8b6f3fd242
1. precompile_moe_topk.py: skip GPU verification during Docker build (torch.cuda.is_available() check — .so compilation doesn't need GPU) 2. _custom_ops.py topk_softmax init: 3-tier loading - import precompiled module (torch cache) - scan known .so paths (torch_extensions cache dirs) - JIT compile from .cu source - PyTorch fallback with WARNING (not silent — must know if CUDA failed) 3. patch_ops.sh: report .so location after precompile for debugging
1241 lines
44 KiB
Python
1241 lines
44 KiB
Python
import contextlib
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import functools
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from typing import TYPE_CHECKING, List, Optional, Tuple, Union, Dict, Any
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import torch
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import torch.library
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import vllm.envs as envs
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from vllm._core_ext import ScalarType
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from vllm.logger import init_logger
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from vllm.platforms import current_platform
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# import ixformer.inference.functions as ops
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import ixformer.functions as ixf_F
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from ixformer.distributed import _distributed as cdist
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import torch.nn.functional as F
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logger = init_logger(__name__)
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supports_moe_ops = True
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if TYPE_CHECKING:
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def register_fake(fn):
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return lambda name: fn
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else:
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try:
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from torch.library import register_fake
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except ImportError:
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try:
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from torch.library import impl_abstract as register_fake
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except:
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def register_fake(fn):
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return lambda name: fn
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def hint_on_error(fn):
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@functools.wraps(fn)
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def wrapper(*args, **kwargs):
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try:
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return fn(*args, **kwargs)
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except NotImplementedError as e:
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msg = (
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"Error in calling custom op %s: %s\n"
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"Not implemented or built, mostly likely because the current current device "
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"does not support this kernel (less likely TORCH_CUDA_ARCH_LIST was set "
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"incorrectly while building)")
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logger.error(msg, fn.__name__, e)
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raise NotImplementedError(msg % (fn.__name__, e)) from e
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except AttributeError as e:
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msg = (
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"Error in calling custom op %s: %s\n"
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"Possibly you have built or installed an obsolete version of vllm.\n"
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"Please try a clean build and install of vllm,"
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"or remove old built files such as vllm/*cpython*.so and build/ ."
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)
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logger.error(msg, fn.__name__, e)
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raise e
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return wrapper
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# activation ops
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def silu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
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ixf_F.silu_and_mul(x, out)
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def gelu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
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ixf_F.gelu_and_mul(x, out)
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def gelu_tanh_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
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ixf_F.gelu_tanh_and_mul(x, out)
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def gelu_fast(out: torch.Tensor, x: torch.Tensor) -> None:
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out.copy_(F.gelu(x,approximate="tanh"))
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return out
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def gelu_new(out: torch.Tensor, x: torch.Tensor) -> None:
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out.copy_(F.gelu(x,approximate="tanh"))
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return out
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def gelu_quick(out: torch.Tensor, x: torch.Tensor) -> None:
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out.copy_(F.gelu(x,approximate="tanh"))
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return out
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def paged_attention_v1(
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output,
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query,
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key_cache,
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value_cache,
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head_mapping,
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scale,
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block_tables,
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context_lens,
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block_size,
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max_context_len,
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alibi_slopes=None,
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kv_cache_dtype=None,
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):
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return ixf_F.vllm_single_query_cached_kv_attention(
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output,
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query,
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key_cache,
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value_cache,
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head_mapping,
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scale,
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block_tables,
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context_lens,
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block_size,
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max_context_len,
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alibi_slopes,
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)
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def paged_attention_v2(
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out: torch.Tensor,
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exp_sum: torch.Tensor,
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max_logits: torch.Tensor,
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tmp_out: torch.Tensor,
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query: torch.Tensor,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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num_kv_heads: int,
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scale: float,
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block_tables: torch.Tensor,
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seq_lens: torch.Tensor,
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block_size: int,
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max_seq_len: int,
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alibi_slopes: Optional[torch.Tensor],
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kv_cache_dtype: str,
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k_scale: float,
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v_scale: float,
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tp_rank: int = 0,
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blocksparse_local_blocks: int = 0,
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blocksparse_vert_stride: int = 0,
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blocksparse_block_size: int = 64,
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blocksparse_head_sliding_step: int = 0,
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) -> None:
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# CCCL two-pass dispatch pattern (dispatch_reduce.cuh):
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# Pass 1: N CTAs each reduce their tile → d_block_reductions[N]
