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docs/conf.py

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# General information about the project.
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project = "Tile Language <br>"
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author = "Tile Lang Contributors"

examples/bitnet-1.58b/modeling_bitnet.py

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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch LLaMA model."""
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from __future__ import annotations
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import math

examples/bitnet-1.58b/tokenization_bitnet.py

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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tokenization classes for LLaMA."""
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from __future__ import annotations
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import os

tilelang/autotuner/param.py

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"""The auto-tune parameters."""
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from __future__ import annotations
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import tilelang

tilelang/autotuner/tuner.py

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This module provides functionality for auto-tuning tilelang programs, including JIT compilation
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and performance optimization through configuration search.
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"""
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from __future__ import annotations
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import tilelang

tilelang/cache/__init__.py

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"""The cache utils with class and database persistence - Init file"""
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from __future__ import annotations
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from typing import Literal

tilelang/cache/kernel_cache.py

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"""The cache utils with class and database persistence - KernelCache Class"""
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from __future__ import annotations
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import json

tilelang/carver/analysis.py

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"""Analysis on TIR blocks, loops and functions."""
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from __future__ import annotations
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from typing_extensions import Literal

tilelang/carver/common_schedules.py

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# Modifications Copyright (c) Microsoft.
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# The code below is mostly copied from apache/tvm common_schedules.py in dlight.
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"""Common schedule strategies for TIR."""
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from __future__ import annotations
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from typing import Callable

tilelang/carver/matmul_analysis.py

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# pylint: disable=missing-docstring, invalid-name
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"""A GEMM schedule rule for GPU operators."""
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from __future__ import annotations
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from dataclasses import dataclass
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sm_version = arch.replace("sm_", "")
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return int(sm_version) if sm_version.isdigit() else -1
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def analysis_tensorcore_tags(
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sch: tir.Schedule, block: BlockRV, target: Target
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) -> bool | dict:
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def analysis_tensorcore_tags(sch: tir.Schedule, block: BlockRV, target: Target) -> bool | dict:
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tags: dict[str, list[int] | int] = {}
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block_stmt = sch.get(block)
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