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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,131 @@ | ||
| # SPDX-FileCopyrightText: © 2024 Tenstorrent Inc. | ||
|
|
||
| # SPDX-License-Identifier: Apache-2.0 | ||
|
|
||
| from typing import Optional, Tuple, List | ||
| import itertools | ||
| import random | ||
| import torch | ||
| import math | ||
|
|
||
| import ttnn | ||
|
|
||
| from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time | ||
| from models.utility_functions import torch_random | ||
|
|
||
|
|
||
| def invalidate_vector(test_vector) -> Tuple[bool, Optional[str]]: | ||
| [pad_h, pad_w] = test_vector["padding"] | ||
| [_, _, kernel_h, kernel_w] = test_vector["shape"] | ||
| if 2 * pad_h > kernel_h or 2 * pad_w > kernel_w: | ||
| return True, "double of padding can not be greater than kernel size." | ||
| return False, None | ||
|
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||
|
|
||
| def mesh_device_fixture(): | ||
| num_devices = ttnn.GetNumPCIeDevices() | ||
| # As of now take device id as 0. | ||
| device_id = 0 | ||
| assert device_id < num_devices, "CreateDevice not supported for non-mmio device" | ||
| device = ttnn.CreateDevice(device_id=device_id, l1_small_size=32768) | ||
| ttnn.SetDefaultDevice(device) | ||
|
|
||
| device_name = "Unknown" | ||
| if ttnn.device.is_grayskull(device): | ||
| device_name = "grayskull" | ||
| elif ttnn.device.is_wormhole_b0(device): | ||
| device_name = "wormhole_b0" | ||
| yield device, device_name | ||
|
|
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| ttnn.close_device(device) | ||
|
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||
|
|
||
| def run_max_pool2d( | ||
| in_n, | ||
| in_c, | ||
| in_h, | ||
| in_w, | ||
| kernel_h, | ||
| kernel_w, | ||
| stride_h, | ||
| stride_w, | ||
| pad_h, | ||
| pad_w, | ||
| dilation_h, | ||
| dilation_w, | ||
| dtype, | ||
| device, | ||
| sharding=ttnn.TensorMemoryLayout.HEIGHT_SHARDED, | ||
| ceil_mode=False, | ||
| ): | ||
| act_shape = [in_n, in_c, in_h, in_w] | ||
| kernel_size = [kernel_h, kernel_w] | ||
| stride = [stride_h, stride_h] | ||
| padding = [pad_h, pad_w] | ||
| dilation = [dilation_h, dilation_w] | ||
|
|
||
| out_h = math.floor((in_h + 2 * pad_h - (dilation_h * kernel_h - 1) - 1) / stride_h) + 1 | ||
| out_w = math.floor((in_w + 2 * pad_w - (dilation_w * kernel_w - 1) - 1) / stride_w) + 1 | ||
|
|
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| torch.manual_seed(0) | ||
| torch.set_printoptions(precision=3, sci_mode=False, linewidth=500, threshold=10000, edgeitems=32) | ||
|
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| act = torch.randn(act_shape, dtype=torch.bfloat16) | ||
| act_shape = (1, 1, in_n * in_h * in_w, in_c) | ||
| act_permuted = torch.permute(act, (0, 2, 3, 1)) | ||
| act_reshaped = act_permuted.reshape(act_shape) | ||
|
|
||
| if dtype == ttnn.bfloat8_b: | ||
| ttact = ttnn.from_torch(act_reshaped, dtype, layout=ttnn.TILE_LAYOUT) | ||
| else: | ||
| ttact = ttnn.from_torch(act_reshaped, dtype) | ||
|
|
||
| ttact_device = ttnn.to_device(ttact, device) | ||
| start_time = start_measuring_time() | ||
| output = ttnn.max_pool2d( | ||
| input_tensor=ttact_device, | ||
| batch_size=in_n, | ||
| input_h=in_h, | ||
| input_w=in_w, | ||
| channels=in_c, | ||
| kernel_size=[kernel_h, kernel_w], | ||
| stride=[stride_h, stride_w], | ||
| padding=[pad_h, pad_w], | ||
| dilation=[dilation_h, dilation_w], | ||
| memory_config=None, | ||
| applied_shard_scheme=sharding, | ||
| ) | ||
|
|
||
| output_host = output.cpu() | ||
| output_pytorch_padded = torch.Tensor(ttnn.to_torch(output_host)) | ||
| output_pytorch = output_pytorch_padded[:, :, :, :in_c] | ||
| e2e_perf = stop_measuring_time(start_time) | ||
|
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||
| ## reference | ||
| golden_pytorch = torch.nn.MaxPool2d( | ||
| kernel_size, | ||
| stride=stride, | ||
| padding=padding, | ||
| dilation=dilation, | ||
| return_indices=False, | ||
| ceil_mode=False, | ||
| )(act) | ||
|
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| golden_shape = golden_pytorch.shape | ||
| output_pytorch = output_pytorch.reshape(golden_shape[0], golden_shape[2], golden_shape[3], golden_shape[1]) | ||
| output_pytorch = torch.permute(output_pytorch, (0, 3, 1, 2)) ## N, C, H, W | ||
|
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| atol, rtol = torch.testing._comparison.default_tolerances(torch.bfloat16) | ||
| if dtype == ttnn.bfloat8_b: | ||
| atol = 0.35 | ||
|
|
||
| ## test for equivalance | ||
| allclose = torch.allclose(output_pytorch, golden_pytorch, atol=atol) | ||
| isequal = torch.equal(output_pytorch, golden_pytorch) | ||
|
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| assert allclose, " Reference and output tensor are not close" | ||
| if dtype == ttnn.bfloat16: | ||
| assert isequal, " Reference and output tensor are not equal" | ||
|
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| # check pcc and return | ||
| return [check_with_pcc(output_pytorch, golden_pytorch, pcc=0.998), e2e_perf] |
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137 changes: 137 additions & 0 deletions
137
tests/sweep_framework/sweeps/eltwise/unary_complex/conj_bw.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,137 @@ | ||
| # SPDX-FileCopyrightText: © 2024 Tenstorrent Inc. | ||
|
|
||
| # SPDX-License-Identifier: Apache-2.0 | ||
|
|
||
