diff --git a/models/experimental/functional_UFLD_v2/README.md b/models/experimental/functional_UFLD_v2/README.md index b3bdb54cb13..4244b362c7a 100644 --- a/models/experimental/functional_UFLD_v2/README.md +++ b/models/experimental/functional_UFLD_v2/README.md @@ -1,22 +1,26 @@ # Ultra-Fast-Lane-Detection-v2 - +### Platforms: + WH N300 ### Introduction The Ultra-Fast-Lane-Detection-v2 is a PyTorch-based implementation designed for fast and efficient deep lane detection using hybrid anchor-driven ordinal classification. It enhances the speed and accuracy of lane detection models with a focus on real-time applications. -The repository includes models trained on various datasets such as [CULane](https://github.com/cfzd/Ultra-Fast-Lane-Detection-v2/blob/master/model/model_culane.py), [CurveLanes](https://github.com/cfzd/Ultra-Fast-Lane-Detection-v2/blob/master/model/model_curvelanes.py), and [Tusimple](https://github.com/cfzd/Ultra-Fast-Lane-Detection-v2/blob/master/model/model_tusimple.py) - Resource link - [source](https://github.com/cfzd/Ultra-Fast-Lane-Detection-v2) Dataset Link - [source](https://github.com/TuSimple/tusimple-benchmark/issues/3), [kaggle](https://www.kaggle.com/datasets/manideep1108/tusimple?resource=download) + +The repository includes models trained on various datasets such as [CULane](https://github.com/cfzd/Ultra-Fast-Lane-Detection-v2/blob/master/model/model_culane.py), [CurveLanes](https://github.com/cfzd/Ultra-Fast-Lane-Detection-v2/blob/master/model/model_curvelanes.py), and [Tusimple](https://github.com/cfzd/Ultra-Fast-Lane-Detection-v2/blob/master/model/model_tusimple.py) + + + ### Model Details - The entry point to the UFLD_v2 is located at:`models/experimental/functional_UFLD_v2/ttnn/ttnn_UFLD_v2.py` - The model picks up trained weights from the **tusimple_res34.pth** file located at:`models/experimental/functional_UFLD_v2/reference/tusimple_res34.pth` -- Batch Size :1 (Currently supported) +- Batch Size :2 ### UFLD_v2 Demo @@ -27,3 +31,9 @@ Dataset Link - [source](https://github.com/TuSimple/tusimple-benchmark/issues/3) - The output results for both torch and ttnn model will be stored in this directory(.txt files). - `models/experimental/functional_UFLD_v2/demo/results_txt` #### Note: The post-processing is performed using PyTorch. + + +- Use the following command to run the UFLD_v2 performant implementation: + `pytest tests/ttnn/integration_tests/UFLD_v2/test_UFLD_v2_performant.py::test_run_ufld_v2_trace_2cq_inference` + +### Owner: [Venkatesh](https://github.com/vguduruTT) diff --git a/models/experimental/functional_UFLD_v2/demo/demo.py b/models/experimental/functional_UFLD_v2/demo/demo.py index 1a4564b5330..23c18dfae61 100644 --- a/models/experimental/functional_UFLD_v2/demo/demo.py +++ b/models/experimental/functional_UFLD_v2/demo/demo.py @@ -1,5 +1,16 @@ +# SPDX-FileCopyrightText: © 2025 Tenstorrent Inc. + +# SPDX-License-Identifier: Apache-2.0 + import torch import pytest +import json +import numpy as np +import os +import ttnn +import gdown +from pathlib import Path +from loguru import logger from models.experimental.functional_UFLD_v2.reference.UFLD_v2_model import Tu_Simple from models.experimental.functional_UFLD_v2.demo import model_config as cfg from models.experimental.functional_UFLD_v2.demo.demo_utils import ( @@ -16,13 +27,6 @@ preprocess_linear_weight, preprocess_linear_bias, ) -import json -import numpy as np -import os -import ttnn -import gdown -from pathlib import Path -from loguru import logger def custom_preprocessor(model, name): @@ -294,29 +298,6 @@ def custom_preprocessor(model, name): return parameters -def attempt_download(file, key="dumps"): - tests = Path(__file__).parent.parent / key - file_path = tests / Path(str(file).strip().replace("'", "").lower()) - print("tests and file path is", tests, file_path) - if not file_path.exists(): - google_drive_url = "https://drive.google.com/file/d/1pkz8homK433z39uStGK3ZWkDXrnBAMmX/view" - - file_id = google_drive_url.split("/d/")[1].split("/")[0] - google_drive_download_url = f"https://drive.google.com/uc?id={file_id}" - - try: - print(f"Downloading from Google Drive: {google_drive_download_url} to {file_path}...") - gdown.download(google_drive_download_url, str(file_path), quiet=False) - - # Check if the file exists and its size - assert file_path.exists() and file_path.stat().st_size > 1e6, f"Download failed for {file}" - - except Exception as e: - print(f"Error downloading from Google Drive: {e}") - - return file_path - - @pytest.mark.parametrize( "batch_size,input_channels,height,width", [ @@ -327,11 +308,11 @@ def attempt_download(file, key="dumps"): "use_pretrained_weight", [ # False, - True # uncomment to run the model for real weights + True ], ids=[ # "pretrained_weight_false", - "pretrained_weight_true", # uncomment to run the model for real weights + "pretrained_weight_true", ], ) @pytest.mark.parametrize("device_params", [{"l1_small_size": 79104}], indirect=True) @@ -341,8 +322,10 @@ def test_tu_simple_res34_inference(batch_size, input_channels, height, width, de if use_pretrained_weight: logger.info(f"Demo Inference using Pre-trained Weights") file = "tusimple_res34.pth" - downloaded_file_path = attempt_download(file, key="reference") - state_dict = torch.load(downloaded_file_path) + weights_path = "models/experimental/functional_UFLD_v2/tusimple_res34.pth" + if not os.path.exists(weights_path): + os.system("bash models/experimental/functional_UFLD_v2/weights_download.sh") + state_dict = torch.load(weights_path) new_state_dict = {} for key, value in state_dict["model"].items(): new_key = key.replace("model.", "res_model.") diff --git a/models/experimental/functional_UFLD_v2/demo/demo_utils.py b/models/experimental/functional_UFLD_v2/demo/demo_utils.py index 5d819d64ecd..65efa8e1f70 100644 --- a/models/experimental/functional_UFLD_v2/demo/demo_utils.py +++ b/models/experimental/functional_UFLD_v2/demo/demo_utils.py @@ -1,13 +1,15 @@ +# SPDX-FileCopyrightText: © 2025 Tenstorrent Inc. + +# SPDX-License-Identifier: Apache-2.0 + +import ttnn import os, json import torch import torchvision.transforms as transforms -from PIL import Image import numpy as np +from PIL import Image from tqdm import tqdm -import numpy as np from sklearn.linear_model import LinearRegression -import json -import ttnn def loader_func(path): @@ -69,10 +71,7 @@ def bench(pred, gt, y_samples, running_time): @staticmethod def bench_one_submit(pred_file, gt_file): - # try: json_pred = [json.loads(line) for line in open(pred_file, "r").readlines()] - # except BaseException as e: - # raise Exception('Fail to load json file of the prediction.') json_gt = [json.loads(line) for line in open(gt_file).readlines()] if len(json_gt) != len(json_pred): raise Exception("We do not get the predictions of all the test tasks") @@ -122,7 +121,7 @@ def __init__(self, path, list_path, img_transform=None, crop_size=None): self.crop_size = crop_size with open(list_path, "r") as f: self.list = f.readlines() - self.list = [l[1:] if l[0] == "/" else l for l in self.list] # exclude the incorrect path prefix '/' of CULane + self.list = [l[1:] if l[0] == "/" else l for l in self.list] def __getitem__(self, index): name = self.list[index].split()[0] @@ -256,7 +255,6 @@ def generate_tusimple_lines(row_out, row_ext, col_out, col_ext, row_anchor=None, lanes_on_tusimple[tusimple_h_sample > bot_lim] = -2 all_lanes.append(lanes_on_tusimple.tolist()) else: - # all_lanes.append([-2]*56) pass return all_lanes @@ -286,7 +284,7 @@ def run_test_tusimple( else: imgs = imgs.permute(0, 2, 3, 1) imgs = ttnn.from_torch(imgs, dtype=ttnn.bfloat16, layout=ttnn.ROW_MAJOR_LAYOUT, device=device) - out, pred = net(imgs) + out, pred = net(imgs, batch_size=batch_size) out = ttnn.to_torch(out) pred["loc_row"] = ttnn.to_torch(pred["loc_row"]) pred["loc_col"] = ttnn.to_torch(pred["loc_col"]) diff --git a/models/experimental/functional_UFLD_v2/demo/model_config.py b/models/experimental/functional_UFLD_v2/demo/model_config.py index cd615405ff7..e93172769fd 100644 --- a/models/experimental/functional_UFLD_v2/demo/model_config.py +++ b/models/experimental/functional_UFLD_v2/demo/model_config.py @@ -1,3 +1,7 @@ +# SPDX-FileCopyrightText: © 2025 Tenstorrent Inc. + +# SPDX-License-Identifier: Apache-2.0 + dataset = "Tusimple" data_root = "models/experimental/functional_UFLD_v2/demo" # Need to be modified before running epoch = 100 diff --git a/models/experimental/functional_UFLD_v2/demo/reference_model_results.txt b/models/experimental/functional_UFLD_v2/demo/reference_model_results.txt index 45ddc878f21..8b9923748ae 100644 --- a/models/experimental/functional_UFLD_v2/demo/reference_model_results.txt +++ b/models/experimental/functional_UFLD_v2/demo/reference_model_results.txt @@ -1,30 +1,30 @@ -{"lanes": [[-2, -2, -2, -2, -2, -2, -2, -2, -2, 628, 632, 621, 621, 614, 604, 599, 587, 577, 566, 555, 547, 538, 529, 519, 509, 498, 489, 481, 471, 461, 452, 442, 432, 423, 412, 403, 393, 383, 375, 365, 356, 346, 338, 329, 318, 311, 301, 293, 282, 276, 267, 256, 248, 239, 230, 223], [-2, -2, -2, -2, -2, -2, -2, -2, -2, -2, 687, 697, 710, 723, 734, 747, 763, 776, 791, 801, 816, 828, 842, 855, 870, 884, 896, 911, 923, 936, 950, 963, 978, 991, 1006, 1021, 1032, 1046, 1062, 1076, 1090, 1104, 1117, 1132, 1146, 1161, 1173, 1186, 1200, 1215, 1230, 1242, 1255, 1264, 1269, -2], [-2, -2, -2, -2, -2, -2, -2, -2, -2, 709, 734, 763, 792, 821, 854, 885, 919, 953, 984, 1019, 1052, 1086, 1116, 1152, 1184, 1215, 1245, 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640, 650, 660, 670, 680, 690, 700, 710], "raw_file": "1494453525592287732_30.jpg", "run_time": 10} diff --git a/models/experimental/functional_UFLD_v2/reference/UFLD_v2_model.py b/models/experimental/functional_UFLD_v2/reference/UFLD_v2_model.py index 43844265b08..ef07c20cf94 100644 --- a/models/experimental/functional_UFLD_v2/reference/UFLD_v2_model.py +++ b/models/experimental/functional_UFLD_v2/reference/UFLD_v2_model.py @@ -4,7 +4,6 @@ import torch import torch.nn.modules -import numpy as np import torch.nn as nn from typing import Callable, Optional, List @@ -98,8 +97,6 @@ def __init__( self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0]) self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]) self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2]) - # self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) - # self.fc = nn.Linear(512 * block.expansion, num_classes) def _make_layer( self, @@ -175,7 +172,6 @@ def __init__(self, input_height=320, input_width=800): self.input_dim = input_height // 32 * input_width // 32 * 8 self.fc_norm = False self.res_model = Custom_ResNet(BasicBlock, [3, 4, 6, 3]) - # resnet(layers=34, pretrained=True) self.cls = torch.nn.Sequential( torch.nn.Identity(), diff --git a/models/experimental/functional_UFLD_v2/tests/UFLD_v2_performant.py b/models/experimental/functional_UFLD_v2/tests/UFLD_v2_performant.py new file mode 100644 index 00000000000..de7639ecc42 --- /dev/null +++ b/models/experimental/functional_UFLD_v2/tests/UFLD_v2_performant.py @@ -0,0 +1,178 @@ +# SPDX-FileCopyrightText: © 2025 Tenstorrent Inc. + +# SPDX-License-Identifier: Apache-2.0 + + +import ttnn +from models.experimental.functional_UFLD_v2.tests.UFLD_v2_test_infra import create_test_infra + +try: + from tracy import signpost + + use_signpost = True + +except ModuleNotFoundError: + use_signpost = False + + +def buffer_address(tensor): + addr = [] + for ten in ttnn.get_device_tensors(tensor): + addr.append(ten.buffer_address()) + return addr + + +# TODO: Create ttnn apis for this +ttnn.buffer_address = buffer_address + + +def run_ufld_v2_inference( + device, + device_batch_size, +): + test_infra = create_test_infra( + device, + device_batch_size, + ) + + tt_inputs_host, input_mem_config = test_infra.setup_l1_sharded_input(device) + + # # First run configures convs JIT + test_infra.input_tensor = tt_inputs_host.to(device, input_mem_config) + + test_infra.run() + test_infra.validate() + test_infra.dealloc_output() + + # Optimized run + test_infra.input_tensor = tt_inputs_host.to(device, input_mem_config) + test_infra.run() + test_infra.validate() + test_infra.dealloc_output() + + # More optimized run with caching + if use_signpost: + signpost(header="start") + test_infra.input_tensor = tt_inputs_host.to(device, input_mem_config) + test_infra.run() + if use_signpost: + signpost(header="stop") + test_infra.validate() + test_infra.dealloc_output() + + +def run_ufld_v2_trace_inference( + device, + device_batch_size, +): + test_infra = create_test_infra( + device=device, + batch_size=device_batch_size, + ) + tt_inputs_host, input_mem_config = test_infra.setup_l1_sharded_input(device) + + # First run configures convs JIT + test_infra.input_tensor = tt_inputs_host.to(device, input_mem_config) + spec = test_infra.input_tensor.spec + test_infra.run() + test_infra.validate() + test_infra.dealloc_output() + + # Optimized run + test_infra.input_tensor = tt_inputs_host.to(device, input_mem_config) + test_infra.run() + test_infra.validate() + + # Capture + test_infra.input_tensor = tt_inputs_host.to(device, input_mem_config) + + test_infra.dealloc_output() + trace_input_addr = ttnn.buffer_address(test_infra.input_tensor) + tid = ttnn.begin_trace_capture(device, cq_id=0) + test_infra.run() + tt_image_res = ttnn.allocate_tensor_on_device(spec, device) + ttnn.end_trace_capture(device, tid, cq_id=0) + assert trace_input_addr == ttnn.buffer_address(tt_image_res) + + # More optimized run with caching + if use_signpost: + signpost(header="start") + ttnn.copy_host_to_device_tensor(tt_inputs_host, tt_image_res, 0) + ttnn.execute_trace(device, tid, cq_id=0, blocking=True) + if use_signpost: + signpost(header="stop") + test_infra.validate() + + ttnn.release_trace(device, tid) + test_infra.dealloc_output() + + +def ufld_v2_trace_2cqs_inference( + device, + device_batch_size, +): + test_infra = create_test_infra( + device, + device_batch_size, + ) + tt_inputs_host, sharded_mem_config_DRAM, input_mem_config = test_infra.setup_dram_sharded_input(device) + tt_image_res = tt_inputs_host.to(device, sharded_mem_config_DRAM) + # op_event = ttnn.create_event(device) + # write_event = ttnn.create_event(device) + op_event = ttnn.record_event(device, 0) + write_event = ttnn.record_event(device, 0) # check with latest maiin + + # First run configures convs JIT + ttnn.wait_for_event(1, op_event) + ttnn.copy_host_to_device_tensor(tt_inputs_host, tt_image_res, 1) + ttnn.record_event(1, write_event) + ttnn.wait_for_event(0, write_event) + test_infra.input_tensor = ttnn.to_memory_config(tt_image_res, input_mem_config) + spec = test_infra.input_tensor.spec + ttnn.record_event(0, op_event) + test_infra.run() + test_infra.validate() + test_infra.dealloc_output() + # Optimized run + ttnn.wait_for_event(1, op_event) + ttnn.copy_host_to_device_tensor(tt_inputs_host, tt_image_res, 1) + ttnn.record_event(1, write_event) + ttnn.wait_for_event(0, write_event) + test_infra.input_tensor = ttnn.to_memory_config(tt_image_res, input_mem_config) + ttnn.record_event(0, op_event) + test_infra.run() + test_infra.validate() + + # Capture + ttnn.wait_for_event(1, op_event) + ttnn.copy_host_to_device_tensor(tt_inputs_host, tt_image_res, 1) + ttnn.record_event(1, write_event) + ttnn.wait_for_event(0, write_event) + test_infra.input_tensor = ttnn.to_memory_config(tt_image_res, input_mem_config) + ttnn.record_event(0, op_event) + test_infra.dealloc_output() + trace_input_addr = ttnn.buffer_address(test_infra.input_tensor) + tid = ttnn.begin_trace_capture(device, cq_id=0) + test_infra.run() + input_tensor = ttnn.allocate_tensor_on_device(spec, device) + ttnn.end_trace_capture(device, tid, cq_id=0) + assert trace_input_addr == ttnn.buffer_address(input_tensor) + + # More optimized run with caching + if use_signpost: + signpost(header="start") + for iter in range(0, 2): + ttnn.wait_for_event(1, op_event) + ttnn.copy_host_to_device_tensor(tt_inputs_host, tt_image_res, 1) + ttnn.record_event(1, write_event) + ttnn.wait_for_event(0, write_event) + # TODO: Add in place support to ttnn to_memory_config + input_tensor = ttnn.reshard(tt_image_res, input_mem_config, input_tensor) + ttnn.record_event(0, op_event) + ttnn.execute_trace(device, tid, cq_id=0, blocking=False) + ttnn.synchronize_devices(device) + + if