hardware support, we can leverage the
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# Marlin kernel for fast weight-only FP8 quantization
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reshaped_x = input.reshape(-1, input.shape[-1])
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out_shape = input.shape[:-1] + (size_n, )
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output = ops.fp8_marlin_gemm(
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a=reshaped_x,
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b_q_weight=weight,
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b_scales=weight_scale,
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workspace=workspace,
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num_bits=8,
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size_m=reshaped_x.shape[0],
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size_n=size_n,
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size_k=size_k,
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)
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if bias is not None:
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output.add_(bias) # In-place add
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return output.reshape(out_shape)
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def prepare_fp8_layer_for_marlin(layer: torch.nn.Module) -> None:
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print_warning_once(
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"Your GPU does not have native support for FP8 computation but "
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"FP8 quantization is being used. Weight-only FP8 compression will "
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"be used leveraging the Marlin kernel. This may degrade "
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"performance for compute-heavy workloads.")
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part_size_n = layer.output_size_per_partition
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part_size_k = layer.input_size_per_partition
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device = layer.weight.device
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# WEIGHTS
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# Repack weights to gptq format (packed int32 elements)
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packed_gptq_qweight = pack_fp8_to_int32(layer.weight)
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# Repack weights to marlin format
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marlin_qweight = ops.gptq_marlin_repack(
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b_q_weight=packed_gptq_qweight,
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perm=torch.empty(0, dtype=torch.int, device=device),
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size_k=part_size_k,
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size_n=part_size_n,
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num_bits=8,
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)
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layer.weight = torch.nn.Parameter(marlin_qweight, requires_grad=False)
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# WEIGHT SCALES
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# Currently Marlin doesn't support per-tensor scales, so we
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# expand it to channelwise
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scales = layer.weight_scale.repeat(1, part_size_n).to(
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layer.orig_dtype).to(device)
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# Permute scales
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num_bits = 8
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marlin_scales = marlin_permute_scales(
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s=scales,
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size_k=part_size_k,
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size_n=part_size_n,
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group_size=-1,
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scale_perm=marlin_scale_perm[num_bits],
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scale_perm_single=marlin_scale_perm_single[num_bits])
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layer.weight_scale = torch.nn.Parameter(marlin_scales, requires_grad=False)
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# Allocate marlin workspace
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max_workspace_size = (part_size_n //
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GPTQ_MARLIN_MIN_THREAD_N) * GPTQ_MARLIN_MAX_PARALLEL
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workspace = torch.zeros(max_workspace_size,
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dtype=torch.int,
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device=device,
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requires_grad=False)
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layer.workspace = workspace
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def marlin_permute_weights(q_w, size_k, size_n, perm, tile=GPTQ_MARLIN_TILE):
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assert q_w.shape == (size_k, size_n)
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assert size_k % tile == 0, f"size_k = {size_k}, tile = {tile}"
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assert size_n % tile == 0, f"size_k = {size_n}, tile = {tile}"
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# Permute weights to 16x64 marlin tiles
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q_w = q_w.reshape((size_k // tile, tile, size_n // tile, tile))
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q_w = q_w.permute((0, 2, 1, 3))
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q_w = q_w.reshape((size_k // tile, size_n * tile))
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q_w = q_w.reshape((-1, perm.numel()))[:, perm].reshape(q_w.shape)
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return q_w
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def marlin_weights(q_w, size_k, size_n, num_bits, perm):
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# Permute
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q_w = marlin_permute_weights(q_w, size_k, size_n, perm)
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# Pack
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pack_factor = get_pack_factor(num_bits)
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orig_device = q_w.device
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q_w = q_w.cpu().numpy().astype(numpy.uint32)
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q_packed = numpy.zeros((q_w.shape[0], q_w.shape[1] // pack_factor),
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dtype=numpy.uint32)
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for i in range(pack_factor):
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q_packed |= q_w[:, i::pack_factor] << num_bits * i
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q_packed = torch.from_numpy(q_packed.astype(numpy.int32)).to(orig_device)
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return q_packed
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def marlin_permute_scales(s, size_k, size_n, group_size, scale_perm,
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scale_perm_single):
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if group_size < size_k and group_size != -1:
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s = s.reshape((-1, len(scale_perm)))[:, scale_perm]
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else:
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s = s.reshape((-1, len(scale_perm_single)))[:, scale_perm_single]
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s = s.reshape((-1, size_n)).contiguous()
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return s
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def marlin_quantize(
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w: torch.Tensor,
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num_bits: int,
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group_size: int,
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act_order: bool,
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):
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size_k, size_n = w.shape
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# Normalize group_size
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if group_size == -1:
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group_size = size_k
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assert group_size <= size_k
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# Quantize (and apply act_order if provided)
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w_ref, q_w, s, g_idx, rand_perm = quantize_weights(w, num_bits, group_size,
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act_order)
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# For act_order, sort the "weights" and "g_idx" so that group ids are
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# increasing
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sort_indices = torch.empty(0, dtype=torch.int, device=w.device)
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if act_order:
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q_w, g_idx, sort_indices = sort_weights(q_w, g_idx)
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# Reformat to marlin
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marlin_q_w = marlin_weights(q_w, size_k, size_n, num_bits,
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marlin_perm[num_bits])
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marlin_s = marlin_permute_scales(s, size_k, size_n, group_size,
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marlin_scale_perm[num_bits],
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marlin_scale_perm_single[num_bits])
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# Create result
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res_list = [w_ref, marlin_q_w, marlin_s, g_idx, sort_indices, rand_perm]
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for i in range(len(res_list)):
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res_list[i] = res_list[i].to(w.device)
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return res_list
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def inject_24(w, size_k, size_n):
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assert w.shape == (size_k, size_n)
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mask = mask_creator(w.t()).t().cuda().bool()
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return (mask * w).contiguous(), mask.contiguous()
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def check_24(w, num_rows_to_sample=50, _verbose=False):
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BLOCK_SIZE = 4
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MAX_NON_ZEROS = 2
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w = w.t().contiguous()
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print("check_24: w.shape = {}".format(w.shape))
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num_rows, num_cols = w.shape
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sampled_row_idxs = random.choices(range(num_rows), k=num_rows_to_sample)
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if _verbose:
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print(f"Sampled row idxs = {sampled_row_idxs}")
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total_segments = 0
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non_24_segments = 0
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for i in sampled_row_idxs:
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for j in range(0, num_cols - BLOCK_SIZE, BLOCK_SIZE):
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total_segments += 1
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block = w[i, j:j + BLOCK_SIZE]
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num_nonzero = torch.count_nonzero(block)
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if num_nonzero > MAX_NON_ZEROS:
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print("i = {} j = {} block = {}".format(i, j, block))
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non_24_segments += 1
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print(f"{non_24_segments} / {total_segments} do not have 2:4 structure.")