| from typing import Optional, Tuple | ||
| from functools import partial | ||
|
|
||
| import torch | ||
| import random | ||
| import ttnn | ||
| from tests.sweep_framework.sweep_utils.utils import gen_shapes | ||
| from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt | ||
|
|
||
| from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time | ||
| from models.utility_functions import torch_random | ||
|
|
||
| # Override the default timeout in seconds for hang detection. | ||
| TIMEOUT = 30 | ||
|
|
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| random.seed(0) | ||
|
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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") 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_shape": gen_shapes([1, 1, 1, 1], [6, 12, 256, 256], [1, 1, 1, 1], 16) | ||
| + gen_shapes([1, 1, 1], [12, 256, 256], [1, 1, 1], 16) | ||
| + gen_shapes([1, 1], [256, 256], [1, 1], 16), | ||
| "grad_dtype": [ttnn.bfloat16], | ||
| "input_a_dtype": [ttnn.bfloat16], | ||
| "grad_layout": [ttnn.TILE_LAYOUT], | ||
| "input_a_layout": [ttnn.TILE_LAYOUT], | ||
| "grad_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG], | ||
| "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG], | ||
| "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG], | ||
| }, | ||
| } | ||
|
|
||
|
|
||
| def str_to_float(x): | ||
| try: | ||
| return float(x) | ||
| except: | ||
| return 0.0 | ||
|
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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 device_mesh_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_shape, | ||
| grad_dtype, | ||
| input_a_dtype, | ||
| grad_layout, | ||
| input_a_layout, | ||
| grad_memory_config, | ||
| input_a_memory_config, | ||
| output_memory_config, | ||
| *, | ||
| device, | ||
| ) -> list: | ||
| data_seed = random.randint(0, 20000000) | ||
| torch.manual_seed(data_seed) | ||
|
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||
| torch_grad_tensor_r = gen_func_with_cast_tt( | ||
| partial(torch_random, low=0.01, high=100, dtype=torch.float32), grad_dtype | ||
| )(input_shape) | ||
| torch_grad_tensor_r.requires_grad = True | ||
| torch_grad_tensor_r.retain_grad() | ||
|
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| torch_grad_tensor_c = gen_func_with_cast_tt( | ||
| partial(torch_random, low=0.01, high=100, dtype=torch.float32), grad_dtype | ||
| )(input_shape) | ||
| torch_grad_tensor_c.requires_grad = True | ||
| torch_grad_tensor_c.retain_grad() | ||
|
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||
| torch_input_tensor_ar = gen_func_with_cast_tt( | ||
| partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype | ||
| )(input_shape) | ||
| torch_input_tensor_ar.requires_grad = True | ||
| torch_input_tensor_ar.retain_grad() | ||
|
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| torch_input_tensor_ac = gen_func_with_cast_tt( | ||
| partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype | ||
| )(input_shape) | ||
| torch_input_tensor_ac.requires_grad = True | ||
| torch_input_tensor_ac.retain_grad() | ||
|
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| torch_grad_tensor = torch.complex(torch_grad_tensor_r.to(torch.float32), torch_grad_tensor_c.to(torch.float32)) | ||
| torch_input_tensor_a = torch.complex( | ||
| torch_input_tensor_ar.to(torch.float32), torch_input_tensor_ac.to(torch.float32) | ||
| ) | ||
|
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| golden_function = ttnn.get_golden_function(ttnn.conj_bw) | ||
| torch_output_tensor = golden_function(torch_grad_tensor, torch_input_tensor_a)[0] | ||
|
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||
| grad_tensor_r = ttnn.from_torch( | ||
| torch_grad_tensor_r, | ||
| dtype=grad_dtype, | ||
| layout=grad_layout, | ||
| device=device, | ||
| memory_config=grad_memory_config, | ||
| ) | ||
|
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| grad_tensor_c = ttnn.from_torch( | ||
| torch_grad_tensor_c, dtype=grad_dtype, layout=grad_layout, device=device, memory_config=grad_memory_config | ||
| ) | ||
|
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| input_tensor_ar = ttnn.from_torch( | ||
| torch_input_tensor_ar, | ||
| dtype=input_a_dtype, | ||
| layout=input_a_layout, | ||
| device=device, | ||
| memory_config=input_a_memory_config, | ||
| ) | ||
|
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| input_tensor_ac = ttnn.from_torch( | ||
| torch_input_tensor_ac, | ||
| dtype=input_a_dtype, | ||
| layout=input_a_layout, | ||
| device=device, | ||
| memory_config=input_a_memory_config, | ||
| ) | ||
|
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| grad_tensor = ttnn.complex_tensor(grad_tensor_r, grad_tensor_c) | ||
| input_tensor_a = ttnn.complex_tensor(input_tensor_ar, input_tensor_ac) | ||
|
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| start_time = start_measuring_time() | ||
| output_tensor = ttnn.conj_bw(grad_tensor, input_tensor_a, memory_config=output_memory_config)[0] | ||
| e2e_perf = stop_measuring_time(start_time) | ||
|
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| output_tensor = torch.cat((ttnn.to_torch(output_tensor.real), ttnn.to_torch(output_tensor.imag)), dim=-1) | ||
|
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| return [check_with_pcc(torch_output_tensor, output_tensor, 0.999), e2e_perf] |
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