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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,57 @@ | ||
| #!/bin/bash | ||
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| set -eo pipefail | ||
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| if [[ -z "$TT_METAL_HOME" ]]; then | ||
| echo "Must provide TT_METAL_HOME in environment" 1>&2 | ||
| exit 1 | ||
| fi | ||
|
|
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| if [[ -z "$TT_METAL_SLOW_DISPATCH_MODE" ]]; then | ||
| echo "Must provide TT_METAL_SLOW_DISPATCH_MODE in environment" 1>&2 | ||
| exit 1 | ||
| fi | ||
|
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| export TT_METAL_CLEAR_L1=1 | ||
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| echo "Running tunneler tests now..."; | ||
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| run_test() { | ||
| echo $1 | ||
| $1 | ||
| echo | ||
| }; | ||
|
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| run_test_with_watcher() { | ||
| echo $1 | ||
| TT_METAL_WATCHER=1 TT_METAL_WATCHER_NOINLINE=1 $1 | ||
| echo | ||
| }; | ||
|
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| main() { | ||
| # Parse the arguments | ||
| while [[ $# -gt 0 ]]; do | ||
| case $1 in | ||
| --machine-type) | ||
| machine_type=$2 | ||
| shift | ||
| ;; | ||
| *) | ||
| echo "Unknown option: $1" | ||
| exit 1 | ||
| ;; | ||
| esac | ||
| shift | ||
| done | ||
|
|
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| if [[ $ARCH_NAME == "wormhole_b0" && $machine_type != "N150" ]]; then | ||
| for max_packet_size_words in 256 512 1024 2048; do | ||
| run_test "./build/test/tt_metal/perf_microbenchmark/routing/test_vc_uni_tunnel --tx_x 4 --tx_y 7 --mux_x 0 --mux_y 7 --demux_x 0 --demux_y 0 --rx_x 0 --rx_y 1 --max_packet_size_words $max_packet_size_words --tx_skip_pkt_content_gen 1 --rx_disable_data_check 1 --rx_disable_header_check 1 --tx_pkt_dest_size_choice 1 --check_txrx_timeout 1 --data_kb_per_tx 1048576 --tunneler_queue_size_bytes 32768 --tx_queue_size_bytes 65536 --rx_queue_size_bytes 131072 --mux_queue_size_bytes 65536 --demux_queue_size_bytes 65536" | ||
| run_test "./build/test/tt_metal/perf_microbenchmark/routing/test_vc_bi_tunnel_2ep --tx_x 4 --tx_y 7 --mux_x 0 --mux_y 7 --demux_x 0 --demux_y 0 --rx_x 0 --rx_y 1 --max_packet_size_words $max_packet_size_words --tx_skip_pkt_content_gen 1 --rx_disable_data_check 1 --rx_disable_header_check 1 --tx_pkt_dest_size_choice 1 --check_txrx_timeout 1 --data_kb_per_tx 1048576 --tunneler_queue_size_bytes 32768 --tx_queue_size_bytes 65536 --rx_queue_size_bytes 131072 --mux_queue_size_bytes 65536 --demux_queue_size_bytes 65536" | ||
| run_test "./build/test/tt_metal/perf_microbenchmark/routing/test_vc_bi_tunnel_4ep --tx_x 4 --tx_y 7 --mux_x 0 --mux_y 7 --demux_x 0 --demux_y 0 --rx_x 0 --rx_y 1 --max_packet_size_words $max_packet_size_words --tx_skip_pkt_content_gen 1 --rx_disable_data_check 1 --rx_disable_header_check 1 --tx_pkt_dest_size_choice 1 --check_txrx_timeout 1 --data_kb_per_tx 1048576 --tunneler_queue_size_bytes 16384 --tx_queue_size_bytes 65536 --rx_queue_size_bytes 131072 --mux_queue_size_bytes 65536 --demux_queue_size_bytes 65536" | ||
| done | ||
| fi | ||
|
|
||
| } | ||
|
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| main "$@" |
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109 changes: 109 additions & 0 deletions
109
tests/sweep_framework/sweeps/eltwise/unary/frac/frac_sharded.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,109 @@ | ||
| # SPDX-FileCopyrightText: © 2024 Tenstorrent Inc. | ||
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| # SPDX-License-Identifier: Apache-2.0 | ||
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| from typing import Optional, Tuple | ||
| from functools import partial | ||
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| import json | ||
| import torch | ||
| import random | ||
| import ttnn | ||
| import math | ||
| from tests.sweep_framework.sweep_utils.utils import gen_shapes, sanitize_shape_rm | ||
| from tests.sweep_framework.sweep_utils.sharding_utils import gen_sharded_spec_unary, parse_sharding_spec | ||
| from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt | ||
|
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| from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time | ||
| from models.utility_functions import torch_random | ||
|
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| # Override the default timeout in seconds for hang detection. | ||