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def compress_quantized_24_weight(q_24, size_k, size_n, num_bits):
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assert q_24.shape == (size_k, size_n)
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# Remove zp to normalize over 0
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max_q_val = (1 << num_bits) - 1
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zp = (max_q_val + 1) // 2
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q_24_no_zp = q_24 - zp
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# Compress
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q_24_no_zp = q_24_no_zp.t().contiguous()
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q_24_no_zp_comp, meta = sparse_semi_structured_from_dense_cutlass(
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q_24_no_zp)
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q_24_no_zp_comp = q_24_no_zp_comp.t().contiguous()
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# Restore zp
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q_24_comp = q_24_no_zp_comp + zp
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# Resize meta to its actual shape (without moving any data)
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meta = meta.resize_(meta.shape[1] // 2, meta.shape[0] * 2)
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return q_24_comp, meta
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def marlin_24_quantize(
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w: torch.Tensor,
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num_bits: int,
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group_size: int,
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):
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size_k, size_n = w.shape
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# Normalize group_size
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if group_size == -1:
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group_size = size_k
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assert group_size <= size_k
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# Inject 2:4 sparsity
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w_24, mask_24 = inject_24(w, size_k, size_n)
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# Quantize
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w_24_ref, q_w_24, s, g_idx, rand_perm = quantize_weights(w_24,
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num_bits,
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group_size,
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act_order=False)
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# Compress quantized weight
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q_w_24_comp, meta = compress_quantized_24_weight(q_w_24, size_k, size_n,
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num_bits)
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size_k_comp = size_k // 2
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# Reformat to marlin
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marlin_24_q_w_comp = marlin_weights(q_w_24_comp, size_k_comp, size_n,
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num_bits, marlin_24_perm[num_bits])
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marlin_24_s = marlin_permute_scales(s, size_k, size_n, group_size,
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marlin_24_scale_perm[num_bits],
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marlin_24_scale_perm_single[num_bits])
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# Create result
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res_list = [w_24_ref, marlin_24_q_w_comp, meta, marlin_24_s]
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for i in range(len(res_list)):
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res_list[i] = res_list[i].to(w.device)
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return res_list
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def compute_max_diff(output, output_ref):
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return torch.mean(torch.abs(output - output_ref)) / torch.mean(
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torch.abs(output_ref))
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class MarlinWorkspace:
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def __init__(self, out_features, min_thread_n, max_parallel):
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assert (out_features % min_thread_n == 0), (
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"out_features = {} is undivisible by min_thread_n = {}".format(
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out_features, min_thread_n))
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max_workspace_size = ((out_features // min_thread_n) * max_parallel)
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self.scratch = torch.zeros(max_workspace_size,
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dtype=torch.int,
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device="cuda")
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def pack_fp8_to_int32(fp8_tensor: torch.Tensor) -> torch.Tensor:
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"""
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Repack FP8 weights to gptq format (packed int32 elements)
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"""
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assert fp8_tensor.dtype == torch.float8_e4m3fn
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assert fp8_tensor.shape[0] % 4 == 0
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# Reshape to prepare for packing
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reshaped = fp8_tensor.reshape(-1, 4, *fp8_tensor.shape[1:])
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# Convert fp8 to uint8 (byte) representation
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byte_tensor = reshaped.view(torch.uint8)
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# Pack 4 uint8 values into one int32
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packed = (byte_tensor[:, 0].to(torch.int32) |
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(byte_tensor[:, 1].to(torch.int32) << 8) |
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(byte_tensor[:, 2].to(torch.int32) << 16) |
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(byte_tensor[:, 3].to(torch.int32) << 24))
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return packed.view(fp8_tensor.shape[0] // 4,
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*fp8_tensor.shape[1:]).contiguous()
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