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# Pass 2: 1 CTA reduces d_block_reductions[N] → d_out
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# Our PyTorch V2 implementation follows the same pattern:
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# Phase 1: partition attention (each partition = one tile)
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# Phase 2: cross-partition log-sum-exp reduction (summary_statistics binary_op)
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# paged_attention_v2_pytorch.py — try multiple import locations
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# In docker: may be at /workspace/, next to vllm package, or in vllm/ itself
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import sys, os
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_pav2 = None
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# Try 1: same package (patch_ops copies it next to _custom_ops.py)
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try:
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from vllm.paged_attention_v2_pytorch import paged_attention_v2_pytorch
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_pav2 = paged_attention_v2_pytorch
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except ImportError:
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pass
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# Try 2: /workspace/ (Dockerfile WORKDIR)
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if _pav2 is None:
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try:
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_ws = '/workspace'
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if _ws not in sys.path:
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sys.path.insert(0, _ws)
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from paged_attention_v2_pytorch import paged_attention_v2_pytorch
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_pav2 = paged_attention_v2_pytorch
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except ImportError:
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pass
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# Try 3: repo root relative to this file
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if _pav2 is None:
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_repo_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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if _repo_root not in sys.path:
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sys.path.insert(0, _repo_root)
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from paged_attention_v2_pytorch import paged_attention_v2_pytorch
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_pav2 = paged_attention_v2_pytorch
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_pav2(
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out, exp_sum, max_logits, tmp_out,
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query, key_cache, value_cache,
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num_kv_heads, scale, block_tables, seq_lens,
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block_size, max_seq_len, alibi_slopes,
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kv_cache_dtype, k_scale, v_scale, tp_rank,
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blocksparse_local_blocks, blocksparse_vert_stride,
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blocksparse_block_size, blocksparse_head_sliding_step,
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)
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def paged_attention_rocm(
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out: torch.Tensor,
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exp_sum: torch.Tensor,
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max_logits: torch.Tensor,
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tmp_out: torch.Tensor,
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query: torch.Tensor,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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num_kv_heads: int,
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scale: float,
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block_tables: torch.Tensor,
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seq_lens: torch.Tensor,
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block_size: int,
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max_seq_len: int,
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alibi_slopes: Optional[torch.Tensor],
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kv_cache_dtype: str,
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k_scale: float,
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v_scale: float,
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) -> None:
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raise NotImplementedError()
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# pos encoding ops
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def rotary_embedding(
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positions: torch.Tensor,
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query: torch.Tensor,
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key: torch.Tensor,
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head_size: int,
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cos_sin_cache: torch.Tensor,
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is_neox: bool,
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) -> None:
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ixf_F.vllm_rotary_embedding_neox(positions, query, key, head_size,
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cos_sin_cache, is_neox)
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def batched_rotary_embedding(positions: torch.Tensor, query: torch.Tensor,
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key: torch.Tensor, head_size: int,
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cos_sin_cache: torch.Tensor, is_neox: bool,
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rot_dim: int,
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cos_sin_cache_offsets: torch.Tensor) -> None:
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ixf_F.vllm_batched_rotary_embedding(positions, query, key, head_size,
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cos_sin_cache, is_neox, rot_dim,
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cos_sin_cache_offsets)
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# layer norm ops
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def rms_norm(out: torch.Tensor, input: torch.Tensor, weight: torch.Tensor,
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epsilon: float) -> None:
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ixf_F.rms_norm(input, weight, out, epsilon)
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def fused_add_rms_norm(input: torch.Tensor, residual: torch.Tensor,
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weight: torch.Tensor, epsilon: float,
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residual_alpha: Optional[float] = 1) -> None:
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ixf_F.fused_add_rms_norm(input, residual, weight, epsilon)
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def advance_step_flashattn(num_seqs: int, num_queries: int, block_size: int,
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input_tokens: torch.Tensor,
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sampled_token_ids: torch.Tensor,
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input_positions: torch.Tensor,
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seq_lens: torch.Tensor, slot_mapping: torch.Tensor,
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block_tables: torch.Tensor) -> None:
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"""Advance a step on GPU for existing inputs for a multi-step runner"""
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return ixf_F.advance_step_flashattn(num_seqs, num_queries, block_size,
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input_tokens,
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sampled_token_ids,
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input_positions,
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seq_lens, slot_mapping,
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block_tables)
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def advance_step_flashinfer(num_seqs: int, num_queries: int, block_size: int,
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input_tokens: torch.Tensor,
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sampled_token_ids: torch.Tensor,
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input_positions: torch.Tensor,
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seq_lens: torch.Tensor, slot_mapping: torch.Tensor,
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block_tables: torch.Tensor,
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paged_kv_indices: torch.Tensor,
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paged_kv_indptr: torch.Tensor,
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paged_kv_last_page_len: torch.Tensor,
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block_table_bound: torch.Tensor) -> None:
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raise NotImplementedError("FIX SOON")
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# quantization ops
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# awq
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def awq_dequantize(qweight: torch.Tensor, scales: torch.Tensor,
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zeros: torch.Tensor, split_k_iters: int, thx: int,
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thy: int) -> torch.Tensor:
