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e489ad7a21
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
229 lines
7.7 KiB
Python
229 lines
7.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import List, Optional, Tuple
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import torch
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from vllm.logger import init_logger
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logger = init_logger(__name__)
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try:
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import intel_extension_for_pytorch as ipex
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except ImportError as e:
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logger.warning("Import error msg: %s", e.msg)
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class ipex_ops:
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@staticmethod
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def _reshape_activation_tensor(
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x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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num = x.size(0)
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d = x.size(1) // 2
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x = x.reshape(num, 2, d)
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x1, x2 = torch.chunk(x, chunks=2, dim=1)
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x1 = x1.reshape(num, d)
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x2 = x2.reshape(num, d)
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return x1, x2
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@staticmethod
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def silu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
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ipex.llm.functional.silu_and_mul(x, out)
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@staticmethod
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def gelu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
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ipex.llm.functional.gelu_and_mul(x, out)
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@staticmethod
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def gelu_tanh_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
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ipex.llm.functional.gelu_and_mul(x, out)
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@staticmethod
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def gelu_fast(x: torch.Tensor) -> torch.Tensor:
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return torch.nn.functional.gelu(x)
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@staticmethod
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def gelu_new(x: torch.Tensor) -> torch.Tensor:
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return torch.nn.functional.gelu(x)
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@staticmethod
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def gelu_quick(out: torch.Tensor, x: torch.Tensor) -> None:
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ipex.llm.functional.gelu_quick(x, out)
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@staticmethod
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def paged_attention_v1(
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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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context_lens: torch.Tensor,
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block_size: int,
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max_context_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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assert kv_cache_dtype == "auto"
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num_heads = out.size(1)
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num_queries_per_tokens = num_heads // num_kv_heads
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ipex.llm.modules.PagedAttention.single_query_kv_attention(
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out,
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query.contiguous(),
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key_cache.view_as(value_cache),
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value_cache,
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num_queries_per_tokens,
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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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@staticmethod
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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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context_lens: torch.Tensor,
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block_size: int,
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max_context_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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assert kv_cache_dtype == "auto"
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num_heads = out.size(1)
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num_queries_per_tokens = num_heads // num_kv_heads
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ipex.llm.modules.PagedAttention.single_query_kv_attention(
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out,
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query.contiguous(),
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key_cache.view_as(value_cache),
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value_cache,
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num_queries_per_tokens,
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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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@staticmethod
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def rotary_embedding(
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positions: torch.Tensor, # [batch_size, seq_len]
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query: torch.Tensor, # [batch_size, seq_len, num_heads*head_size]
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key: torch.Tensor, # [batch_size, seq_len, num_kv_heads*head_size]
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head_size: int,
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cos_sin_cache: torch.Tensor, # [cos_sin_dim, rot_dim]
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is_neox: bool,
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) -> None:
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rot_dim = cos_sin_cache.size(1)
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ipex.llm.functional.rotary_embedding_batched(positions, query, key,
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head_size, cos_sin_cache,
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is_neox, rot_dim)
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@staticmethod
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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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ipex.llm.functional.rotary_embedding_batched(positions, query, key,
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head_size, cos_sin_cache,
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is_neox, rot_dim,
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cos_sin_cache_offsets)
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@staticmethod
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def rms_norm(input: torch.Tensor, weight: torch.Tensor,
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epsilon: float) -> torch.Tensor:
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return ipex.llm.functional.rms_norm(input, weight, epsilon)
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@staticmethod
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def fused_add_rms_norm(input: torch.Tensor, residual: torch.Tensor,
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weight: torch.Tensor, epsilon: float) -> None:
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tmp = ipex.llm.functional.add_rms_norm(residual, input, weight, None,
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epsilon, True)
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input.copy_(tmp)
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@staticmethod
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def varlen_attention(
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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out: torch.Tensor,
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seqlen_q: torch.Tensor,
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seqlen_k: torch.Tensor,
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max_seqlen_q: int,
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max_seqlen_k: int,
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pdropout: float,
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softmax_scale: float,
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zero_tensors: bool,
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is_causal: bool,
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return_softmax: bool,
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gen_: torch.Generator,
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logits_soft_cap: float,
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) -> None:
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ipex.llm.functional.varlen_attention(query.contiguous(),
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key.contiguous(),
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value.contiguous(), out,
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seqlen_q.int(), seqlen_k.int(),
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max_seqlen_q, max_seqlen_k,
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pdropout, softmax_scale,
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zero_tensors, is_causal,
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return_softmax, gen_,
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logits_soft_cap)
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@staticmethod
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def reshape_and_cache(
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key: torch.Tensor,
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value: torch.Tensor,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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slot_mapping: 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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assert kv_cache_dtype == "auto"
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ipex.llm.modules.PagedAttention.reshape_and_cache(
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key, value, key_cache, value_cache, slot_mapping)
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@staticmethod
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def copy_blocks(key_caches: List[torch.Tensor],
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value_caches: List[torch.Tensor],
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block_mapping: torch.Tensor) -> None:
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torch.xpu.copy_blocks( # type: ignore
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key_caches,
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value_caches,
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block_mapping,
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)
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@staticmethod
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def swap_blocks(src: torch.Tensor, dst: torch.Tensor,
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block_mapping: torch.Tensor) -> None:
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torch.xpu.swap_blocks(src, dst, block_mapping) # type: ignore
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