ttir-builder
ttir-builder is a tool for creating TTIR operations. It provides support for MLIR modules to be generated from user-constructed ops, lowered into TTNN or TTMetal backends, and finally translated into executable flatbuffers. Or you can do all three at once!
Building
- Build tt-mlir
- Build
ttrt - Generate ttsys file from the system you want to compile for using
ttrt. This will create attrt-artifactsfolder containing asystem_desc.ttsysfile.
ttrt query --save-artifacts
- Export this file in your environment using
export SYSTEM_DESC_PATH=/path/to/system_desc.ttsys.ttir_builder.utilsuses thesystem_desc.ttsysfile as it runs a pass over an MLIR module to the TTNN or TTMetal backend.
Getting started
TTIRBuilder is a builder class providing the API for creating TTIR ops. The python package ttir_builder contains everything needed to create ops through a TTIRBuilder object. ttir_builder.utils contains the APIs for wrapping op-creating-functions into MLIR modules and flatbuffers files.
from ttir_builder import TTIRBuilder
from ttir_builder.utils import compile_to_flatbuffer
For the full set of supported TTIR ops, see tools/ttir-builder/builder.py.
For more detailed information on available APIs, see tools/ttir-builder/builder.py and tools/ttir-builder/utils.py.
Creating a TTIR module
build_mlir_module defines an MLIR module specified as a python function. It wraps fn in a MLIR FuncOp then wraps that in an MLIR module, and finally ties arguments of that FuncOp to test function inputs. It will instantiate and pass a TTIRBuilder object as the last argument of fn.
def build_mlir_module(
fn: Callable,
inputs_shapes: List[Shape],
inputs_types: Optional[List[Union[torch.dtype, TypeInfo]]] = None,
mesh_shape: Optional[Tuple[int, int]] = None,
module_dump: bool = False,
base: Optional[str] = None,
output_root: str = ".",
)
Example
from ttir_builder.utils import build_mlir_module
from ttir_builder import Operand, TTIRBuilder
shapes = [(32, 32), (32, 32), (32, 32)]
def model(in0: Operand, in1: Operand, in2: Operand, builder: TTIRBuilder):
add_0 = builder.add(in0, in1)
multiply_1 = builder.multiply(in1, add_0)
return builder.multiply(multiply_1, in2)
module, builder = build_mlir_module(model, shapes)
Returns
An MLIR module containing an MLIR op graph defined by test_fn and the TTIRBuilder object used to create it
module {
func.func @model(%arg0: tensor<32x32xf32>, %arg1: tensor<32x32xf32>, %arg2: tensor<32x32xf32>) -> tensor<32x32xf32> {
%0 = ttir.empty() : tensor<32x32xf32>
%1 = "ttir.add"(%arg0, %arg1, %0) : (tensor<32x32xf32>, tensor<32x32xf32>, tensor<32x32xf32>) -> tensor<32x32xf32>
%2 = ttir.empty() : tensor<32x32xf32>
%3 = "ttir.multiply"(%arg1, %1, %2) : (tensor<32x32xf32>, tensor<32x32xf32>, tensor<32x32xf32>) -> tensor<32x32xf32>
%4 = ttir.empty() : tensor<32x32xf32>
%5 = "ttir.multiply"(%3, %arg2, %4) : (tensor<32x32xf32>, tensor<32x32xf32>, tensor<32x32xf32>) -> tensor<32x32xf32>
return %5 : tensor<32x32xf32>
}
}
Running a pipeline
run_pipeline runs a pass on the TTIR module to lower it into a backend, using pipeline_fn. You can pass pipeline_fn in as one of the following: ttir_to_ttnn_backend_pipeline, ttir_to_ttmetal_backend_pipeline (both found in ttmlir.passes), or a custom pipeline built with create_custom_pipeline_fn. The default if none is provided is the TTNN pipeline.
def run_pipeline(
module,
pipeline_fn: Callable = ttir_to_ttnn_backend_pipeline,
pipeline_options: List[str] = None,
dump_to_file: bool = True,
output_file_name: str = "test.mlir",
system_desc_path: Optional[str] = None,
mesh_shape: Optional[Tuple[int, int]] = None,
argument_types_string: Optional[str] = None,
)
TTNN example
Let's expand on our previous example
from ttir_builder.utils import build_mlir_module, run_pipeline
from ttir_builder import Operand, TTIRBuilder
from ttmlir.passes import ttir_to_ttnn_backend_pipeline
shapes = [(32, 32), (32, 32), (32, 32)]
def model(in0: Operand, in1: Operand, in2: Operand, builder: TTIRBuilder):
add_0 = builder.add(in0, in1)
multiply_1 = builder.multiply(in1, add_0)
return builder.multiply(multiply_1, in2)
module, builder = build_mlir_module(model, shapes)
ttnn_module = run_pipeline(module, ttir_to_ttnn_backend_pipeline)
Returns
An MLIR module lowered into TTNN
#dram = #ttnn.buffer_type<dram>
#system_desc = #ttcore.system_desc<[{role = host, target_triple = "x86_64-pc-linux"}], [{arch = <wormhole_b0>, grid = 8x8, coord_translation_offsets = 18x18, l1_size = 1499136, num_dram_channels = 12, dram_channel_size = 1073741824, noc_l1_address_align_bytes = 16, pcie_address_align_bytes = 32, noc_dram_address_align_bytes = 32, l1_unreserved_base = 97248, erisc_l1_unreserved_base = 69632, dram_unreserved_base = 32, dram_unreserved_end = 1073158336, physical_helper_cores = {dram = [ 0x0, 0x1, 0x2, 0x3, 0x4, 0x5, 0x6, 0x7, 0x8, 0x9, 0x10, 0x11] eth_inactive = [ 16x18, 16x19, 16x20, 16x21, 16x22, 16x23, 16x24, 16x25, 17x19, 17x20, 17x22, 17x23, 17x24]}, supported_data_types = [<f32>, <f16>, <bf16>, <bfp_f8>, <bfp_bf8>, <bfp_f4>, <bfp_bf4>, <bfp_f2>, <bfp_bf2>, <u32>, <u16>, <u8>, <si32>], supported_tile_sizes = [ 4x16, 16x16, 32x16, 4x32, 16x32, 32x32], num_cbs = 32, num_compute_threads = 1, num_datamovement_threads = 2}], [0], [3 : i32], [ 0x0x0x0]>
#ttnn_layout = #ttnn.ttnn_layout<(d0, d1) -> (d0, d1), <1x1>, memref<1x1x!ttcore.tile<32x32, f32>, #dram>, <interleaved>>
module {
ttcore.device_module {
builtin.module attributes {ttcore.system_desc = #system_desc} {
ttcore.device @default_device = <workerGrid = #ttcore.grid<8x8, (d0, d1) -> (0, d0, d1)>, l1Map = (d0, d1, d2)[s0] -> (0, d0, d1, d2 + s0), dramMap = (d0, d1, d2)[s0, s1, s2, s3, s4, s5] -> (0, 0, (((d0 * s1) * (s2 * s3) + d1 * (s2 * s3) + d2) floordiv s4) mod 12, ((d0 * s1) * (s2 * s3) + d1 * (s2 * s3) + d2) floordiv (s4 * 12) + ((d0 * s1) * (s2 * s3) + d1 * (s2 * s3) + d2) mod s4 + s5), meshShape = , chipIds = [0]>