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raise NotImplementedError()
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def awq_gemm(input: torch.Tensor, qweight: torch.Tensor, scales: torch.Tensor, qzeros: torch.Tensor,
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pack_factor, group_size: int = 128) -> torch.Tensor:
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return ixf_F.quantized_linear(input, qweight, scales,"awq",32 // pack_factor,qzeros=qzeros,group_size=group_size)
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# gptq
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def gptq_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
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b_gptq_qzeros: torch.Tensor, b_gptq_scales: torch.Tensor,
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b_g_idx: torch.Tensor, use_exllama: bool,
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bit: int) -> torch.Tensor:
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batch = a.shape[0]
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if batch <= 8:
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return ixf_F.quantized_linear(a,b_q_weight,b_gptq_scales,"gptq",4,b_gptq_qzeros,None,group_size=128)
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o_dtype_str = "fp16" if a.dtype == torch.half else "bf16"
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deq_w = ixf_F.quantized_weight_dequant(b_q_weight,b_gptq_scales,"gptq",o_dtype_str,4,b_gptq_qzeros,group_size=128)
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return torch.matmul(a,deq_w)
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if hasattr(torch.ops._C, "gptq_gemm"):
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@register_fake("_C::gptq_gemm")
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def _gptq_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
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b_gptq_qzeros: torch.Tensor,
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b_gptq_scales: torch.Tensor, b_g_idx: torch.Tensor,
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use_exllama: bool, bit: int) -> torch.Tensor:
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return torch.empty((a.size(0), b_q_weight.size(1)),
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dtype=a.dtype,
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device=a.device)
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def gptq_shuffle(q_weight: torch.Tensor, q_perm: torch.Tensor,
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bit: int) -> None:
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return ixf_F.vllm_gptq_shuffle(q_weight,q_perm)
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# marlin
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def marlin_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
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b_scales: torch.Tensor, workspace: torch.Tensor, size_m: int,
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size_n: int, size_k: int) -> torch.Tensor:
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raise NotImplementedError()
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# marlin_24
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def gptq_marlin_24_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
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b_meta: torch.Tensor, b_scales: torch.Tensor,
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workspace: torch.Tensor, b_q_type: ScalarType,
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size_m: int, size_n: int, size_k: int) -> torch.Tensor:
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raise NotImplementedError()
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if hasattr(torch.ops._C, "gptq_marlin_24_gemm"):
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@register_fake("_C::gptq_marlin_24_gemm")
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def _gptq_marlin_24_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
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b_meta: torch.Tensor, b_scales: torch.Tensor,
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workspace: torch.Tensor,
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b_q_type: ScalarType, size_m: int,
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size_n: int, size_k: int) -> torch.Tensor:
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return torch.empty((size_m, size_n), device=a.device, dtype=a.dtype)
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@register_fake("_C::gptq_marlin_gemm")
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def _gptq_marlin_gemm_fake(a: torch.Tensor,
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b_q_weight: torch.Tensor,
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b_scales: torch.Tensor,
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b_zeros: torch.Tensor,
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g_idx: torch.Tensor,
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perm: torch.Tensor,
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workspace: torch.Tensor,
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b_q_type: ScalarType,
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size_m: int,
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size_n: int,
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size_k: int,
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is_k_full: bool,
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has_zp: bool = False,
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use_fp32_reduce: bool = False) -> torch.Tensor:
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return torch.empty((size_m, size_n), device=a.device, dtype=a.dtype)
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@register_fake("_C::ggml_dequantize")
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def _ggml_dequantize_fake(W: torch.Tensor, quant_type: int, m: int,
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n: int) -> torch.Tensor:
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return torch.empty((m, n), dtype=torch.float16, device=W.device)
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@register_fake("_C::ggml_mul_mat_vec_a8")
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def _ggml_mul_mat_vec_a8_fake(
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W: torch.Tensor,
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X: torch.Tensor,
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quant_type: int,
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row: int,
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) -> torch.Tensor:
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return torch.empty((1, row), dtype=torch.float16, device=W.device)
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@register_fake("_C::ggml_mul_mat_a8")
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def _ggml_mul_mat_a8_fake(
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W: torch.Tensor,
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X: torch.Tensor,
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quant_type: int,
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row: int,
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) -> torch.Tensor:
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batch = X.size(0)
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return torch.empty((batch, row), dtype=torch.float16, device=W.device)
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@register_fake("_C::marlin_qqq_gemm")
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def _marlin_qqq_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
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s_tok: torch.Tensor, s_ch: torch.Tensor,
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s_group: torch.Tensor, workspace: torch.Tensor,
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size_m: int, size_n: int,
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size_k: int) -> torch.Tensor:
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return torch.empty((size_m, size_n),
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dtype=torch.float16,
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device=a.device)
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@register_fake("_C::marlin_gemm")
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def _marlin_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
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b_scales: torch.Tensor, workspace: torch.Tensor,
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size_m: int, size_n: int,
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size_k: int) -> torch.Tensor:
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return torch.empty((size_m, size_n),
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dtype=torch.float16,
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device=a.device)
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@register_fake("_C::awq_dequantize")
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def _awq_dequantize_fake(qweight: torch.Tensor, scales: torch.Tensor,
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zeros: torch.Tensor, split_k_iters: int, thx: int,
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thy: int) -> torch.Tensor:
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in_c = qweight.size(0)
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qout_c = qweight.size(1)
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out_c = qout_c * 8
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return torch.empty((in_c, out_c),
