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279 lines
9.7 KiB
Python
279 lines
9.7 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2025 - 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
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#
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# Use of this software is governed by the terms and conditions of the
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# NVIDIA End User License Agreement (EULA), available at:
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# https://docs.nvidia.com/cutlass/media/docs/pythonDSL/license.html
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#
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# Any use, reproduction, disclosure, or distribution of this software
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# and related documentation outside the scope permitted by the EULA
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# is strictly prohibited.
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from typing import Tuple
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from cutlass.cutlass_dsl import (
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Boolean,
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Integer,
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Int32,
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min,
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extract_mlir_values,
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new_from_mlir_values,
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dsl_user_op,
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T,
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)
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from cutlass._mlir import ir
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from cutlass.utils.static_persistent_tile_scheduler import (
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WorkTileInfo,
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)
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import cutlass.cute as cute
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class ClcDynamicPersistentTileSchedulerParams:
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"""A class to represent parameters for a dynamic persistent tile scheduler.
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This class is designed to manage and compute the layout of clusters and tiles
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in a batched gemm problem.
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:ivar cluster_shape_mn: Shape of the cluster in (m, n) dimensions (K dimension cta count must be 1).
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:type cluster_shape_mn: tuple
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"""
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def __init__(
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self,
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problem_shape_ntile_mnl: cute.Shape,
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cluster_shape_mnk: cute.Shape,
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*,
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loc=None,
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ip=None,
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):
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"""
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Initializes the ClcDynamicPersistentTileSchedulerParams with the given parameters.
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:param problem_shape_ntile_mnl: The shape of the problem in terms of
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number of CTA (Cooperative Thread Array) in (m, n, l) dimensions.
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:type problem_shape_ntile_mnl: cute.Shape
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:param cluster_shape_mnk: The shape of the cluster in (m, n) dimensions.
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:type cluster_shape_mnk: cute.Shape
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:raises ValueError: If cluster_shape_k is not 1.
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"""
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if cluster_shape_mnk[2] != 1:
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raise ValueError(f"unsupported cluster_shape_k {cluster_shape_mnk[2]}")
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self.problem_shape_ntile_mnl = problem_shape_ntile_mnl
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# cluster_shape_mnk is kept for reconstruction
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self._cluster_shape_mnk = cluster_shape_mnk
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self.cluster_shape_mn = cluster_shape_mnk[:2]
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self._loc = loc
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def __extract_mlir_values__(self):
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values, self._values_pos = [], []
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for obj in [self.problem_shape_ntile_mnl, self._cluster_shape_mnk]:
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obj_values = extract_mlir_values(obj)
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values += obj_values
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self._values_pos.append(len(obj_values))
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return values
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def __new_from_mlir_values__(self, values):
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obj_list = []
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for obj, n_items in zip(
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[self.problem_shape_ntile_mnl, self._cluster_shape_mnk], self._values_pos
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):
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obj_list.append(new_from_mlir_values(obj, values[:n_items]))
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values = values[n_items:]
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return ClcDynamicPersistentTileSchedulerParams(
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*(tuple(obj_list)), loc=self._loc
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)
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@dsl_user_op
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def get_grid_shape(self, *, loc=None, ip=None) -> Tuple[Integer, Integer, Integer]:
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"""
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Computes the grid shape based on the problem shape and cluster shape.
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:return: the grid is the CTA numbers that has aligned with cluster shape.
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"""
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problem_ceiling_cta_mnl = cute.round_up(
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self.problem_shape_ntile_mnl, self._cluster_shape_mnk
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)
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return problem_ceiling_cta_mnl
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class ClcDynamicPersistentTileScheduler:
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"""A scheduler for dynamic persistent tile execution in CUTLASS/CuTe kernels.
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:ivar params: Tile schedule related params, including cluster shape.
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:type params: ClcDynamicPersistentTileSchedulerParams
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:ivar cta_id_in_cluster: ID of the CTA within its cluster
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:type cta_id_in_cluster: cute.Coord
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:ivar _num_tiles_executed: Counter for executed tiles
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:type _num_tiles_executed: Int32
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"""
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def __init__(
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self,
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params: ClcDynamicPersistentTileSchedulerParams,
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cta_id_in_cluster: cute.Coord,
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num_tiles_executed: Int32,
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clc_response_ptr: cute.Pointer,
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block_idx: Tuple[Integer, Integer, Integer],
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):
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"""
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Initializes the ClcDynamicPersistentTileScheduler with the given parameters.
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:param params: Tile schedule related params, including cluster shape.
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:type params: ClcDynamicPersistentTileSchedulerParams
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:param cta_id_in_cluster: ID of the CTA within its cluster.
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:type cta_id_in_cluster: cute.Coord
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:param num_tiles_executed: Counter for executed tiles.
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:type num_tiles_executed: Int32
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:param clc_response_ptr: Pointer of the clc rsponse.
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:type clc_response_ptr: cute.Pointer
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:param block_idx: The block index.