func.func @model(%arg0: tensor<32x32xf32, #ttnn_layout>, %arg1: tensor<32x32xf32, #ttnn_layout>, %arg2: tensor<32x32xf32, #ttnn_layout>) -> tensor<32x32xf32, #ttnn_layout> {
%0 = "ttnn.abs"(%arg0) : (tensor<32x32xf32, #ttnn_layout>) -> tensor<32x32xf32, #ttnn_layout>
"ttnn.deallocate"(%arg0) <{force = false}> : (tensor<32x32xf32, #ttnn_layout>) -> ()
%1 = "ttnn.multiply"(%arg1, %0) : (tensor<32x32xf32, #ttnn_layout>, tensor<32x32xf32, #ttnn_layout>) -> tensor<32x32xf32, #ttnn_layout>
"ttnn.deallocate"(%0) <{force = false}> : (tensor<32x32xf32, #ttnn_layout>) -> ()
"ttnn.deallocate"(%arg1) <{force = false}> : (tensor<32x32xf32, #ttnn_layout>) -> ()
%2 = "ttnn.multiply"(%1, %arg2) : (tensor<32x32xf32, #ttnn_layout>, tensor<32x32xf32, #ttnn_layout>) -> tensor<32x32xf32, #ttnn_layout>
"ttnn.deallocate"(%1) <{force = false}> : (tensor<32x32xf32, #ttnn_layout>) -> ()
"ttnn.deallocate"(%arg2) <{force = false}> : (tensor<32x32xf32, #ttnn_layout>) -> ()
return %2 : tensor<32x32xf32, #ttnn_layout>
}
}
}
}
TTMetal example
Let's use the same code for TTMetal that was used in the TTNN example but change the pipeline_fn to ttir_to_ttmetal_backend_pipeline. Only one or the other can be run on a module since run_pipeline modifies the module in place. Note that while all TTIR ops supported by builder can be lowered to TTNN, not all can be lowered to TTMetal yet. Adding documentation to specify what ops can be lowered to TTMetal is in the works.
from ttmlir.passes import ttir_to_ttmetal_backend_pipeline
ttmetal_module = run_pipeline(module, ttir_to_ttmetal_backend_pipeline)
Returns
An MLIR module lowered into TTMetal
#l1 = #ttcore.memory_space<l1>
#system_desc = #ttcore.system_desc<[{role = host, target_triple = "x86_64-pc-linux-gnu"}], [{arch = <wormhole_b0>, grid = 8x8, coord_translation_offsets = 18x18, l1_size = 1499136, num_dram_channels = 12, dram_channel_size = 1073741824, noc_l1_address_align_bytes = 16, pcie_address_align_bytes = 32, noc_dram_address_align_bytes = 32, l1_unreserved_base = 1024, erisc_l1_unreserved_base = 1024, dram_unreserved_base = 1024, dram_unreserved_end = 1073741824, physical_helper_cores = {dram = [ 8x0, 9x0, 10x0, 8x1, 9x1, 10x1, 8x2, 9x2, 10x2, 8x3, 9x3, 10x3]}, supported_data_types = [<f32>, <f16>, <bf16>, <bfp_f8>, <bfp_bf8>, <bfp_f4>, <bfp_bf4>, <bfp_f2>, <bfp_bf2>, <u32>, <u16>, <u8>, <si32>], supported_tile_sizes = [ 4x16, 16x16, 32x16, 4x32, 16x32, 32x32], num_cbs = 32, num_compute_threads = 1, num_datamovement_threads = 2}], [0], [3 : i32], [ 0x0x0x0]>
module {
ttcore.device_module {
builtin.module attributes {ttcore.system_desc = #system_desc} {
ttcore.device @default_device = <workerGrid = #ttcore.grid<8x8, (d0, d1) -> (0, d0, d1)>, l1Map = (d0, d1, d2)[s0] -> (0, d0, d1, d2 + s0), dramMap = (d0, d1, d2)[s0, s1, s2, s3, s4, s5] -> (0, 0, (((d0 * s1) * (s2 * s3) + d1 * (s2 * s3) + d2) floordiv s4) mod 12, ((d0 * s1) * (s2 * s3) + d1 * (s2 * s3) + d2) floordiv (s4 * 12) + ((d0 * s1) * (s2 * s3) + d1 * (s2 * s3) + d2) mod s4 + s5), meshShape = , chipIds = [0]>
func.func @model(%arg0: memref<32x32xf32>, %arg1: memref<32x32xf32>, %arg2: memref<32x32xf32>) -> memref<32x32xf32> {
%0 = "ttmetal.create_buffer"() <{address = 9216 : i64}> : () -> memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>
%1 = "ttmetal.create_buffer"() <{address = 1024 : i64}> : () -> memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>
"ttmetal.enqueue_write_buffer"(%arg0, %1) : (memref<32x32xf32>, memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>) -> ()
"ttmetal.enqueue_program"(%1, %0, %1, %0) <{cb_ports = array<i64: 0, 1>, kernelConfigs = [#ttmetal.noc_config<@datamovement_kernel0, #ttmetal.core_range<0x0, 1x1>, #ttmetal.kernel_args< ct_args = [<cb_port[0]>, <cb_port[1]>]>, noc0>, #ttmetal.compute_config<@compute_kernel1, #ttmetal.core_range<0x0, 1x1>, #ttmetal.kernel_args< ct_args = [<cb_port[0]>, <cb_port[1]>]>, hifi4, false, false, [default]>], operandSegmentSizes = array<i32: 2, 2>}> : (memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>) -> ()
"ttmetal.deallocate_buffer"(%1) : (memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>) -> ()
%2 = "ttmetal.create_buffer"() <{address = 1024 : i64}> : () -> memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>
%3 = "ttmetal.create_buffer"() <{address = 5120 : i64}> : () -> memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>
"ttmetal.enqueue_write_buffer"(%arg1, %3) : (memref<32x32xf32>, memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>) -> ()
"ttmetal.enqueue_program"(%3, %2, %3, %2) <{cb_ports = array<i64: 0, 1>, kernelConfigs = [#ttmetal.noc_config<@datamovement_kernel2, #ttmetal.core_range<0x0, 1x1>, #ttmetal.kernel_args< ct_args = [<cb_port[0]>, <cb_port[1]>]>, noc0>, #ttmetal.compute_config<@compute_kernel3, #ttmetal.core_range<0x0, 1x1>, #ttmetal.kernel_args< ct_args = [<cb_port[0]>, <cb_port[1]>]>, hifi4, false, false, [default]>], operandSegmentSizes = array<i32: 2, 2>}> : (memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>) -> ()
"ttmetal.deallocate_buffer"(%3) : (memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>) -> ()
%4 = "ttmetal.create_buffer"() <{address = 13312 : i64}> : () -> memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>
"ttmetal.enqueue_program"(%0, %2, %4, %0, %2, %4) <{cb_ports = array<i64: 0, 1, 2>, kernelConfigs = [#ttmetal.noc_config<@datamovement_kernel4, #ttmetal.core_range<0x0, 1x1>, #ttmetal.kernel_args< ct_args = [<cb_port[0]>, <cb_port[1]>, <cb_port[2]>]>, noc0>, #ttmetal.noc_config<@datamovement_kernel5, #ttmetal.core_range<0x0, 1x1>, #ttmetal.kernel_args< ct_args = [<cb_port[0]>, <cb_port[1]>, <cb_port[2]>]>, noc1>, #ttmetal.compute_config<@compute_kernel6, #ttmetal.core_range<0x0, 1x1>, #ttmetal.kernel_args< ct_args = [<cb_port[0]>, <cb_port[1]>, <cb_port[2]>]>, hifi4, false, false, [default]>], operandSegmentSizes = array<i32: 3, 3>}> : (memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>) -> ()