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dtype=scales.dtype,
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device=scales.device)
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@register_fake("_C::awq_gemm")
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def _awq_gemm_fake(input: torch.Tensor, qweight: torch.Tensor,
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qzeros: torch.Tensor, scales: torch.Tensor,
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split_k_iters: int) -> torch.Tensor:
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num_in_feats = input.size(0)
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return torch.empty((split_k_iters, num_in_feats, qweight.size(1) * 8),
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dtype=input.dtype,
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device=input.device).sum(0)
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@register_fake("_C::aqlm_gemm")
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def _aqlm_gemm_fake(input: torch.Tensor, codes: torch.Tensor,
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codebooks: torch.Tensor, scales: torch.Tensor,
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codebook_partition_sizes: List[int],
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bias: Optional[torch.Tensor]) -> torch.Tensor:
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out_features = codes.size(0) * codebooks.size(2)
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flat_input = input.reshape((-1, input.size(-1)))
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flat_output = torch.empty((flat_input.size(0), out_features),
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dtype=input.dtype,
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device=input.device)
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output_sizes = list(input.shape)
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output_sizes.pop()
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output_sizes.append(-1)
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return flat_output.reshape(tuple(output_sizes))
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@register_fake("_C::aqlm_dequant")
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def _aqlm_dequant_fake(
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codes: torch.Tensor, codebooks: torch.Tensor,
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codebook_partition_sizes: List[int]) -> torch.Tensor:
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in_features = codes.size(1) * 8
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out_features = codes.size(0)
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return torch.empty((out_features, in_features),
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dtype=codebooks.dtype,
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device=codebooks.device)
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@register_fake("_C::fp8_marlin_gemm")
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def _fp8_marlin_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
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b_scales: torch.Tensor, workspace: torch.Tensor,
|
|
num_bits: int, size_m: int, size_n: int,
|
|
size_k: int) -> torch.Tensor:
|
|
return torch.empty((size_m, size_n), dtype=a.dtype, device=a.device)
|
|
|
|
@register_fake("_C::machete_gemm")
|
|
def machete_gemm_fake(
|
|
a: torch.Tensor,
|
|
# Should be the tensor returned by machete_prepack_B
|
|
b_q: torch.Tensor,
|
|
b_type: ScalarType,
|
|
b_scales: Optional[torch.Tensor] = None,
|
|
b_zeros: Optional[torch.Tensor] = None,
|
|
b_group_size: Optional[int] = None,
|
|
c: Optional[torch.Tensor] = None,
|
|
alpha: Optional[float] = None,
|
|
beta: Optional[float] = None,
|
|
schedule: Optional[str] = None,
|
|
) -> torch.Tensor:
|
|
m = a.size(0)
|
|
n = b_q.size(1)
|
|
return torch.empty((m, n), device=a.device, dtype=a.dtype)
|
|
|
|
@register_fake("_C::machete_prepack_B")
|
|
def machete_prepack_B_fake(b_q_weight: torch.Tensor,
|
|
b_type: ScalarType) -> torch.Tensor:
|
|
return torch.empty_like(b_q_weight,
|
|
memory_format=torch.contiguous_format)
|
|
|
|
@register_fake("_C::causal_conv1d_fwd")
|
|
def causal_conv1d_fwd_fake(x: torch.Tensor, weight: torch.Tensor,
|
|
bias_: Optional[torch.Tensor],
|
|
conv_states: Optional[torch.Tensor],
|
|
cu_seq_len: Optional[torch.Tensor],
|
|
cache_indices: Optional[torch.Tensor],
|
|
has_initial_state: Optional[torch.Tensor],
|
|
silu_activation: bool) -> torch.Tensor:
|
|
return torch.empty_like(x)
|
|
|
|
@register_fake("_C::causal_conv1d_update")
|
|
def causal_conv1d_update_fake(
|
|
x: torch.Tensor, conv_state: torch.Tensor, weight: torch.Tensor,
|
|
bias_: Optional[torch.Tensor], silu_activation: bool,
|
|
cache_seqlens: Optional[torch.Tensor],
|
|
conv_state_indices: Optional[torch.Tensor]) -> torch.Tensor:
|
|
return torch.empty_like(x)
|
|
|
|
@register_fake("_C::selective_scan_fwd")
|
|
def selective_scan_fwd_fake(u: torch.Tensor, delta: torch.Tensor,
|
|
A: torch.Tensor, B: torch.Tensor,
|
|
C: torch.Tensor, D_: Optional[torch.Tensor],
|
|
z_: Optional[torch.Tensor],
|
|
delta_bias_: Optional[torch.Tensor],
|
|
delta_softplus: bool,
|
|
cu_seq_len: Optional[torch.Tensor],
|
|
cache_indices: Optional[torch.Tensor],
|
|
has_initial_state: Optional[torch.Tensor],
|
|
ssm_states: Optional[torch.Tensor]) -> None:
|
|
return None
|
|
|
|
|
|
# cutlass
|
|
def cutlass_scaled_mm_supports_fp8(cuda_device_capability: int) -> bool:
|
|
return True
|
|
|
|
|
|
def cutlass_scaled_mm(a: torch.Tensor,
|
|
b: torch.Tensor,
|
|
scale_a: torch.Tensor,
|
|
scale_b: torch.Tensor,
|
|
out_dtype: torch.dtype,
|
|
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
|
|
|
|
m = a.shape[0]
|
|
n = b.shape[1]
|
|
out = torch.empty((m, n), dtype=out_dtype, device=a.device)
|
|
ixf_F.w8a8(a, b.transpose(0,1), scale_a, scale_b, bias, output=out, out_dtype=out_dtype)
|
|
|
|
return out
|
|
|
|
|
|
def cutlass_scaled_mm_azp(a: torch.Tensor,
|
|
b: torch.Tensor,
|
|
scale_a: torch.Tensor,
|
|
scale_b: torch.Tensor,
|
|
out_dtype: torch.dtype,
|
|
azp_adj: torch.Tensor,
|
|
azp: Optional[torch.Tensor] = None,
|
|
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
|
|
raise NotImplementedError()
|
|
|
|
|
|
# aqlm
|
|
def aqlm_gemm(input: torch.Tensor, codes: torch.Tensor,
|
|
codebooks: torch.Tensor, scales: torch.Tensor,
|
|
codebook_partition_sizes: List[int],
|
|
bias: Optional[torch.Tensor]) -> torch.Tensor:
|
|
raise NotImplementedError()
|
|
|
|
|
|
def aqlm_dequant(codes: torch.Tensor, codebooks: torch.Tensor,
|
|
codebook_partition_sizes: List[int]) -> torch.Tensor:
|
|
raise NotImplementedError()
|
|
|
|
|
|
# gptq_marlin
|
|
def gptq_marlin_repack(b_q_weight: torch.Tensor, perm: torch.Tensor,
|
|
size_k: int, size_n: int,
|
|
num_bits: int) -> torch.Tensor:
|
|
raise NotImplementedError()
|
|
|
|
|
|
# gptq_marlin
|
|
def awq_marlin_repack(b_q_weight: torch.Tensor, size_k: int, size_n: int,
|
|
num_bits: int) -> torch.Tensor:
|
|
raise NotImplementedError()
|
|
|
|
|
|
def gptq_marlin_moe_repack(b_q_weight: torch.Tensor, perm: torch.Tensor,
|
|
size_k: int, size_n: int,
|
|
num_bits: int) -> torch.Tensor:
|
|
raise NotImplementedError()
|
|
|
|
|
|
def awq_marlin_moe_repack(b_q_weight: torch.Tensor, perm: torch.Tensor,
|
|
size_k: int, size_n: int,
|
|
num_bits: int) -> torch.Tensor:
|
|
num_experts = b_q_weight.shape[0]
|
|
assert size_k % 16 == 0
|
|
output = torch.empty((num_experts, size_k // 16, size_n * (num_bits // 2)),
|
|
device=b_q_weight.device,
|
|
dtype=b_q_weight.dtype)
|
|
for e in range(num_experts):
|
|
output[e] = torch.ops._C.awq_marlin_repack(b_q_weight[e], size_k,
|
|
size_n, num_bits)
|
|
return output
|
|
|
|
|
|
def gptq_marlin_gemm(a: torch.Tensor,
|
|
b_q_weight: torch.Tensor,
|
|
b_scales: torch.Tensor,
|
|
b_zeros: torch.Tensor,
|
|
g_idx: torch.Tensor,
|
|
perm: torch.Tensor,
|
|
workspace: torch.Tensor,
|
|
b_q_type: ScalarType,
|
|
size_m: int,
|
|
size_n: int,
|
|
size_k: int,
|
|
is_k_full: bool,
|
|
has_zp: bool = False,
|
|
use_fp32_reduce: bool = False) -> torch.Tensor:
|
|
raise NotImplementedError()
|
|
|
|
|
|
# fp8 marlin
|
|
def fp8_marlin_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
|
|
b_scales: torch.Tensor, workspace: torch.Tensor,
|
|
num_bits: int, size_m: int, size_n: int,
|
|
size_k: int) -> torch.Tensor:
|
|
raise NotImplementedError()
|
|
|
|
|
|
# machete
|
|
def machete_supported_schedules(b_type: ScalarType) -> List[str]:
|
|
raise NotImplementedError()
|
|
|
|
|
|
def machete_gemm(
|
|
a: torch.Tensor,
|
|
b_q: torch.Tensor, # Should be the tensor returned by machete_prepack_B
|
|
b_type: ScalarType,
|
|
b_scales: Optional[torch.Tensor] = None,
|
|
b_zeros: Optional[torch.Tensor] = None,
|
|
b_group_size: Optional[int] = None,
|
|
c: Optional[torch.Tensor] = None,
|
|
alpha: Optional[float] = None,
|
|
beta: Optional[float] = None,
|
|
schedule: Optional[str] = None,
|
|
) -> torch.Tensor:
|
|
raise NotImplementedError()
|
|
|
|
|
|
def machete_prepack_B(b_q_weight: torch.Tensor,
|
|
b_type: ScalarType) -> torch.Tensor:
|
|
raise NotImplementedError()
|
|
|
|
|
|
if hasattr(torch.ops._C, "permute_cols"):
|
|
|
|
@register_fake("_C::permute_cols")
|
|
def _permute_cols_fake(a: torch.Tensor,
|
|
perm: torch.Tensor) -> torch.Tensor:
|
|
return torch.empty_like(a)
|
|
|
|
|
|
def permute_cols(a: torch.Tensor, perm: torch.Tensor) -> torch.Tensor:
|
|
raise NotImplementedError()
|
|
|
|
|
|
# fp8
|
|
def scaled_fp8_quant(
|
|
input: torch.Tensor,
|
|
scale: Optional[torch.Tensor] = None,
|
|
num_token_padding: Optional[int] = None,
|
|
scale_ub: Optional[torch.Tensor] = None,
|
|
use_per_token_if_dynamic: bool = False,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
"""
|
|
Quantize input tensor to FP8 and return quantized tensor and scale.