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:type block_idx: Tuple[Integer, Integer, Integer]
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"""
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self.params = params
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self.cta_id_in_cluster = cta_id_in_cluster
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self._num_tiles_executed = num_tiles_executed
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self._clc_response_ptr = clc_response_ptr
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self._block_idx = block_idx
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def __extract_mlir_values__(self) -> list[ir.Value]:
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values = extract_mlir_values(self.cta_id_in_cluster)
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values.extend(extract_mlir_values(self._num_tiles_executed))
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values.extend(extract_mlir_values(self._clc_response_ptr))
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values.extend(extract_mlir_values(self._block_idx))
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return values
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def __new_from_mlir_values__(
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self, values: list[ir.Value]
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) -> "ClcDynamicPersistentTileScheduler":
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assert len(values) == 8
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new_cta_id_in_cluster = new_from_mlir_values(
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self.cta_id_in_cluster, values[0:3]
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)
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new_num_tiles_executed = new_from_mlir_values(
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self._num_tiles_executed, [values[3]]
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)
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new_clc_response_ptr = new_from_mlir_values(self._clc_response_ptr, [values[4]])
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new_block_idx = new_from_mlir_values(self._block_idx, values[5:8])
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return ClcDynamicPersistentTileScheduler(
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self.params,
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new_cta_id_in_cluster,
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new_num_tiles_executed,
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new_clc_response_ptr,
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new_block_idx,
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)
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@dsl_user_op
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@staticmethod
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def create(
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params: ClcDynamicPersistentTileSchedulerParams,
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block_idx: Tuple[Integer, Integer, Integer],
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grid_dim: Tuple[Integer, Integer, Integer],
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clc_response_ptr: cute.Pointer,
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*,
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loc=None,
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ip=None,
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):
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"""Initialize the dynamic persistent tile scheduler.
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:param params: Parameters for the persistent
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tile scheduler.
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:type params: ClcDynamicPersistentTileSchedulerParams
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:param block_idx: The 3d block index in the format (bidx, bidy, bidz).
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:type block_idx: Tuple[Integer, Integer, Integer]
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:param grid_dim: The 3d grid dimensions for kernel launch.
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:type grid_dim: Tuple[Integer, Integer, Integer]
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:return: A ClcDynamicPersistentTileScheduler object.
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:rtype: ClcDynamicPersistentTileScheduler
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"""
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params = params
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bidx, bidy, bidz = block_idx
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# CTA id in the cluster
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cta_id_in_cluster = (
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Int32(bidx % params.cluster_shape_mn[0]),
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Int32(bidy % params.cluster_shape_mn[1]),
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Int32(0),
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)
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# Initialize number of tiles executed to zero
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num_tiles_executed = Int32(0)
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# Initialize clc response pointer
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clc_response_ptr = clc_response_ptr
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# The block index
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block_idx = block_idx
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return ClcDynamicPersistentTileScheduler(
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params,
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cta_id_in_cluster,
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num_tiles_executed,
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clc_response_ptr,
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block_idx,
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)
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# called by host
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@dsl_user_op
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def get_grid_shape(
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params: ClcDynamicPersistentTileSchedulerParams,
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*,
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loc=None,
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ip=None,
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) -> Tuple[Integer, Integer, Integer]:
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"""Calculates the grid shape to be launched on GPU using problem shape,
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threadblock shape, and active cluster size.
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:param params: Parameters for grid shape calculation.
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:type params: ClcDynamicPersistentTileSchedulerParams
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:return: The calculated 3d grid shape.
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:rtype: Tuple[Integer, Integer, Integer]
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"""
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return params.get_grid_shape(loc=loc, ip=ip)
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@dsl_user_op
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def work_tile_info_from_clc_response(
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self, result_addr: cute.Pointer, *, loc=None, ip=None
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) -> WorkTileInfo:
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"""
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Simulates parsing CLC response data in Python.
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result_addr: 16-byte response data (simulating shared memory access)
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"""
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m_idx, n_idx, l_idx, vld = cute.arch.clc_response(result_addr, loc=loc, ip=ip)
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cute.arch.fence_proxy(
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"async.shared",
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space="cta",
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)
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cta_idx_in_cluster, cta_idy_in_cluster, _ = self.cta_id_in_cluster
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cur_tile_coord = (m_idx + cta_idx_in_cluster, n_idx + cta_idy_in_cluster, l_idx)
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return WorkTileInfo(cur_tile_coord, vld)
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@dsl_user_op
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def get_current_work(self, *, loc=None, ip=None) -> WorkTileInfo:
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smem_addr = self._clc_response_ptr
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work_tile = self.work_tile_info_from_clc_response(smem_addr)
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return work_tile
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@dsl_user_op
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def initial_work_tile_info(self, *, loc=None, ip=None) -> WorkTileInfo:
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bidx, bidy, bidz = self._block_idx
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return WorkTileInfo((bidx, bidy, bidz), True)
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@dsl_user_op
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def advance_to_next_work(self, mbarrier_addr, loc=None, ip=None):
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# Query new work tile
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with cute.arch.elect_one():
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cute.arch.issue_clc_query(
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mbarrier_addr, self._clc_response_ptr, loc=loc, ip=ip
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)
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self._num_tiles_executed += Int32(1)
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@property
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def num_tiles_executed(self) -> Int32:
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return self._num_tiles_executed
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