"ttmetal.deallocate_buffer"(%0) : (memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>) -> ()
"ttmetal.deallocate_buffer"(%2) : (memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>) -> ()
%5 = "ttmetal.create_buffer"() <{address = 1024 : i64}> : () -> memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>
%6 = "ttmetal.create_buffer"() <{address = 5120 : i64}> : () -> memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>
"ttmetal.enqueue_write_buffer"(%arg1, %6) : (memref<32x32xf32>, memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>) -> ()
"ttmetal.enqueue_program"(%6, %5, %6, %5) <{cb_ports = array<i64: 0, 1>, kernelConfigs = [#ttmetal.noc_config<@datamovement_kernel7, #ttmetal.core_range<0x0, 1x1>, #ttmetal.kernel_args< ct_args = [<cb_port[0]>, <cb_port[1]>]>, noc0>, #ttmetal.compute_config<@compute_kernel8, #ttmetal.core_range<0x0, 1x1>, #ttmetal.kernel_args< ct_args = [<cb_port[0]>, <cb_port[1]>]>, hifi4, false, false, [default]>], operandSegmentSizes = array<i32: 2, 2>}> : (memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>) -> ()
"ttmetal.deallocate_buffer"(%6) : (memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>) -> ()
%7 = "ttmetal.create_buffer"() <{address = 17408 : i64}> : () -> memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>
"ttmetal.enqueue_program"(%5, %4, %7, %5, %4, %7) <{cb_ports = array<i64: 0, 1, 2>, kernelConfigs = [#ttmetal.noc_config<@datamovement_kernel9, #ttmetal.core_range<0x0, 1x1>, #ttmetal.kernel_args< ct_args = [<cb_port[0]>, <cb_port[1]>, <cb_port[2]>]>, noc0>, #ttmetal.noc_config<@datamovement_kernel10, #ttmetal.core_range<0x0, 1x1>, #ttmetal.kernel_args< ct_args = [<cb_port[0]>, <cb_port[1]>, <cb_port[2]>]>, noc1>, #ttmetal.compute_config<@compute_kernel11, #ttmetal.core_range<0x0, 1x1>, #ttmetal.kernel_args< ct_args = [<cb_port[0]>, <cb_port[1]>, <cb_port[2]>]>, hifi4, false, false, [default]>], operandSegmentSizes = array<i32: 3, 3>}> : (memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>) -> ()
"ttmetal.deallocate_buffer"(%5) : (memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>) -> ()
"ttmetal.deallocate_buffer"(%4) : (memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>) -> ()
%8 = "ttmetal.create_buffer"() <{address = 9216 : i64}> : () -> memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>
%9 = "ttmetal.create_buffer"() <{address = 1024 : i64}> : () -> memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>
"ttmetal.enqueue_write_buffer"(%arg2, %9) : (memref<32x32xf32>, memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>) -> ()
"ttmetal.enqueue_program"(%9, %8, %9, %8) <{cb_ports = array<i64: 0, 1>, kernelConfigs = [#ttmetal.noc_config<@datamovement_kernel12, #ttmetal.core_range<0x0, 1x1>, #ttmetal.kernel_args< ct_args = [<cb_port[0]>, <cb_port[1]>]>, noc0>, #ttmetal.compute_config<@compute_kernel13, #ttmetal.core_range<0x0, 1x1>, #ttmetal.kernel_args< ct_args = [<cb_port[0]>, <cb_port[1]>]>, hifi4, false, false, [default]>], operandSegmentSizes = array<i32: 2, 2>}> : (memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>) -> ()
"ttmetal.deallocate_buffer"(%9) : (memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>) -> ()
%10 = "ttmetal.create_buffer"() <{address = 5120 : i64}> : () -> memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>
"ttmetal.enqueue_program"(%7, %8, %10, %7, %8, %10) <{cb_ports = array<i64: 0, 1, 2>, kernelConfigs = [#ttmetal.noc_config<@datamovement_kernel14, #ttmetal.core_range<0x0, 1x1>, #ttmetal.kernel_args< ct_args = [<cb_port[0]>, <cb_port[1]>, <cb_port[2]>]>, noc0>, #ttmetal.noc_config<@datamovement_kernel15, #ttmetal.core_range<0x0, 1x1>, #ttmetal.kernel_args< ct_args = [<cb_port[0]>, <cb_port[1]>, <cb_port[2]>]>, noc1>, #ttmetal.compute_config<@compute_kernel16, #ttmetal.core_range<0x0, 1x1>, #ttmetal.kernel_args< ct_args = [<cb_port[0]>, <cb_port[1]>, <cb_port[2]>]>, hifi4, false, false, [default]>], operandSegmentSizes = array<i32: 3, 3>}> : (memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>) -> ()
"ttmetal.deallocate_buffer"(%8) : (memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>) -> ()
"ttmetal.deallocate_buffer"(%7) : (memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>) -> ()
%alloc = memref.alloc() : memref<32x32xf32>
%11 = "ttmetal.create_buffer"() <{address = 1024 : i64}> : () -> memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>
"ttmetal.enqueue_program"(%10, %11, %10, %11) <{cb_ports = array<i64: 0, 1>, kernelConfigs = [#ttmetal.noc_config<@datamovement_kernel17, #ttmetal.core_range<0x0, 1x1>, #ttmetal.kernel_args< ct_args = [<cb_port[0]>, <cb_port[1]>]>, noc0>, #ttmetal.compute_config<@compute_kernel18, #ttmetal.core_range<0x0, 1x1>, #ttmetal.kernel_args< ct_args = [<cb_port[0]>, <cb_port[1]>]>, hifi4, false, false, [default]>], operandSegmentSizes = array<i32: 2, 2>}> : (memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>, memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>, memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>) -> ()
"ttmetal.deallocate_buffer"(%10) : (memref<1x1x1x1x!ttcore.tile<32x32, f32>, #ttcore.shard<4096x4096>, #l1>) -> ()
"ttmetal.enqueue_read_buffer"(%11, %alloc) : (memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>, memref<32x32xf32>) -> ()
"ttmetal.finish"() : () -> ()
"ttmetal.deallocate_buffer"(%11) : (memref<1x1x32x32xf32, #ttcore.shard<128x4>, #l1>) -> ()
return %alloc : memref<32x32xf32>
}
func.func private @datamovement_kernel0() attributes {ttkernel.arg_spec = #ttkernel.arg_spec< ct_args = [<arg_type = cb_port, operand_index = 0>, <arg_type = cb_port, operand_index = 1>]>, ttkernel.thread = #ttkernel.thread<noc>} {