| TIMEOUT = 120 | ||
|
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| random.seed(0) | ||
|
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|
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| # Parameters provided to the test vector generator are defined here. | ||
| # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values. | ||
| # Each suite has a key name (in this case "suite_1" and "suite_2") which will associate the test vectors to this specific suite of inputs. | ||
| # Developers can create their own generator functions and pass them to the parameters as inputs. | ||
| parameters = { | ||
| "nightly": { | ||
| "input_spec": gen_sharded_spec_unary(12, layouts=["TILE_LAYOUT"]), | ||
| "input_a_dtype": [ttnn.bfloat16, ttnn.bfloat8_b], | ||
| }, | ||
| } | ||
|
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| # Invalidate vector is called during the generation phase where each vector will be passed in. | ||
| # If invalidated, the vector will still be stored but will be skipped. | ||
| # Returns False, None if the vector is valid, and True, str with a reason for invalidation if it is invalid. | ||
| def invalidate_vector(test_vector) -> Tuple[bool, Optional[str]]: | ||
| input_shape, X, Y, sharding_strategy, _, _, input_layout = test_vector["input_spec"].values() | ||
| pre_sharded_height = math.prod(input_shape[:-1]) | ||
| pre_sharded_width = input_shape[-1] | ||
|
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| if input_layout == "ROW_MAJOR_LAYOUT": | ||
| return True, "Input to eltwise binary must be tilized" | ||
|
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| if input_layout == "ROW_MAJOR_LAYOUT" and test_vector["input_a_dtype"] == ttnn.bfloat8_b: | ||
| return True, "bfloat8_b is only supported on tiled layout" | ||
|
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| return False, None | ||
|
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|
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| # This is the run instructions for the test, defined by the developer. | ||
| # The run function must take the above-defined parameters as inputs. | ||
| # The runner will call this run function with each test vector, and the returned results from this function will be stored. | ||
| # If you defined a mesh_device_fixture above, the object you yielded will be passed into this function as 'device'. Otherwise, it will be the default ttnn device opened by the infra. | ||
| def run( | ||
| input_spec, | ||
| input_a_dtype, | ||
| *, | ||
| device, | ||
| ) -> list: | ||
| data_seed = random.randint(0, 20000000) | ||
| torch.manual_seed(data_seed) | ||
|
|
||
| ( | ||
| input_shape, | ||
| core_grid, | ||
| sharding_strategy, | ||
| shard_orientation, | ||
| tensor_hw_as_shard_shape, | ||
| input_layout, | ||
| ) = parse_sharding_spec(input_spec) | ||
|
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| if input_layout == ttnn.ROW_MAJOR_LAYOUT: | ||
| input_shape = sanitize_shape_rm(input_shape) | ||
|
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| torch_input_tensor_a = gen_func_with_cast_tt( | ||
| partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype | ||
| )(input_shape) | ||
|
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| torch_op = ttnn.get_golden_function(ttnn.frac) | ||
| torch_output_tensor = torch_op(torch_input_tensor_a) | ||
|
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| sharded_config = ttnn.create_sharded_memory_config_( | ||
| shape=input_shape, | ||
| core_grid=core_grid, | ||
| strategy=sharding_strategy, | ||
| orientation=shard_orientation, | ||
| use_height_and_width_as_shard_shape=tensor_hw_as_shard_shape, | ||
| ) | ||
|
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| input_tensor_a = ttnn.from_torch( | ||
| torch_input_tensor_a, | ||
| dtype=input_a_dtype, | ||
| layout=input_layout, | ||
| device=device, | ||
| memory_config=sharded_config, | ||
| ) | ||
|
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| start_time = start_measuring_time() | ||
| output_tensor = ttnn.frac(input_tensor_a, memory_config=sharded_config) | ||
| e2e_perf = stop_measuring_time(start_time) | ||
| output_tensor = ttnn.to_torch(output_tensor) | ||
|
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| pcc = check_with_pcc(torch_output_tensor, output_tensor, 0.999) | ||
| return [pcc, e2e_perf] |
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