|
|
|
|
This function supports both static and dynamic quantization: If you
|
|
provide the scale, it will use static scaling and if you omit it,
|
|
the scale will be determined dynamically. The function also allows
|
|
optional padding of the output tensors for downstream kernels that
|
|
will benefit from padding.
|
|
|
|
Args:
|
|
input: The input tensor to be quantized to FP8
|
|
scale: Optional scaling factor for the FP8 quantization
|
|
scale_ub: Optional upper bound for scaling factor in dynamic
|
|
per token case
|
|
num_token_padding: If specified, pad the first dimension
|
|
of the output to at least this value.
|
|
use_per_token_if_dynamic: Whether to do per_tensor or per_token
|
|
in the dynamic quantization case.
|
|
|
|
Returns:
|
|
Tuple[torch.Tensor, torch.Tensor]: The output tensor in FP8 and
|
|
scaling factor.
|
|
"""
|
|
raise NotImplementedError()
|
|
|
|
|
|
# int8
|
|
def scaled_int8_quant(
|
|
input: torch.Tensor,
|
|
scale: Optional[torch.Tensor] = None,
|
|
azp: Optional[torch.Tensor] = None,
|
|
symmetric: bool = True
|
|
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
|
|
"""
|
|
Quantize the input tensor to int8 and return the quantized tensor and scale, and maybe azp.
|
|
|
|
Args:
|
|
input: The input tensor to be quantized to int8.
|
|
scale: Optional scaling factor for the int8 quantization.
|
|
When not provided, we invoke dynamic-per-token quantization.
|
|
azp: Optional zero-point for the int8 quantization.
|
|
Must be provided for asymmetric quantization if `scale` is provided.
|
|
symmetric: Whether to use symmetric quantization (scale only, azp ignored).
|
|
|
|
Returns:
|
|
Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]] : Output int8 tensor, scales, and optionally azp.
|
|
"""
|
|
output = torch.empty_like(input, dtype=torch.int8)
|
|
if scale is not None:
|
|
# static-per-tensor quantization.
|
|
assert symmetric == (
|
|
azp is
|
|
None), "azp must only be provided for asymmetric quantization."
|
|
ixf_F.static_scaled_int8_quant(output, input, scale)
|
|
return output, scale, None
|
|
|
|
# dynamic-per-token quantization.
|
|
input_scales = torch.empty((input.numel() // input.shape[-1], 1),
|
|
device=input.device,
|
|
dtype=torch.float32)
|
|
input_azp = None if symmetric else torch.empty_like(input_scales,
|
|
dtype=torch.int32)
|
|
ixf_F.dynamic_scaled_int8_quant(output, input, input_scales)
|
|
return output, input_scales, input_azp
|
|
|
|
|
|
# qqq ops
|
|
def marlin_qqq_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
|
|
s_tok: torch.Tensor, s_ch: torch.Tensor,
|
|
s_group: torch.Tensor, workspace: torch.Tensor,
|
|
size_m: int, size_n: int, size_k: int) -> torch.Tensor:
|
|
raise NotImplementedError()
|
|
|
|
|
|
# gguf
|
|
def ggml_dequantize(W: torch.Tensor, quant_type: int, m: int,
|
|
n: int) -> torch.Tensor:
|
|
raise NotImplementedError()
|
|
|
|
|
|
def ggml_mul_mat_vec_a8(
|
|
W: torch.Tensor,
|
|
X: torch.Tensor,
|
|
quant_type: int,
|
|
row: int,
|
|
) -> torch.Tensor:
|
|
raise NotImplementedError()
|
|
|
|
|
|
def ggml_mul_mat_a8(
|
|
W: torch.Tensor,
|
|
X: torch.Tensor,
|
|
quant_type: int,
|
|
row: int,
|
|
) -> torch.Tensor:
|
|
raise NotImplementedError()
|
|
|
|
|
|
# mamba
|
|
def causal_conv1d_fwd(x: torch.Tensor, weight: torch.Tensor,
|
|
bias_: Optional[torch.Tensor],
|
|
conv_states: Optional[torch.Tensor],
|
|
query_start_loc: Optional[torch.Tensor],
|
|
cache_indices: Optional[torch.Tensor],
|
|
has_initial_state: Optional[torch.Tensor],
|
|
silu_activation: bool) -> torch.Tensor:
|
|
raise NotImplementedError()
|
|
|
|
|
|
def causal_conv1d_update(
|
|
x: torch.Tensor, conv_state: torch.Tensor, weight: torch.Tensor,
|
|
bias_: Optional[torch.Tensor], silu_activation: bool,
|
|
cache_seqlens: Optional[torch.Tensor],
|
|
conv_state_indices: Optional[torch.Tensor]) -> torch.Tensor:
|
|
raise NotImplementedError()
|
|
|
|
|
|
def selective_scan_fwd(
|
|
u: torch.Tensor, delta: torch.Tensor, A: torch.Tensor, B: torch.Tensor,
|
|
C: torch.Tensor, D_: Optional[torch.Tensor],
|
|
z_: Optional[torch.Tensor], delta_bias_: Optional[torch.Tensor],
|
|
delta_softplus: bool, query_start_loc: Optional[torch.Tensor],
|
|
cache_indices: Optional[torch.Tensor],
|
|
has_initial_state: Optional[torch.Tensor], ssm_states: torch.Tensor):
|
|
raise NotImplementedError()
|
|
|
|
|
|
# moe
|
|
def moe_align_block_size(topk_ids: torch.Tensor, num_experts: int,
|
|
block_size: int, sorted_token_ids: torch.Tensor,
|
|
experts_ids: torch.Tensor,
|
|
num_tokens_post_pad: torch.Tensor) -> None:
|
|
ixf_F.vllm_moe_align_block_size(topk_ids, num_experts, block_size,
|
|
sorted_token_ids, experts_ids,
|
|
num_tokens_post_pad)
|
|
|
|
|
|
def invoke_fused_moe_kernel(
|
|
A: torch.Tensor,
|
|
B: torch.Tensor,
|
|
C: torch.Tensor,
|
|
A_scale: Optional[torch.Tensor],
|
|
B_scale: Optional[torch.Tensor],
|
|
topk_weights: torch.Tensor,
|
|
topk_ids: torch.Tensor,
|
|
sorted_token_ids: torch.Tensor,
|
|
expert_ids: torch.Tensor,
|
|
num_tokens_post_padded: torch.Tensor,
|
|
mul_routed_weight: bool,
|
|
top_k: int,
|
|
config: Dict[str, Any],
|
|
compute_type,
|
|
use_fp8_w8a8: bool,
|
|
use_int8_w8a16: bool,
|
|
) -> None:
|
|
ixf_F.vllm_invoke_fused_moe_kernel(
|
|
A,
|
|
B,
|
|
C,
|
|
topk_weights,
|
|
topk_ids,
|
|
sorted_token_ids,
|
|
expert_ids,
|
|
num_tokens_post_padded,
|
|
mul_routed_weight,
|
|
top_k,
|
|
config['BLOCK_SIZE_M']
|
|
)