%0 = "emitc.constant"() <{value = 1 : i32}> : () -> i32
%1 = emitc.literal "get_compile_time_arg_val(0)" : !emitc.opaque<"::tt::CB">
emitc.call_opaque "cb_reserve_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_push_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
return
}
func.func private @compute_kernel1() attributes {ttkernel.arg_spec = #ttkernel.arg_spec< ct_args = [<arg_type = cb_port, operand_index = 0>, <arg_type = cb_port, operand_index = 1>]>, ttkernel.thread = #ttkernel.thread<compute>} {
%0 = "emitc.constant"() <{value = 1 : i32}> : () -> i32
%1 = emitc.literal "get_compile_time_arg_val(0)" : !emitc.opaque<"::tt::CB">
%2 = emitc.literal "get_compile_time_arg_val(1)" : !emitc.opaque<"::tt::CB">
emitc.call_opaque "cb_reserve_back"(%2, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_wait_front"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "tilize_init"(%1, %0, %2) : (!emitc.opaque<"::tt::CB">, i32, !emitc.opaque<"::tt::CB">) -> ()
emitc.call_opaque "experimental::tilize_block"(%1, %2, %0, %0) : (!emitc.opaque<"::tt::CB">, !emitc.opaque<"::tt::CB">, i32, i32) -> ()
emitc.call_opaque "cb_push_back"(%2, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_wait_front"(%2, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_pop_front"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_pop_front"(%2, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
return
}
func.func private @datamovement_kernel2() attributes {ttkernel.arg_spec = #ttkernel.arg_spec< ct_args = [<arg_type = cb_port, operand_index = 0>, <arg_type = cb_port, operand_index = 1>]>, ttkernel.thread = #ttkernel.thread<noc>} {
%0 = "emitc.constant"() <{value = 1 : i32}> : () -> i32
%1 = emitc.literal "get_compile_time_arg_val(0)" : !emitc.opaque<"::tt::CB">
emitc.call_opaque "cb_reserve_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_push_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
return
}
func.func private @compute_kernel3() attributes {ttkernel.arg_spec = #ttkernel.arg_spec< ct_args = [<arg_type = cb_port, operand_index = 0>, <arg_type = cb_port, operand_index = 1>]>, ttkernel.thread = #ttkernel.thread<compute>} {
%0 = "emitc.constant"() <{value = 1 : i32}> : () -> i32
%1 = emitc.literal "get_compile_time_arg_val(0)" : !emitc.opaque<"::tt::CB">
%2 = emitc.literal "get_compile_time_arg_val(1)" : !emitc.opaque<"::tt::CB">
emitc.call_opaque "cb_reserve_back"(%2, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_wait_front"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "tilize_init"(%1, %0, %2) : (!emitc.opaque<"::tt::CB">, i32, !emitc.opaque<"::tt::CB">) -> ()
emitc.call_opaque "experimental::tilize_block"(%1, %2, %0, %0) : (!emitc.opaque<"::tt::CB">, !emitc.opaque<"::tt::CB">, i32, i32) -> ()
emitc.call_opaque "cb_push_back"(%2, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_wait_front"(%2, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_pop_front"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_pop_front"(%2, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
return
}
func.func private @datamovement_kernel4() attributes {ttkernel.arg_spec = #ttkernel.arg_spec< ct_args = [<arg_type = cb_port, operand_index = 0>, <arg_type = cb_port, operand_index = 1>, <arg_type = cb_port, operand_index = 2>]>, ttkernel.thread = #ttkernel.thread<noc>} {
%0 = "emitc.constant"() <{value = 1 : i32}> : () -> i32
%1 = emitc.literal "get_compile_time_arg_val(0)" : !emitc.opaque<"::tt::CB">
emitc.call_opaque "cb_reserve_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_push_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
return
}
func.func private @datamovement_kernel5() attributes {ttkernel.arg_spec = #ttkernel.arg_spec< ct_args = [<arg_type = cb_port, operand_index = 0>, <arg_type = cb_port, operand_index = 1>, <arg_type = cb_port, operand_index = 2>]>, ttkernel.thread = #ttkernel.thread<noc>} {
%0 = "emitc.constant"() <{value = 1 : i32}> : () -> i32
%1 = emitc.literal "get_compile_time_arg_val(1)" : !emitc.opaque<"::tt::CB">
emitc.call_opaque "cb_reserve_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_push_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
return
}
func.func private @compute_kernel6() attributes {ttkernel.arg_spec = #ttkernel.arg_spec< ct_args = [<arg_type = cb_port, operand_index = 0>, <arg_type = cb_port, operand_index = 1>, <arg_type = cb_port, operand_index = 2>]>, ttkernel.thread = #ttkernel.thread<compute>} {
%0 = "emitc.constant"() <{value = 0 : index}> : () -> !emitc.size_t
%1 = "emitc.constant"() <{value = 1 : i32}> : () -> i32
emitc.call_opaque "tile_regs_acquire"() : () -> ()
%2 = emitc.literal "get_compile_time_arg_val(0)" : !emitc.opaque<"::tt::CB">
%3 = emitc.literal "get_compile_time_arg_val(1)" : !emitc.opaque<"::tt::CB">
%4 = emitc.literal "get_compile_time_arg_val(2)" : !emitc.opaque<"::tt::CB">
emitc.call_opaque "cb_reserve_back"(%4, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_wait_front"(%2, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_wait_front"(%3, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "binary_op_init_common"(%2, %3, %4) : (!emitc.opaque<"::tt::CB">, !emitc.opaque<"::tt::CB">, !emitc.opaque<"::tt::CB">) -> ()
emitc.call_opaque "add_tiles_init"(%2, %3) : (!emitc.opaque<"::tt::CB">, !emitc.opaque<"::tt::CB">) -> ()
emitc.call_opaque "add_tiles"(%2, %3, %0, %0, %0) : (!emitc.opaque<"::tt::CB">, !emitc.opaque<"::tt::CB">, !emitc.size_t, !emitc.size_t, !emitc.size_t) -> ()
emitc.call_opaque "tile_regs_commit"() : () -> ()
emitc.call_opaque "tile_regs_wait"() : () -> ()
emitc.call_opaque "pack_tile"(%0, %4, %0) {template_args = [true]} : (!emitc.size_t, !emitc.opaque<"::tt::CB">, !emitc.size_t) -> ()
emitc.call_opaque "tile_regs_release"() : () -> ()
