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'transforms' Submodule

Introduction

from streamtensor import transforms

Transformations for StreamTensor compiler.

This module provides a set of transformations for the StreamTensor compiler. These transformations are used to optimize the input MLIR module targeting spatial accelerators. The module is transformed in place.

apply_bufferization(module, task_chain_merging=False)

Apply bufferization.

Parameters:

Name Type Description Default
module Module

The module to be transformed.

 required
task_chain_merging bool

Whether to merge task chains.

 False

apply_dataflow_optimization_transforms(module, max_widen_bitwidth=512, constant_folding=False)

Apply dataflow optimization transforms.

Parameters:

Name Type Description Default
module Module

The module to be transformed.

 required
max_widen_bitwidth int

The maximum bitwidth to widen.

 512
constant_folding bool

Whether to perform constant folding.

 False

apply_greedy_kernel_fusion(module, entry_name, max_fusion_cost, dot_file=None, fused_dot_file=None, delete_sequence=True)

Apply greedy kernel fusion.

Parameters:

Name Type Description Default
module Module

The module to be transformed.

 required
entry_name str

The entry function name.

 required
max_fusion_cost int

The maximum fusion cost.

 required
dot_file Optional[str]

The DOT file to print the kernel graph.

 None
fused_dot_file Optional[str]

The DOT file to print the post-fusion kernel graph.

 None
delete_sequence bool

Whether to delete the transform sequence after transform.

 True

apply_hls_codegen(module, num_uram=960, num_bram=4032, size_lutram_in_bit=36700000, num_hbm_port=32, create_kernel_wrapper=True)

Apply HLS C++ code generation transforms.

Parameters:

Name Type Description Default
module Module

The module to be transformed.

 required
num_uram int

The number of URAMs.

 960
num_bram int

The number of BRAMs.

 4032
size_lutram_in_bit int

The size of LUTRAM in bits.

 36700000
num_hbm_port int

The number of HBM ports.

 32
create_kernel_wrapper bool

Whether to create kernel wrapper function.

 True

apply_intensity_aware_linalg_tiling(module, entry_name, default_tile_size, overall_unroll_size, convert_linalg_to_dataflow, op_ii_map=None, dot_file=None, delete_sequence=True)

Apply intensity-aware linalg tiling.

Parameters:

Name Type Description Default
module Module

The module to be transformed.

 required
entry_name str

The entry function name.

 required
default_tile_size int

The default tile size.

 required
overall_unroll_size int

The overall unroll size.

 required
convert_linalg_to_dataflow bool

Whether to convert tiled linalg op to dataflow kernel.

 required
op_ii_map Optional[Dict[str, int]]

The map from op name to its initiation interval on hardware.

 None
dot_file Optional[str]

The DOT file to print the linalg graph.

 None
delete_sequence bool

Whether to delete the transform sequence after transform.

 True

apply_linalg_optimization_transforms(module, fake_quantize=False, quantize_bitwidth=8, constant_quantize_bitwidth=8)

Apply linalg optimization transforms.

Parameters:

Name Type Description Default
module Module

The module to be transformed.

 required
fake_quantize bool

Whether to apply fake quantization.

 False
quantize_bitwidth int

The bitwidth for fake quantization.

 8
constant_quantize_bitwidth int

The bitwidth of constants for fake quantization.

 8

apply_linear_programming_stream_sizing(module, entry_name, pipeline_rewinding=True, stream_size_map=None, stream_full_size=False, dot_file=None)

Apply linear programming stream sizing.

Parameters:

Name Type Description Default
module Module

The module to be transformed.

 required
entry_name str

The entry function name.

 required
pipeline_rewinding bool

Whether to enable pipeline rewinding.

 True
stream_size_map Optional[Dict[Tuple[str, str], int]]

The overriding map from edge to stream size.

 None
stream_full_size bool

Set all stream sizes to the maximum (token number).

 False
dot_file Optional[str]

The DOT file to print the stream graph.

 None

apply_runtime_codegen(module, host_func_name, timeout_in_ms=1000)

Apply runtime C++ code generation transforms.

Parameters:

Name Type Description Default
module Module

The module to be transformed.

 required
host_func_name str

The name of the host function.

 required
timeout_in_ms int

The timeout in milliseconds.

 1000

apply_tensor_to_dataflow_conversion(module)

Apply tensor to dataflow conversion.

Parameters:

Name Type Description Default
module Module

The module to be transformed.

 required

construct_kernel_fusion_transform_sequence(target, design_space)

Construct a transform sequence for applying kernel fusion.

Parameters:

Name Type Description Default
target BlockArgument

The handle of the target op.

 required
design_space KernelFusionDesignSpace

The design space of kernel fusion.

 required

Returns:

Type Description
List[Value]

A list of transformed values, which is empty in this case.

Raises:

Type Description
ValueError

If the kernel is not a KernelOp.

construct_linalg_tiling_transform_sequence(target, design_space, convert_linalg_to_dataflow=True)

Construct a transform sequence for applying linalg tiling.

The following parameters are required in the input design space
  • parallel_tile_sizes: The parallel tile sizes.
  • reduction_tile_sizes: The reduction tile sizes.
  • unroll_sizes: The unroll sizes.
  • inputs_vec_sizes: The input vector sizes.
  • outputs_vec_sizes: The output vector sizes.
  • permutation: The permutation of the loop order.

Parameters:

Name Type Description Default
target BlockArgument

The handle of the target op.

 required
design_space LinalgTilingDesignSpace

The design space of linalg tiling.

 required
convert_linalg_to_dataflow bool

Whether to convert tiled linalg op to dataflow kernel.

 True

Returns:

Type Description
List[Value]

A list of transformed values, which is empty in this case.

Raises:

Type Description
ValueError

If the required attributes are not found.