|
|
|
|
|
|
# ---------- topk_softmax: CUDA kernel → PyTorch fallback ----------
|
|
# moe_topk_softmax_v3.cu: fused warp-shuffle kernel, 64 experts, zero SMEM.
|
|
# Precompiled during Docker build → .so cached by torch.
|
|
# If not found, JIT from .cu source. PyTorch last resort.
|
|
_moe_topk_ext = None
|
|
_moe_topk_init_done = False
|
|
|
|
def _init_moe_topk():
|
|
global _moe_topk_ext, _moe_topk_init_done
|
|
_moe_topk_init_done = True
|
|
# 1. Try import precompiled module (torch cache from Docker build)
|
|
try:
|
|
import moe_topk_softmax_v3 as ext
|
|
_moe_topk_ext = ext
|
|
logger.info("topk_softmax: loaded precompiled CUDA kernel")
|
|
return
|
|
except ImportError:
|
|
pass
|
|
# 2. Try loading from known .so paths
|
|
import glob
|
|
so_patterns = [
|
|
"/workspace/ex_engine/build/moe_topk_softmax_v3*.so",
|
|
"/root/.cache/torch_extensions/*/moe_topk_softmax_v3/*.so",
|
|
"/tmp/torch_extensions/*/moe_topk_softmax_v3/*.so",
|
|
]
|
|
for pattern in so_patterns:
|
|
for so_path in glob.glob(pattern):
|
|
try:
|
|
torch.ops.load_library(so_path)
|
|
# After load_library, the pybind module should be importable
|
|
import moe_topk_softmax_v3 as ext
|
|
_moe_topk_ext = ext
|
|
logger.info("topk_softmax: loaded CUDA kernel from %s", so_path)
|
|
return
|
|
except Exception:
|
|
pass
|
|
# 3. JIT compile from .cu source
|
|
import os
|
|
search_paths = [
|
|
"/workspace/ex_engine/csrc/moe_topk_softmax_v3.cu",
|
|
os.path.join(os.path.dirname(os.path.abspath(__file__)), "moe_topk_softmax_v3.cu"),
|
|
]
|
|
for base in ["/usr/local/corex/lib/python3/dist-packages/vllm/model_executor/models",
|
|
"/usr/local/corex/lib64/python3/dist-packages/vllm/model_executor/models"]:
|
|
search_paths.append(os.path.join(base, "moe_topk_softmax_v3.cu"))
|
|
for cu_path in search_paths:
|
|
if os.path.isfile(cu_path):
|
|
try:
|
|
from torch.utils.cpp_extension import load
|
|
ext = load(
|
|
name="moe_topk_softmax_v3",
|
|
sources=[cu_path],
|
|
extra_cuda_cflags=["-O3"],
|
|
verbose=False,
|
|
)
|
|
_moe_topk_ext = ext
|
|
logger.info("topk_softmax: JIT compiled CUDA kernel from %s", cu_path)
|
|
return
|
|
except Exception as e:
|
|
logger.warning("topk_softmax: JIT compile failed (%s)", e)
|
|
break # Don't retry same source with different paths
|
|
logger.warning("topk_softmax: CUDA kernel unavailable — PyTorch fallback (SLOW)")
|
|
|
|
|
|
def topk_softmax(topk_weights: torch.Tensor, topk_ids: torch.Tensor,
|
|
token_expert_indicies: torch.Tensor,
|
|
gating_output: float) -> None:
|
|
global _moe_topk_ext, _moe_topk_init_done
|
|
if not _moe_topk_init_done:
|
|
_init_moe_topk()
|
|
|
|
# Priority 1: Our CUDA kernel (fused warp-shuffle, ~5x faster than PyTorch)
|
|
if _moe_topk_ext is not None:
|
|
try:
|
|
gating = gating_output if isinstance(gating_output, torch.Tensor) else gating_output
|
|
topk_k = topk_weights.shape[1]
|
|
results = _moe_topk_ext.moe_topk_softmax(gating, topk_k, False)
|
|
topk_weights.copy_(results[0].to(topk_weights.dtype))
|
|
topk_ids.copy_(results[1].to(topk_ids.dtype))
|
|
token_expert_indicies.copy_(results[2].to(token_expert_indicies.dtype))
|
|
return
|
|
except Exception as e:
|
|
logger.warning("topk_softmax CUDA kernel failed (%s), falling back to PyTorch", e)
|
|
_moe_topk_ext = None # disable permanently on failure
|
|
|
|
# Priority 2: PyTorch fallback (always works)
|
|
if isinstance(gating_output, torch.Tensor):
|
|
probs = torch.softmax(gating_output.float(), dim=-1)
|
|
else:
|
|
probs = torch.softmax(gating_output, dim=-1)
|
|
topk = topk_weights.shape[1]
|
|
tw, ti = torch.topk(probs, topk, dim=-1)
|
|
topk_weights.copy_(tw.to(topk_weights.dtype))
|
|
topk_ids.copy_(ti.to(topk_ids.dtype))
|
|
token_expert_indicies.copy_(
|
|
torch.arange(topk, device=topk_ids.device, dtype=topk_ids.dtype)
|
|
.unsqueeze(0).expand_as(topk_ids))
|
|
|
|
|
|
if supports_moe_ops and hasattr(torch.ops._moe_C, "marlin_gemm_moe"):
|
|
|
|
@register_fake("_moe_C::marlin_gemm_moe")
|
|
def marlin_gemm_moe_fake(a: torch.Tensor, b_q_weights: torch.Tensor,
|
|
sorted_ids: torch.Tensor,
|
|