emitc.call_opaque "cb_push_back"(%4, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_wait_front"(%4, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_pop_front"(%2, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_pop_front"(%3, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_pop_front"(%4, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
return
}
func.func private @datamovement_kernel7() attributes {ttkernel.arg_spec = #ttkernel.arg_spec< ct_args = [<arg_type = cb_port, operand_index = 0>, <arg_type = cb_port, operand_index = 1>]>, ttkernel.thread = #ttkernel.thread<noc>} {
%0 = "emitc.constant"() <{value = 1 : i32}> : () -> i32
%1 = emitc.literal "get_compile_time_arg_val(0)" : !emitc.opaque<"::tt::CB">
emitc.call_opaque "cb_reserve_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_push_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
return
}
func.func private @compute_kernel8() attributes {ttkernel.arg_spec = #ttkernel.arg_spec< ct_args = [<arg_type = cb_port, operand_index = 0>, <arg_type = cb_port, operand_index = 1>]>, ttkernel.thread = #ttkernel.thread<compute>} {
%0 = "emitc.constant"() <{value = 1 : i32}> : () -> i32
%1 = emitc.literal "get_compile_time_arg_val(0)" : !emitc.opaque<"::tt::CB">
%2 = emitc.literal "get_compile_time_arg_val(1)" : !emitc.opaque<"::tt::CB">
emitc.call_opaque "cb_reserve_back"(%2, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_wait_front"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "tilize_init"(%1, %0, %2) : (!emitc.opaque<"::tt::CB">, i32, !emitc.opaque<"::tt::CB">) -> ()
emitc.call_opaque "experimental::tilize_block"(%1, %2, %0, %0) : (!emitc.opaque<"::tt::CB">, !emitc.opaque<"::tt::CB">, i32, i32) -> ()
emitc.call_opaque "cb_push_back"(%2, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_wait_front"(%2, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_pop_front"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_pop_front"(%2, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
return
}
func.func private @datamovement_kernel9() attributes {ttkernel.arg_spec = #ttkernel.arg_spec< ct_args = [<arg_type = cb_port, operand_index = 0>, <arg_type = cb_port, operand_index = 1>, <arg_type = cb_port, operand_index = 2>]>, ttkernel.thread = #ttkernel.thread<noc>} {
%0 = "emitc.constant"() <{value = 1 : i32}> : () -> i32
%1 = emitc.literal "get_compile_time_arg_val(0)" : !emitc.opaque<"::tt::CB">
emitc.call_opaque "cb_reserve_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_push_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
return
}
func.func private @datamovement_kernel10() attributes {ttkernel.arg_spec = #ttkernel.arg_spec< ct_args = [<arg_type = cb_port, operand_index = 0>, <arg_type = cb_port, operand_index = 1>, <arg_type = cb_port, operand_index = 2>]>, ttkernel.thread = #ttkernel.thread<noc>} {
%0 = "emitc.constant"() <{value = 1 : i32}> : () -> i32
%1 = emitc.literal "get_compile_time_arg_val(1)" : !emitc.opaque<"::tt::CB">
emitc.call_opaque "cb_reserve_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_push_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
return
}
func.func private @compute_kernel11() attributes {ttkernel.arg_spec = #ttkernel.arg_spec< ct_args = [<arg_type = cb_port, operand_index = 0>, <arg_type = cb_port, operand_index = 1>, <arg_type = cb_port, operand_index = 2>]>, ttkernel.thread = #ttkernel.thread<compute>} {
%0 = "emitc.constant"() <{value = 0 : index}> : () -> !emitc.size_t
%1 = "emitc.constant"() <{value = 1 : i32}> : () -> i32
emitc.call_opaque "tile_regs_acquire"() : () -> ()
%2 = emitc.literal "get_compile_time_arg_val(0)" : !emitc.opaque<"::tt::CB">
%3 = emitc.literal "get_compile_time_arg_val(1)" : !emitc.opaque<"::tt::CB">
%4 = emitc.literal "get_compile_time_arg_val(2)" : !emitc.opaque<"::tt::CB">
emitc.call_opaque "cb_reserve_back"(%4, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_wait_front"(%2, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_wait_front"(%3, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "binary_op_init_common"(%2, %3, %4) : (!emitc.opaque<"::tt::CB">, !emitc.opaque<"::tt::CB">, !emitc.opaque<"::tt::CB">) -> ()
emitc.call_opaque "mul_tiles_init"(%2, %3) : (!emitc.opaque<"::tt::CB">, !emitc.opaque<"::tt::CB">) -> ()
emitc.call_opaque "mul_tiles"(%2, %3, %0, %0, %0) : (!emitc.opaque<"::tt::CB">, !emitc.opaque<"::tt::CB">, !emitc.size_t, !emitc.size_t, !emitc.size_t) -> ()
emitc.call_opaque "tile_regs_commit"() : () -> ()
emitc.call_opaque "tile_regs_wait"() : () -> ()
emitc.call_opaque "pack_tile"(%0, %4, %0) {template_args = [true]} : (!emitc.size_t, !emitc.opaque<"::tt::CB">, !emitc.size_t) -> ()
emitc.call_opaque "tile_regs_release"() : () -> ()
emitc.call_opaque "cb_push_back"(%4, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_wait_front"(%4, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_pop_front"(%2, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_pop_front"(%3, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_pop_front"(%4, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
return
}
func.func private @datamovement_kernel12() attributes {ttkernel.arg_spec = #ttkernel.arg_spec< ct_args = [<arg_type = cb_port, operand_index = 0>, <arg_type = cb_port, operand_index = 1>]>, ttkernel.thread = #ttkernel.thread<noc>} {
%0 = "emitc.constant"() <{value = 1 : i32}> : () -> i32
%1 = emitc.literal "get_compile_time_arg_val(0)" : !emitc.opaque<"::tt::CB">
emitc.call_opaque "cb_reserve_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_push_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
return
}
func.func private @compute_kernel13() attributes {ttkernel.arg_spec = #ttkernel.arg_spec< ct_args = [<arg_type = cb_port, operand_index = 0>, <arg_type = cb_port, operand_index = 1>]>, ttkernel.thread = #ttkernel.thread<compute>} {
%0 = "emitc.constant"() <{value = 1 : i32}> : () -> i32
%1 = emitc.literal "get_compile_time_arg_val(0)" : !emitc.opaque<"::tt::CB">