use_signpost: + signpost(header="stop") + + ttnn.release_trace(device, tid) diff --git a/models/experimental/functional_UFLD_v2/tests/UFLD_v2_test_infra.py b/models/experimental/functional_UFLD_v2/tests/UFLD_v2_test_infra.py new file mode 100644 index 00000000000..9f50f7cc9d8 --- /dev/null +++ b/models/experimental/functional_UFLD_v2/tests/UFLD_v2_test_infra.py @@ -0,0 +1,147 @@ +# SPDX-FileCopyrightText: © 2025 Tenstorrent Inc. + +# SPDX-License-Identifier: Apache-2.0 + +import ttnn +import torch +from loguru import logger +from tests.ttnn.utils_for_testing import assert_with_pcc +from models.utility_functions import is_wormhole_b0, divup +from models.experimental.functional_UFLD_v2.reference.UFLD_v2_model import Tu_Simple +from models.experimental.functional_UFLD_v2.ttnn.ttnn_UFLD_v2 import ttnn_UFLD_V2 +from ttnn.model_preprocessing import preprocess_model_parameters, infer_ttnn_module_args +from tests.ttnn.integration_tests.UFLD_v2.test_ttnn_UFLD_v2 import custom_preprocessor_whole_model + + +def load_torch_model(): + torch_model = Tu_Simple(input_height=320, input_width=800) + torch_model.eval() + return torch_model + + +def load_ttnn_model(device, torch_model, torch_input_tensor): + parameters = preprocess_model_parameters( + initialize_model=lambda: torch_model, + custom_preprocessor=custom_preprocessor_whole_model, + device=device, + ) + parameters.conv_args = {} + parameters.conv_args = infer_ttnn_module_args( + model=torch_model, run_model=lambda model: torch_model(torch_input_tensor), device=device + ) + ttnn_model = ttnn_UFLD_V2(conv_args=parameters.conv_args, conv_pth=parameters, device=device) + return ttnn_model + + +class UFLD_v2_TestInfra: + def __init__( + self, + device, + batch_size, + model_location_generator=None, + ): + super().__init__() + torch.manual_seed(0) + self.pcc_passed = False + self.pcc_message = "Did you forget to call validate()?" + self.device = device + self.batch_size = batch_size + self.model_location_generator = model_location_generator + input_shape = (batch_size, 3, 320, 800) + self.torch_input_tensor = torch.randn(input_shape, dtype=torch.float32) + torch_model = load_torch_model() + # parameters = preprocess_model_parameters( + # initialize_model=lambda: torch_model, + # custom_preprocessor=custom_preprocessor_whole_model, + # device=device, + # ) + # parameters.conv_args = {} + # parameters.conv_args = infer_ttnn_module_args( + # model=torch_model, run_model=lambda model: torch_model(self.torch_input_tensor), device=None + # ) + # ttnn_model = ttnn_UFLD_V2(conv_args=parameters.conv_args, conv_pth=parameters, device=device) + self.ttnn_ufld_v2_model = load_ttnn_model(self.device, torch_model, self.torch_input_tensor) + self.tt_input_tensor = self.torch_input_tensor.permute(0, 3, 1, 2) + self.tt_input_tensor = ttnn.from_torch(self.tt_input_tensor, ttnn.bfloat16) + self.torch_output_tensor_1, self.torch_output_tensor_2 = torch_model(self.torch_input_tensor) + + def run(self): + self.output_tensor_1, self.output_tensor_2 = self.ttnn_ufld_v2_model( + input=self.input_tensor, batch_size=self.batch_size + ) + + def setup_l1_sharded_input(self, device, torch_input_tensor=None): + if is_wormhole_b0(): + core_grid = ttnn.CoreGrid(y=8, x=8) + else: + exit("Unsupported device") + # torch tensor + torch_input_tensor = self.torch_input_tensor if torch_input_tensor is None else torch_input_tensor + + n, c, h, w = torch_input_tensor.shape + # sharded mem config for fold input + num_cores = core_grid.x * core_grid.y + shard_h = (n * w * h + num_cores - 1) // num_cores + grid_size = core_grid + grid_coord = ttnn.CoreCoord(grid_size.x - 1, grid_size.y - 1) + shard_grid = ttnn.CoreRangeSet({ttnn.CoreRange(ttnn.CoreCoord(0, 0), grid_coord)}) + shard_spec = ttnn.ShardSpec(shard_grid, (shard_h, 16), ttnn.ShardOrientation.ROW_MAJOR) + input_mem_config = ttnn.MemoryConfig( + ttnn.types.TensorMemoryLayout.HEIGHT_SHARDED, ttnn.types.BufferType.L1, shard_spec + ) + torch_input_tensor = torch_input_tensor.permute(0, 2, 3, 1) + torch_input_tensor = torch_input_tensor.reshape(1, 1, h * w * n, c) + tt_inputs_host = ttnn.from_torch(torch_input_tensor, dtype=ttnn.bfloat16, layout=ttnn.ROW_MAJOR_LAYOUT) + tt_inputs_host = ttnn.pad(tt_inputs_host, [1, 1, n * h * w, 16], [0, 0, 0, 0], 0) + return tt_inputs_host, input_mem_config + + def setup_dram_sharded_input(self, device, torch_input_tensor=None, mesh_mapper=None, mesh_composer=None): + tt_inputs_host, input_mem_config = self.setup_l1_sharded_input(device) + dram_grid_size = device.dram_grid_size() + dram_shard_spec = ttnn.ShardSpec( + ttnn.CoreRangeSet( + {ttnn.CoreRange(ttnn.CoreCoord(0, 0), ttnn.CoreCoord(dram_grid_size.x - 1, dram_grid_size.y - 1))} + ), + [ + divup(tt_inputs_host.volume() // tt_inputs_host.shape[-1], (dram_grid_size.x * dram_grid_size.y)), + 16, + ], + ttnn.ShardOrientation.ROW_MAJOR, + ) + sharded_mem_config_DRAM = ttnn.MemoryConfig( + ttnn.TensorMemoryLayout.HEIGHT_SHARDED, ttnn.BufferType.DRAM, dram_shard_spec + ) + + return tt_inputs_host, sharded_mem_config_DRAM, input_mem_config + + def validate(self, output_tensor=None): + output_tensor_1 = ttnn.to_torch(self.output_tensor_1) + output_tensor_2_loc_row = ttnn.to_torch(self.output_tensor_2["loc_row"]) + output_tensor_2_loc_col = ttnn.to_torch(self.output_tensor_2["loc_col"]) + output_tensor_2_exist_row = ttnn.to_torch(self.output_tensor_2["exist_row"]) + output_tensor_2_exist_col = ttnn.to_torch(self.output_tensor_2["exist_col"]) + valid_pcc = 0.99 + self.pcc_passed, self.pcc_message = assert_with_pcc(self.torch_output_tensor_1, output_tensor_1, pcc=valid_pcc) + assert_with_pcc(output_tensor_2_loc_row, self.torch_output_tensor_2["loc_row"], 0.99) + assert_with_pcc(output_tensor_2_loc_col, self.torch_output_tensor_2["loc_col"], 0.99) + assert_with_pcc(output_tensor_2_exist_row, self.torch_output_tensor_2["exist_row"], 0.99) + assert_with_pcc(output_tensor_2_exist_col, self.torch_output_tensor_2["exist_col"], 0.99) + + logger.info(f"UFLD_V2 batch_size={self.batch_size}, PCC={self.pcc_message}") + + def dealloc_output(self): + ttnn.deallocate(self.output_tensor_1) + ttnn.deallocate(self.output_tensor_2["loc_row"]) + ttnn.deallocate(self.output_tensor_2["loc_col"]) + ttnn.deallocate(self.output_tensor_2["exist_row"]) + ttnn.deallocate(self.output_tensor_2["exist_col"]) + + +def create_test_infra( + device, + batch_size, +): + return UFLD_v2_TestInfra( + device, + batch_size, + ) diff --git a/models/experimental/functional_UFLD_v2/tests/test_UFLD_v2.py b/models/experimental/functional_UFLD_v2/tests/test_UFLD_v2.py new file mode 100644 index 00000000000..07a3537a06f --- /dev/null +++ b/models/experimental/functional_UFLD_v2/tests/test_UFLD_v2.py @@ -0,0 +1,146 @@ +# SPDX-FileCopyrightText: © 2025 Tenstorrent Inc. + +# SPDX-License-Identifier: Apache-2.0 + +import os +import ttnn +import time +import torch +import pytest +import torch.nn as nn +from loguru import logger +from models.utility_functions import is_wormhole_b0 +from models.perf.perf_utils import prep_perf_report +from models.experimental.functional_UFLD_v2.ttnn.ttnn_UFLD_v2 import ( + ttnn_UFLD_V2, +) +from models.experimental.functional_UFLD_v2.reference.UFLD_v2_model import Tu_Simple +from models.utility_functions import enable_persistent_kernel_cache, disable_persistent_kernel_cache +from models.perf.device_perf_utils import run_device_perf, check_device_perf, prep_device_perf_report +from ttnn.model_preprocessing import preprocess_model_parameters, infer_ttnn_module_args +from models.experimental.functional_UFLD_v2.demo.demo import custom_preprocessor + + +def get_expected_times(name): + base = {"ufld_v2": (42.7, 0.32)} + return base[name] + + +@pytest.mark.models_performance_bare_metal +@pytest.mark.parametrize("device_params", [{"l1_small_size": 79104}], indirect=True) +@pytest.mark.parametrize( + "batch_size,input_channels,height,width", + [ + (2, 3, 320, 800), + ], +) +@pytest.mark.parametrize( + "use_pretrained_weight", + [ + False, + # True # uncomment to run the model for real weights + ], + ids=[ + "pretrained_weight_false", + # "pretrained_weight_true", # uncomment to run the model for real weights + ], +) +def test_ufld_v2(device, batch_size, input_channels, height, width, use_pretrained_weight): + disable_persistent_kernel_cache() + torch_input_tensor = torch.randn((batch_size, input_channels, height, width)) + reference_model = Tu_Simple(input_height=height, input_width=width) + weights_path = "models/experimental/functional_UFLD_v2/tusimple_res34.pth" + if not os.path.exists(weights_path): + os.system("bash models/experimental/functional_UFLD_v2/weights_download.sh") + state_dict = torch.load(weights_path) + new_state_dict = {} + for key, value in state_dict["model"].items(): + new_key = key.replace("model.", "res_model.") + new_state_dict[new_key] = value + reference_model.load_state_dict(new_state_dict) + parameters = preprocess_model_parameters( + initialize_model=lambda: reference_model, + custom_preprocessor=custom_preprocessor, + device=device, + ) + parameters.conv_args = {} + parameters.conv_args = infer_ttnn_module_args( + model=reference_model, run_model=lambda model: reference_model(torch_input_tensor), device=device + ) + ttnn_model = ttnn_UFLD_V2(conv_args=parameters.conv_args, conv_pth=parameters, device=device) + ttnn_input_tensor = torch.permute(torch_input_tensor, (0, 2, 3, 1)) + ttnn_input_tensor = ttnn_input_tensor.reshape( + 1, + 1, + (ttnn_input_tensor.shape[0] * ttnn_input_tensor.shape[1] * ttnn_input_tensor.shape[2]), + ttnn_input_tensor.shape[3], + ) + ttnn_input_tensor = ttnn.from_torch(ttnn_input_tensor, dtype=ttnn.bfloat16, layout=ttnn.ROW_MAJOR_LAYOUT) + durations = [] + + for i in range(2): + start = time.time() + ttnn_model_output_1, ttnn_model_output_2 = ttnn_model(ttnn_input_tensor, batch_size=batch_size) + end = time.time() + durations.append(end - start) + ttnn.deallocate(ttnn_model_output_1) + ttnn.deallocate(ttnn_model_output_2["loc_row"]) + ttnn.deallocate(ttnn_model_output_2["loc_col"]) + ttnn.deallocate(ttnn_model_output_2["exist_row"]) + ttnn.deallocate(ttnn_model_output_2["exist_col"]) + enable_persistent_kernel_cache() + + inference_and_compile_time, inference_time, *_ = durations + + expected_compile_time, expected_inference_time = get_expected_times("ufld_v2") + + prep_perf_report( + model_name="models/experimental/functional_UFLD_v2", + batch_size=batch_size, + inference_and_compile_time=inference_and_compile_time, + inference_time=inference_time, + expected_compile_time=expected_compile_time, + expected_inference_time=expected_inference_time, + comments="", + inference_time_cpu=0.0, + ) + + logger.info(f"Compile time: {inference_and_compile_time - inference_time}") + logger.info(f"Inference time: {inference_time}") + logger.info(f"Samples per second: {1 / inference_time * batch_size}") + assert ( + inference_time < expected_inference_time + ), f"Expected inference time: {expected_inference_time} Actual inference time: {inference_time}" + + +@pytest.mark.parametrize( + "batch_size, expected_perf,test", + [ + [2, 74.5, "UFLD-v2"], + ], +) +@pytest.mark.models_device_performance_bare_metal +def test_perf_device_bare_metal_ufld_v2(batch_size, expected_perf, test): + subdir = "ttnn_UFLD_v2" + num_iterations = 1 + margin = 0.03 + expected_perf = expected_perf if is_wormhole_b0() else 0 + + command = f"pytest models/experimental/functional_UFLD_v2/demo/demo.py::test_tu_simple_res34_inference" + cols = ["DEVICE FW", "DEVICE KERNEL", "DEVICE BRISC KERNEL"] + + inference_time_key = "AVG DEVICE KERNEL SAMPLES/S" + expected_perf_cols = {inference_time_key: expected_perf} + + post_processed_results = run_device_perf(command, subdir, num_iterations, cols, batch_size) + expected_results = check_device_perf(post_processed_results, margin, expected_perf_cols, assert_on_fail=True) + + logger.info(f"{expected_results}") + + prep_device_perf_report( + model_name=f"ttnn_ufld_v2{batch_size}", + batch_size=batch_size, + post_processed_results=post_processed_results, + expected_results=expected_results, + comments=test.replace("/", "_"), + ) diff --git a/models/experimental/functional_UFLD_v2/ttnn/ttnn_UFLD_v2.py b/models/experimental/functional_UFLD_v2/ttnn/ttnn_UFLD_v2.py index 