topk_weights: torch.Tensor,
|
|
topk_ids: torch.Tensor, b_scales: torch.Tensor,
|
|
b_zero_points: torch.Tensor, g_idx: torch.Tensor,
|
|
perm: torch.Tensor, workspace: torch.Tensor,
|
|
b_q_type: ScalarType, size_m: int, size_n: int,
|
|
size_k: int, is_k_full: bool, num_experts: int,
|
|
topk: int, moe_block_size: int,
|
|
replicate_input: bool,
|
|
apply_weights: bool) -> torch.Tensor:
|
|
return torch.empty((size_m, topk, size_n),
|
|
dtype=a.dtype,
|
|
device=a.device)
|
|
|
|
|
|
def reshape_and_cache(
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
key_cache: torch.Tensor,
|
|
value_cache: torch.Tensor,
|
|
slot_mapping: torch.Tensor,
|
|
kv_cache_dtype: str,
|
|
k_scale: float,
|
|
v_scale: float,
|
|
) -> None:
|
|
slot_mapping = slot_mapping.to(torch.int32)
|
|
ixf_F.vllm_cache_ops_reshape_and_cache(key, value, key_cache,
|
|
value_cache, slot_mapping)
|
|
|
|
|
|
def reshape_and_cache_flash(
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
key_cache: torch.Tensor,
|
|
value_cache: torch.Tensor,
|
|
slot_mapping: torch.Tensor,
|
|
kv_cache_dtype: str,
|
|
k_scale: float,
|
|
v_scale: float,
|
|
) -> None:
|
|
ixf_F.reshape_and_cache_flash(key, value, key_cache,
|
|
value_cache, slot_mapping,
|
|
kv_cache_dtype, k_scale,
|
|
v_scale)
|
|
|
|
def reshape_and_cache_flashinfer(
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
key_cache: torch.Tensor,
|
|
value_cache: torch.Tensor,
|
|
slot_mapping: torch.Tensor,
|
|
kv_cache_dtype: str,
|
|
k_scale: float, # for fp8
|
|
v_scale: float, # for fp8
|
|
kv_cache_format: str = "NHD",
|
|
key_cache_scales: torch.Tensor = None, # for int8
|
|
value_cache_scales: torch.Tensor = None, # for int8
|
|
) -> None:
|
|
ixf_F.paged_attention_cache_appended(
|
|
key,
|
|
value,
|
|
key_cache,
|
|
value_cache,
|
|
slot_mapping,
|
|
kv_cache_format,
|
|
key_cache_scales,
|
|
value_cache_scales,
|
|
)
|
|
|
|
def copy_blocks(key_caches: List[torch.Tensor],
|
|
value_caches: List[torch.Tensor],
|
|
block_mapping: torch.Tensor) -> None:
|
|
ixf_F.copy_blocks(key_caches, value_caches, block_mapping)
|
|
|
|
|
|
def swap_blocks(src: torch.Tensor, dst: torch.Tensor,
|
|
block_mapping: torch.Tensor) -> None:
|
|
ixf_F.swap_blocks(src, dst, block_mapping)
|
|
|
|
|
|
def convert_fp8(output: torch.Tensor,
|
|
input: torch.Tensor,
|
|
scale: float = 1.0,
|
|
kv_dtype: str = "fp8") -> None:
|
|
raise NotImplementedError()
|
|
|
|
|
|
def get_device_attribute(attribute: int, device: int) -> int:
|
|
raise NotImplementedError()
|
|
|
|
|
|
def get_max_shared_memory_per_block_device_attribute(device: int) -> int:
|
|
# BI-V100 SMEM = 49152 bytes (48KB), confirmed via ixsmi
|
|
# Was incorrectly hardcoded to 32KB (32768), limiting Triton tile sizes
|
|
# and potentially constraining ixformer internal SMEM allocation.
|
|
return 49152
|
|
|
|
|
|
# custom ar
|
|
def init_custom_ar(meta: torch.Tensor, rank_data: torch.Tensor,
|
|
handles: List[str], offsets: List[int], rank: int,
|
|
full_nvlink: bool) -> int:
|
|
raise NotImplementedError()
|
|
|
|
|
|
def should_custom_ar(inp: torch.Tensor, max_size: int, world_size: int,
|
|
full_nvlink: bool) -> bool:
|
|
raise NotImplementedError()
|
|
|
|
|
|
def all_reduce_reg(fa: int, inp: torch.Tensor, out: torch.Tensor) -> None:
|
|
raise NotImplementedError()
|
|
|
|
|
|
def all_reduce_unreg(fa: int, inp: torch.Tensor, reg_buffer: torch.Tensor,
|
|
out: torch.Tensor) -> None:
|
|
raise NotImplementedError()
|
|
|
|
|
|
def dispose(fa: int) -> None:
|
|
raise NotImplementedError()
|
|
|
|
|
|
def meta_size() -> int:
|
|
raise NotImplementedError()
|
|
|
|
|
|
def register_buffer(fa: int, t: torch.Tensor, handles: List[str],
|
|
offsets: List[int]) -> None:
|
|
raise NotImplementedError()
|
|
|
|
|
|
def get_graph_buffer_ipc_meta(fa: int) -> Tuple[List[str], List[int]]:
|
|
raise NotImplementedError()
|
|
|
|
|
|
def register_graph_buffers(fa: int, handles: List[str],
|
|
offsets: List[List[int]]) -> None:
|
|
raise NotImplementedError()
|
|
|
|
|
|
# Add our new features here..
|
|
|
|
# broadcast
|
|
class Async_helper():