%2 = emitc.literal "get_compile_time_arg_val(1)" : !emitc.opaque<"::tt::CB">
emitc.call_opaque "cb_reserve_back"(%2, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_wait_front"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "tilize_init"(%1, %0, %2) : (!emitc.opaque<"::tt::CB">, i32, !emitc.opaque<"::tt::CB">) -> ()
emitc.call_opaque "experimental::tilize_block"(%1, %2, %0, %0) : (!emitc.opaque<"::tt::CB">, !emitc.opaque<"::tt::CB">, i32, i32) -> ()
emitc.call_opaque "cb_push_back"(%2, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_wait_front"(%2, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_pop_front"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_pop_front"(%2, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
return
}
func.func private @datamovement_kernel14() attributes {ttkernel.arg_spec = #ttkernel.arg_spec< ct_args = [<arg_type = cb_port, operand_index = 0>, <arg_type = cb_port, operand_index = 1>, <arg_type = cb_port, operand_index = 2>]>, ttkernel.thread = #ttkernel.thread<noc>} {
%0 = "emitc.constant"() <{value = 1 : i32}> : () -> i32
%1 = emitc.literal "get_compile_time_arg_val(0)" : !emitc.opaque<"::tt::CB">
emitc.call_opaque "cb_reserve_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_push_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
return
}
func.func private @datamovement_kernel15() attributes {ttkernel.arg_spec = #ttkernel.arg_spec< ct_args = [<arg_type = cb_port, operand_index = 0>, <arg_type = cb_port, operand_index = 1>, <arg_type = cb_port, operand_index = 2>]>, ttkernel.thread = #ttkernel.thread<noc>} {
%0 = "emitc.constant"() <{value = 1 : i32}> : () -> i32
%1 = emitc.literal "get_compile_time_arg_val(1)" : !emitc.opaque<"::tt::CB">
emitc.call_opaque "cb_reserve_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_push_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
return
}
func.func private @compute_kernel16() attributes {ttkernel.arg_spec = #ttkernel.arg_spec< ct_args = [<arg_type = cb_port, operand_index = 0>, <arg_type = cb_port, operand_index = 1>, <arg_type = cb_port, operand_index = 2>]>, ttkernel.thread = #ttkernel.thread<compute>} {
%0 = "emitc.constant"() <{value = 0 : index}> : () -> !emitc.size_t
%1 = "emitc.constant"() <{value = 1 : i32}> : () -> i32
emitc.call_opaque "tile_regs_acquire"() : () -> ()
%2 = emitc.literal "get_compile_time_arg_val(0)" : !emitc.opaque<"::tt::CB">
%3 = emitc.literal "get_compile_time_arg_val(1)" : !emitc.opaque<"::tt::CB">
%4 = emitc.literal "get_compile_time_arg_val(2)" : !emitc.opaque<"::tt::CB">
emitc.call_opaque "cb_reserve_back"(%4, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_wait_front"(%2, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_wait_front"(%3, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "binary_op_init_common"(%2, %3, %4) : (!emitc.opaque<"::tt::CB">, !emitc.opaque<"::tt::CB">, !emitc.opaque<"::tt::CB">) -> ()
emitc.call_opaque "mul_tiles_init"(%2, %3) : (!emitc.opaque<"::tt::CB">, !emitc.opaque<"::tt::CB">) -> ()
emitc.call_opaque "mul_tiles"(%2, %3, %0, %0, %0) : (!emitc.opaque<"::tt::CB">, !emitc.opaque<"::tt::CB">, !emitc.size_t, !emitc.size_t, !emitc.size_t) -> ()
emitc.call_opaque "tile_regs_commit"() : () -> ()
emitc.call_opaque "tile_regs_wait"() : () -> ()
emitc.call_opaque "pack_tile"(%0, %4, %0) {template_args = [true]} : (!emitc.size_t, !emitc.opaque<"::tt::CB">, !emitc.size_t) -> ()
emitc.call_opaque "tile_regs_release"() : () -> ()
emitc.call_opaque "cb_push_back"(%4, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_wait_front"(%4, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_pop_front"(%2, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_pop_front"(%3, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_pop_front"(%4, %1) : (!emitc.opaque<"::tt::CB">, i32) -> ()
return
}
func.func private @datamovement_kernel17() attributes {ttkernel.arg_spec = #ttkernel.arg_spec< ct_args = [<arg_type = cb_port, operand_index = 0>, <arg_type = cb_port, operand_index = 1>]>, ttkernel.thread = #ttkernel.thread<noc>} {
%0 = "emitc.constant"() <{value = 1 : i32}> : () -> i32
%1 = emitc.literal "get_compile_time_arg_val(0)" : !emitc.opaque<"::tt::CB">
emitc.call_opaque "cb_reserve_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_push_back"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
return
}
func.func private @compute_kernel18() attributes {ttkernel.arg_spec = #ttkernel.arg_spec< ct_args = [<arg_type = cb_port, operand_index = 0>, <arg_type = cb_port, operand_index = 1>]>, ttkernel.thread = #ttkernel.thread<compute>} {
%0 = "emitc.constant"() <{value = 1 : i32}> : () -> i32
%1 = emitc.literal "get_compile_time_arg_val(0)" : !emitc.opaque<"::tt::CB">
%2 = emitc.literal "get_compile_time_arg_val(1)" : !emitc.opaque<"::tt::CB">
emitc.call_opaque "cb_reserve_back"(%2, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_wait_front"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "untilize_init"(%1, %2) : (!emitc.opaque<"::tt::CB">, !emitc.opaque<"::tt::CB">) -> ()
emitc.call_opaque "experimental::untilize_block"(%1, %2, %0, %0) : (!emitc.opaque<"::tt::CB">, !emitc.opaque<"::tt::CB">, i32, i32) -> ()
emitc.call_opaque "cb_push_back"(%2, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_wait_front"(%2, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_pop_front"(%1, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
emitc.call_opaque "cb_pop_front"(%2, %0) : (!emitc.opaque<"::tt::CB">, i32) -> ()
return
}
}
}
}
Compiling into flatbuffer
compile_to_flatbuffer compiles a TTIRBuilder function fn straight to flatbuffer. This decorator is mainly a wrapper around the following functions, with each next function called on the output of the last: build_mlir_module, run_pipeline, and ttnn_to_flatbuffer_file or ttmetal_to_flatbuffer_file as dictated by the target parameter.
