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c8c0ef5 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 | # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Utility methods for model layers."""
from typing import Callable, Optional
import torch
from vllm import _custom_ops as ops
from vllm import envs
from vllm.platforms import CpuArchEnum, current_platform
from vllm.utils import direct_register_custom_op
def shuffle_weight(w: torch.Tensor) -> torch.Tensor:
# Shuffle weight along the last dimension so that
# we folded the weights to adjance location
# Example:
# input:
# [[1, 2, 3, 4, 5, 6],
# [7, 8, 9, 10, 11, 12]]
# output:
# [[1, 4, 2, 5, 3, 6],
# [7, 10, 8, 11, 9, 12]]
# This will be used together with triton swiglu kernel
shape = w.shape
N = shape[-1]
first = w[..., :N // 2]
second = w[..., N // 2:]
stacked = torch.stack((first, second), dim=-1)
w_shuffled = stacked.reshape(shape)
return w_shuffled
def get_token_bin_counts_and_mask(
tokens: torch.Tensor,
vocab_size: int,
num_seqs: int,
) -> tuple[torch.Tensor, torch.Tensor]:
# Compute the bin counts for the tokens.
# vocab_size + 1 for padding.
bin_counts = torch.zeros((num_seqs, vocab_size + 1),
dtype=torch.long,
device=tokens.device)
bin_counts.scatter_add_(1, tokens, torch.ones_like(tokens))
bin_counts = bin_counts[:, :vocab_size]
mask = bin_counts > 0
return bin_counts, mask
def apply_penalties(logits: torch.Tensor, prompt_tokens_tensor: torch.Tensor,
output_tokens_tensor: torch.Tensor,
presence_penalties: torch.Tensor,
frequency_penalties: torch.Tensor,
repetition_penalties: torch.Tensor) -> torch.Tensor:
"""
Applies penalties in place to the logits tensor
logits : The input logits tensor of shape [num_seqs, vocab_size]
prompt_tokens_tensor: A tensor containing the prompt tokens. The prompts
are padded to the maximum prompt length within the batch using
`vocab_size` as the padding value. The value `vocab_size` is used
for padding because it does not correspond to any valid token ID
in the vocabulary.
output_tokens_tensor: The output tokens tensor.
presence_penalties: The presence penalties of shape (num_seqs, )
frequency_penalties: The frequency penalties of shape (num_seqs, )
repetition_penalties: The repetition penalties of shape (num_seqs, )
"""
num_seqs, vocab_size = logits.shape
_, prompt_mask = get_token_bin_counts_and_mask(prompt_tokens_tensor,
vocab_size, num_seqs)
output_bin_counts, output_mask = get_token_bin_counts_and_mask(
output_tokens_tensor, vocab_size, num_seqs)
# Apply repetition penalties as a custom op
from vllm._custom_ops import apply_repetition_penalties
apply_repetition_penalties(logits, prompt_mask, output_mask,
repetition_penalties)
# We follow the definition in OpenAI API.
# Refer to https://platform.openai.com/docs/api-reference/parameter-details
logits -= frequency_penalties.unsqueeze(dim=1) * output_bin_counts
# logits /= (1+frequency_penalties).unsqueeze(dim=1) ** output_bin_counts # 修改频率惩罚方式，先不改，有负有正反而encourage
logits -= presence_penalties.unsqueeze(dim=1) * output_mask
return logits
def default_unquantized_gemm(layer: torch.nn.Module,
x: torch.Tensor,
weight: torch.Tensor,
bias: Optional[torch.Tensor] = None):
return torch.nn.functional.linear(x, weight, bias)
def rocm_unquantized_gemm_impl(
x: torch.Tensor,
weight: torch.Tensor,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
from vllm.platforms.rocm import on_gfx9
k = weight.shape[1]
use_skinny = (envs.VLLM_ROCM_USE_SKINNY_GEMM and on_gfx9() and \
x.dtype in [torch.float16, torch.bfloat16] \
and k % 8 == 0)
if use_skinny is not True:
return torch.nn.functional.linear(x, weight, bias)
x_view = x.view(-1, x.size(-1))
n = x_view.shape[0]
m = weight.shape[0]
cu_count = current_platform.get_cu_count()
if m > 8 and 0 < n <= 4:
out = ops.wvSplitK(weight, x_view, cu_count, bias)
return out.view(*x.shape[:-1], weight.shape[0])
elif m % 4 == 0 and n == 1 and k <= 8192 and bias is None:
out = ops.LLMM1(weight, x_view, 4)
return out.view(*x.shape[:-1], weight.shape[0])
return torch.nn.functional.linear(x, weight, bias)
def rocm_unquantized_gemm_impl_fake(
x: torch.Tensor,
weight: torch.Tensor,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
return x.new_empty((*x.shape[:-1], weight.shape[0]))
def rocm_unquantized_gemm(layer: torch.nn.Module,
x: torch.Tensor,
weight: torch.Tensor,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
return torch.ops.vllm.rocm_unquantized_gemm_impl(x, weight, bias)
direct_register_custom_op(
op_name="rocm_unquantized_gemm_impl",
op_func=rocm_unquantized_gemm_impl,
fake_impl=rocm_unquantized_gemm_impl_fake,
)
def check_cpu_sgl_kernel(n: int, k: int, dtype: torch.dtype) -> bool:
return (torch._C._cpu._is_amx_tile_supported()
and (dtype in (torch.bfloat16, torch.int8)) and k % 32 == 0
and n % 16 == 0)
def dispatch_cpu_unquantized_gemm(
layer: torch.nn.Module,
remove_weight: bool,
) -> None:
N, K = layer.weight.size()
dtype = layer.weight.dtype
if envs.VLLM_CPU_SGL_KERNEL and check_cpu_sgl_kernel(N, K, dtype):
packed_weight = torch.ops._C.convert_weight_packed(layer.weight)
if getattr(layer, "bias", None) is not None:
bias_f32 = layer.bias.to(torch.float32)
else:
bias_f32 = None
layer.cpu_linear = (
lambda x, weight, bias: torch.ops._C.weight_packed_linear(
x, packed_weight, bias_f32
if bias is not None else None, True))
if remove_weight:
layer.weight = torch.nn.Parameter(torch.empty(0),
requires_grad=False)
elif (ops._supports_onednn
and current_platform.get_cpu_architecture() == CpuArchEnum.X86):
origin_weight = layer.weight
if remove_weight:
layer.weight = torch.nn.Parameter(torch.empty(0),
requires_grad=False)
handler = ops.create_onednn_mm(origin_weight.t(), 32)
layer.cpu_linear = lambda x, weight, bias: ops.onednn_mm(
handler, x, bias)
else:
layer.cpu_linear = lambda x, weight, bias: torch.nn.functional.linear(
x, weight, bias)
def cpu_unquantized_gemm(layer: torch.nn.Module,
x: torch.Tensor,
weight: torch.Tensor,
bias: Optional[torch.Tensor] = None):
return layer.cpu_linear(x, weight, bias)
def dispatch_unquantized_gemm() -> Callable[..., torch.Tensor]:
if current_platform.is_rocm():
return rocm_unquantized_gemm
elif current_platform.is_cpu():
return cpu_unquantized_gemm
else:
return default_unquantized_gemm
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