|
|
# For now, the comm and the other kernels are in the same stream, so we can remove the stream wait..
|
|
def wait(self,):
|
|
return True
|
|
|
|
|
|
def broadcast(tensor, src=0, group=None, async_op=False):
|
|
cdist.broadcast(tensor,src,group,async_op=True)
|
|
if async_op:
|
|
return Async_helper()
|
|
else:
|
|
pass
|
|
|
|
# w8a16
|
|
def linear_w8a16(x: torch.Tensor, qweight: torch.Tensor, scales:torch.Tensor,
|
|
group_size: int = -1, format: str = "TN")-> torch.Tensor:
|
|
return ixf_F.w8a16(x, qweight, scales, format="TN", group_size=group_size)
|
|
|
|
|
|
## lora sgmv / bgmv
|
|
def sbgmv_expand(x: torch.Tensor,
|
|
w_t_all: torch.Tensor,
|
|
y: torch.Tensor,
|
|
b_seq_start_loc: torch.Tensor = None,
|
|
seq_len_tensor: torch.Tensor = None,
|
|
lora_indices_tensor: torch.Tensor = None,
|
|
batches: int = -1,
|
|
max_seq_length: int = -1,
|
|
token_nums: int = -1,
|
|
add_input=True,
|
|
):
|
|
'''
|
|
x: inputs
|
|
w_t_all: lora weight
|
|
y: output
|
|
|
|
y += x@wt_t_all
|
|
'''
|
|
assert x.dtype in [torch.float16, torch.bfloat16, torch.float32]
|
|
assert w_t_all.dtype in [
|
|
torch.float16,
|
|
torch.bfloat16,
|
|
]
|
|
|
|
assert x.is_contiguous()
|
|
# assert y.is_contiguous()
|
|
if x.dtype == torch.float:
|
|
x = x.to(w_t_all.dtype)
|
|
|
|
if w_t_all.ndim == 4: # shape:(lora_num,1,size,rank)
|
|
assert w_t_all.size(1) == 1
|
|
w_t_all = w_t_all.squeeze(dim=1)
|
|
else:
|
|
assert w_t_all.ndim == 3 # shape:(lora_num,size,rank)
|
|
assert w_t_all.is_contiguous()
|
|
|
|
assert add_input == True
|
|
|
|
lora_indices = lora_indices_tensor.cpu().tolist()
|
|
lora_num = w_t_all.shape[0]
|
|
|
|
## 单一lora model, 且所有request均使用lora
|
|
if lora_num == 1 and all(x == lora_indices[0] for x in lora_indices):
|
|
if lora_indices[0] != -1:
|
|
w_t = w_t_all[0]
|
|
y += torch.matmul(x, w_t.t())
|
|
## 多个lora model
|
|
else:
|
|
## prefill
|
|
if batches != -1:
|
|
for i, lora_id, start, seq_len in zip(range(batches), lora_indices, b_seq_start_loc, seq_len_tensor):
|
|
if lora_id != -1:
|
|
xi = x[start: start+seq_len]
|
|
w_t = w_t_all[lora_id]
|
|
y[start:start+seq_len] += (xi @ w_t.t())
|
|
## decode
|
|
else:
|
|
batches = x.shape[0]
|
|
for i, lora_id in zip(range(batches), lora_indices):
|
|
if lora_id != -1:
|
|
xi = x[i].unsqueeze(0)
|
|
w_t = w_t_all[lora_id]
|
|
y[i] += (xi @ w_t.t()).squeeze(0)
|
|
|
|
return y
|
|
|
|
|
|
def sbgmv_shrink(x: torch.Tensor,
|
|
w_t_all: torch.Tensor,
|
|
y: torch.Tensor,
|
|
b_seq_start_loc: torch.Tensor = None,
|
|
seq_len_tensor: torch.Tensor = None,
|
|
lora_indices_tensor: torch.Tensor = None,
|
|
batches: int = -1,
|
|
max_seq_length: int = -1,
|
|
token_nums: int = -1,
|
|
scale: float = 1.0,):
|
|
"""
|
|
xx: inputs
|
|
w_t_all: lora weight
|
|
y: output
|
|
scale: float
|
|
|
|
y = x@w_t_all * scale
|
|
"""
|
|
assert x.dtype == w_t_all.dtype
|
|
assert x.dtype in [torch.float16, torch.bfloat16]
|
|
assert x.is_contiguous()
|
|
assert y.is_contiguous()
|
|
|
|
if w_t_all.ndim == 4: # shape:(lora_num,1,size,rank)
|
|
assert w_t_all.size(1) == 1
|
|
w_t_all = w_t_all.squeeze(dim=1)
|
|
else:
|
|
assert w_t_all.ndim == 3 # shape:(lora_num,size,rank)
|
|
assert w_t_all.is_contiguous()
|
|
|
|
lora_num = w_t_all.shape[0]
|
|
lora_indices = lora_indices_tensor.cpu().tolist()
|
|
|
|
## 单一lora model, 且所有request均使用lora
|
|
if lora_num == 1 and all(x == lora_indices[0] for x in lora_indices):
|
|
if lora_indices[0] != -1:
|
|
w_t = w_t_all[0]
|
|
y = torch.matmul(x, w_t.t()) * scale
|
|
## 多个lora model
|
|
else:
|
|
## prefill
|
|
if batches != -1:
|
|
for i, lora_id, start, seq_len in zip(range(batches), lora_indices, b_seq_start_loc, seq_len_tensor):
|
|
if lora_id != -1:
|
|
xi = x[start: start+seq_len]
|
|
w_t = w_t_all[lora_id]
|
|
y[start:start+seq_len] = (xi @ w_t.t())* scale
|
|
## decode
|
|
else:
|
|
batches = x.shape[0]
|
|
for i, lora_id in zip(range(batches), lora_indices):
|
|
if lora_id != -1:
|
|
xi = x[i].unsqueeze(0)
|
|
w_t = w_t_all[lora_id]
|
|
y[i] = (xi @ w_t.t()).squeeze(0) * scale
|
|
|
|
return y
|
|
|
|
# temporary fix for https://github.com/vllm-project/vllm/issues/5456
|
|
# TODO: remove this in v0.6.0
|
|
names_and_values = globals()
|
|
names_and_values_to_update = {}
|
|
# prepare variables to avoid dict size change during iteration
|
|
k, v, arg = None, None, None
|
|
fn_type = type(lambda x: x)
|
|
for k, v in names_and_values.items():
|
|
# find functions that are defined in this file and have torch.Tensor
|
|
# in their annotations. `arg == "torch.Tensor"` is used to handle
|
|
# the case when users use `import __annotations__` to turn type
|
|
# hints into strings.
|
|
if isinstance(v, fn_type) \
|
|
and v.__code__.co_filename == __file__ \
|
|
and any(arg is torch.Tensor or arg == "torch.Tensor"
|
|
for arg in v.__annotations__.values()):
|
|
names_and_values_to_update[k] = hint_on_error(v)
|
|
|
|
names_and_values.update(names_and_values_to_update)
|
|
del names_and_values_to_update, names_and_values, v, k, fn_type |