def compile_to_flatbuffer(
fn: Callable,
inputs_shapes: List[Shape],
inputs_types: Optional[List[Union[torch.dtype, TypeInfo]]] = None,
system_desc_path: str = "ttrt-artifacts/system_desc.ttsys",
test_base: str = "test",
output_root: str = ".",
target: Literal["ttnn", "ttmetal"] = "ttnn",
mesh_shape: Optional[Tuple[int, int]] = None,
module_dump: bool = True,
argument_types_string: Optional[str] = None,
custom_pipeline: Union[Callable, str] = None,
pipeline_options: List[str] = None,
)
No flatbuffer is printed or returned. It's only written to a file because it is created as an unsupported text encoding.
TTNN example
Let's use our previous model function.
from ttir_builder.utils import compile_to_flatbuffer
from ttir_builder import Operand, TTIRBuilder
shapes = [(32, 32), (32, 32), (32, 32)]
def model(in0: Operand, in1: Operand, in2: Operand, builder: TTIRBuilder):
add_0 = builder.add(in0, in1)
multiply_1 = builder.multiply(in1, add_0)
return builder.multiply(multiply_1, in2)
compile_to_flatbuffer(
model,
shapes,
target="ttnn",
)
TTMetal example
Let's once again use the same code for TTMetal that was used in the TTNN example but change the target to "ttmetal". Just as with run_pipeline, only one or the other can be run on a module since compile_to_flatbuffer modifies the module in place.
compile_to_flatbuffer(
model,
shapes,
target="ttmetal",
)
Integrating with other tt-mlir tools
Alternatives for file creation
- The
ttmlir-opttool runs a compiler pass on an.mlirfile. - The
ttmlir-translatecan generate a flatbuffer from an.mlirfile. llvm-litcan also be used to generate a flatbuffer from an existing.mlirfile.
Running models
ttrt
ttrt is intended to be a swiss army knife for working with flatbuffers.
tt-explorer
tt-explorer is a visualizer tool for ttmlir-powered compiler results.
ttnn-standalone
ttnn-standalone is a post-compile tuning/debugging tool.
llvm-lit
llvm-lit can also be used for MLIR testing.
Golden mode
Golden dataclass
TTIRBuilder provides support to code golden tensors into flatbuffers which will be used for comparison with TT device output in ttrt runtime. Golden is the dataclass used to store information about a golden tensor. Each TTIR op should have a matching PyTorch op (or golden function built from PyTorch ops) which should perform exactly the same operation, generating the same outputs given the same inputs. You can use TTIRBuilder helper functions to store input, intermediate, and output tensors within the flatbuffer. Input and output goldens are mapped with keys "input_" and "output_" followed by a tensor index: input_0. Intermediate output tensors are mapped to the location of the respecive op creation.
GoldenCheckLevel Enum
TTIRBuilder stores an instance of the class GoldenCheckLevel(Enum) that dictates golden handling. It defaults to GoldenCheckLevel.OP_LEVEL. The exception is that TTIRBuilder CCL ops force the golden level to be set to GRAPH_LEVEL.
DISABLED : do not store goldens
OP_LEVEL : check every single op level goldens
GRAPH_LEVEL : check graph level goldens only
Check and set GoldenCheckLevel with TTIRBuilder APIs.
from ttir_builder import TTIRBuilder, Operand, GoldenCheckLevel
def model(in0: Operand, in1: Operand, in2: Operand, builder: TTIRBuilder):
builder.golden_check_level = GoldenCheckLevel.GRAPH_LEVEL
add_0 = builder.add(in0, in1)
multiply_1 = builder.multiply(in1, add_0)
return builder.multiply(multiply_1, in2)
Getting golden data
Unless otherwise specified in the GoldenCheckLevel, all input and output tensors will generate and store a golden in TTIRBuilder as a Golden type. The TTIRBuilder class has an API to print stored goldens if you want access to the data they contain: print_goldens(self).
Golden tensor:
tensor([[ 4.0450e+00, 1.4274e+00, 5.9156e-01, ..., -5.9834e-01,
-1.1830e-01, 1.2837e-01],
[ 2.3788e+00, 2.9242e-03, -5.2838e-02, ..., 1.8294e+00,
5.0348e+00, 9.7179e-01],
[ 1.5168e-02, 1.0577e-01, -3.0682e-01, ..., 6.7212e-01,
9.4523e-02, 5.3765e+00],
...,
[ 1.4241e-01, 1.1838e+00, -1.0601e+00, ..., 4.9099e-01,
4.2267e+00, 4.0610e-01],
[ 5.6630e-01, -1.3068e-01, -1.7771e-01, ..., 2.3862e+00,
3.9376e-01, 7.3140e-01],
[ 4.2420e+00, 1.7006e-01, -3.4861e-01, ..., 1.1471e-01,
1.6189e+00, -6.9106e-01]])
The TTIRBuilder API get_golden_map(self) is used to export golden data for flatbuffer construction. It returns a dictionary of golden tensor names and GoldenTensor objects. Printing that map will look something like this:
{'input_0': <ttmlir._mlir_libs._ttmlir.passes.GoldenTensor object at 0x7f77c70fa0d0>, 'input_1': <ttmlir._mlir_libs._ttmlir.passes.GoldenTensor object at 0x7f77c70fa160>, 'input_2': <ttmlir._mlir_libs._ttmlir.passes.GoldenTensor object at 0x7f77c6fc9500>, 'output_0': <ttmlir._mlir_libs._ttmlir.passes.GoldenTensor object at 0x7f77c6fc9590>}
To get info from a GoldenTensor object, use the attributes supported by ttmlir.passes: name, shape, strides, dtype, data.
from ttmlir.passes import GoldenTensor
Setting golden data
Use TTIRBuilder API set_graph_input_output to set your own input and output golden tensors using PyTorch tensors. Keep in mind that this also sets graph inputs and outputs. There are some functions for which setting custom input tensors is required to pass PCC accuracy checks: ttir.tan, ttir.log, ttir.log1p. See example implementation and explanation in test/python/golden/test_ttir_ops.py.
set_graph_input_output(
self,
inputs: List[torch.Tensor],
outputs: Optional[List[torch.Tensor]] = None,
override: bool = False,
)
import torch
input_0 = torch.ones((32, 32))
output_0 = torch.zeros((32, 32))
builder.set_graph_input_output([input_0], [output_0], override=True)
Running flatbuffer with golden data in ttrt
Running flatbuffers in ttrt requires additional building and setting up the environment. Run these commands before creating MLIR modules or flatbuffers so the system description in the flatbuffers match your device.
cmake --build build -- ttrt
ttrt query --save-artifacts
export SYSTEM_DESC_PATH=/path/to/system_desc.ttsys
Set environment variable TTRT_LOGGER_LEVEL to DEBUG so ttrt logs golden comparison results and prints graph level golden tensors.
export TTRT_LOGGER_LEVEL=DEBUG
Finally run ttrt. Our example flatbuffer file (since we didn't specify otherwise) defaulted to file path ./ttnn/test_ttnn.mlir.ttnn. --log-file ttrt.log and --save-golden-tensors are both optional flags. They ensure that all golden data produced by the ttrt run gets written to files.
ttrt run ttnn/test_ttnn.mlir.ttnn --log-file ttrt.log --save-golden-tensors
Golden callbacks
The ttrt documentation contains a section on the callback function feature. Callback functions run between each op execution during runtime and contain op level golden analysis. They are also customizable and provide the flexibility for you to get creative with your golden usage.
Adding a new op to ttir-builder
ttir-builder is designed to only create ops supported in TTIR. At the moment, most but not all ops are supported, and new ops are still occasionally added to TTIR. Creating ttir-builder support for an op entails writing a function in tools/ttir-builder/builder.py that will create the op and its golden counterpart.
TTIR op factories
All ops are created when their relevant information is run through the op_proxy function which provides a general interface for proxy-ing and creating ops.
def op_proxy(
self,
op_golden_function: Callable,
op_ttir_function: Callable,
inputs: List[Operand],
unit_attrs: List[str] = None,
organize_ttir_args: Optional[Callable] = None,
organize_golden_args: Optional[Callable] = None,
output_shape: Optional[Shape] = None,
output_type: Optional[Type] = None,
output_create_fn: Optional[Callable] = None,
golden_kwargs: dict = {},
ttir_kwargs: dict = {},
)
Start by finding the TTIR op you wish to replicate in include/ttmlir/Dialect/TTIR/IR/TTIROps.td or the TTIR dialect documentation.
All op attributes should be included as arguments in your function and passed into a proxy function as keyword arguments using ttir_kwargs.
All input operands should be passed into a proxy function using the argument inputs. Output operands are considered inputs and can optionally be passed into inputs if their shape or datatype is relevant to the op's result operand. organize_ttir_args dictates what information gets passed into autogenerated file build/python_packages/ttmlir/dialects/_ttir_ops_gen.py and can be used if operand arguments require special handling.
Before writing a golden function, you need to know exactly what the TTIR op does to its input data because you will have to replicate that exactly using Pytorch operations. This information is usually covered in TTIR documentation, but if not, you may have to take it upon yourself to do some detective work and trial and error.
Writing a golden function is very straightforward if Pytorch has a function that performs exactly the same operation. If so, pass that function into a proxy function as op_golden_function, any keywords into golden_kwargs, and use organize_golden_args if input operands differ from those of the TTIR op.
If Pytorch doesn't have an identical operation, your job is about to get a little harder. Get creative with keyword argument handling, using similar Pytorch operations, and maybe multiple operations. Google is your friend. If you have to figure out how to do something Pytorch doesn't, odds are someone online has encountered the same situation.
Example implementation:
def softmax(
self, in0: Operand, dimension: int = 1, unit_attrs: Optional[List[str]] = None
) -> OpView:
return self.op_proxy(
torch.nn.functional.softmax,
ttir.SoftmaxOp,
[in0],
golden_kwargs={"dim": dimension},
organize_ttir_args=lambda i, o, _: (
self._get_type(o),
i[0],
o,
dimension,
),
unit_attrs=unit_attrs,
)
Eltwise operations
Eltwise ops require less specialized handling and call op_proxy through eltwise_proxy.
def eltwise_proxy(
self,
op_golden_function: Callable,
op_ttir_function: Callable,
inputs: List[Operand],
unit_attrs: List[str] = None,
)
CCL ops require GoldenCheckLevel to be set to GRAPH_LEVEL and integrate that into their own proxy function.
def ccl_proxy(
self,
op_golden_function: Callable,
op_ttir_function: Callable,
inputs: List[Operand],
kwargs: dict = {},
)
Golden functions
Setting the various inputs, outputs, arguments, shapes, and types are all fairly straightforward. Find the TTIR op in include/ttmlir/Dialect/TTIR/IR/TTIROps.td and replicate the pertinents. If there is necessary information that is not included, you may have to take it upon yourself to do some detective work and trial and error. The tricky part can be the finding or writing a golden function. It must perform exactly the same operation as the TTIR op and be written using PyTorch operations.