0c43d88b9ae..0ace0198bc2 100644 --- a/models/experimental/functional_UFLD_v2/ttnn/ttnn_UFLD_v2.py +++ b/models/experimental/functional_UFLD_v2/ttnn/ttnn_UFLD_v2.py @@ -5,25 +5,16 @@ import ttnn -def p(x, b="x"): - print(f"{b}'s shape is {x.shape}") - print(f"{b}'s layout is {x.layout}") - print(f"{b}'s dtype is {x.dtype}") - print(f"{b}'s config is {x.memory_config()}") - - class ttnn_UFLD_V2_Conv2D: def __init__( self, conv, conv_pth, - bn=None, device=None, cache={}, activation="", activation_dtype=ttnn.bfloat16, weights_dtype=ttnn.bfloat8_b, - use_1d_systolic_array=True, shard_layout=ttnn.TensorMemoryLayout.HEIGHT_SHARDED, ): self.conv = conv @@ -34,7 +25,6 @@ def __init__( self.padding = conv.padding self.stride = conv.stride self.groups = conv.groups - self.use_1d_systolic_array = use_1d_systolic_array self.deallocate_activation = False self.cache = cache self.compute_config = ttnn.init_device_compute_kernel_config( @@ -52,13 +42,10 @@ def __init__( enable_act_double_buffer=False, enable_split_reader=False, enable_subblock_padding=False, - reshard_if_not_optimal=False, + reshard_if_not_optimal=True, activation=activation, - input_channels_alignment=8, + input_channels_alignment=16 if self.conv.batch_size == 1 else 8, ) - config_override = None - if config_override and "act_block_h" in config_override: - self.conv_config.act_block_h_override = config_override["act_block_h"] if conv_pth.bias is not None: bias = ttnn.from_device(conv_pth.bias) self.bias = bias @@ -114,7 +101,7 @@ def __call__(self, input): x_identity, out_ht, out_wdth = self.downsample(input) x = ttnn.add(x, x_identity, memory_config=x.memory_config()) x = ttnn.relu(x) - + ttnn.deallocate(x_identity) return x @@ -170,10 +157,13 @@ def __init__(self, conv_args, conv_pth, device): conv_args.layer4[2], conv_pth.layer4_2, device=self.device, is_downsample=False ) - def __call__(self, x, batch_size=1): # [1, 320, 800, 3] - x, out_ht, out_wdth = self.conv1(x) - x = ttnn.max_pool2d( - x, + def __call__(self, x, batch_size=1): + x1, out_ht, out_wdth = self.conv1(x) + if x.is_sharded(): + ttnn.deallocate(x) + x1 = ttnn.reallocate(x1) + x1 = ttnn.max_pool2d( + x1, batch_size=batch_size, input_h=out_ht, input_w=out_wdth, @@ -183,13 +173,13 @@ def __call__(self, x, batch_size=1): # [1, 320, 800, 3] padding=[self.maxpool_args.padding, self.maxpool_args.padding], dilation=[self.maxpool_args.dilation, self.maxpool_args.dilation], ) - if x.is_sharded(): - x = ttnn.sharded_to_interleaved(x, memory_config=ttnn.L1_MEMORY_CONFIG) + x = ttnn.sharded_to_interleaved(x1, memory_config=ttnn.L1_MEMORY_CONFIG) + ttnn.deallocate(x1) + x = ttnn.reallocate(x) x = ttnn.to_layout(x, ttnn.TILE_LAYOUT) x = self.layer1_0(x) x = self.layer1_1(x) x = self.layer1_2(x) - x = self.layer2_0(x) x = self.layer2_1(x) x = self.layer2_2(x) @@ -201,7 +191,6 @@ def __call__(self, x, batch_size=1): # [1, 320, 800, 3] x = self.layer3_3(x) x = self.layer3_4(x) x = self.layer3_5(x) - x = self.layer4_0(input=x) x = self.layer4_1(input=x) x = self.layer4_2(input=x) @@ -234,8 +223,7 @@ def __init__(self, conv_args, conv_pth, device): self.res_model = ttnn_Resnet_34(conv_args, conv_pth.res_model, device=self.device) self.pool = ttnn_UFLD_V2_Conv2D(conv_args.pool, conv_pth.pool, activation="", device=self.device) - def __call__(self, input): - batch_size = input.shape[0] + def __call__(self, input, batch_size=1): fea = self.res_model(input, batch_size=batch_size) fea, out_h, out_w = self.pool(fea) if fea.is_sharded(): @@ -254,9 +242,7 @@ def __call__(self, input): ) } ) - shard_shape = [32, 32] - print("shard shape is", shard_shape) - shard_spec = ttnn.ShardSpec(shard_grid, shard_shape, ttnn.ShardOrientation.ROW_MAJOR) + shard_spec = ttnn.ShardSpec(shard_grid, [32, 32], ttnn.ShardOrientation.ROW_MAJOR) width_sharded_mem_config = ttnn.MemoryConfig( ttnn.TensorMemoryLayout.WIDTH_SHARDED, ttnn.BufferType.L1, shard_spec ) diff --git a/models/experimental/functional_UFLD_v2/weights_download.sh b/models/experimental/functional_UFLD_v2/weights_download.sh new file mode 100644 index 00000000000..13c57a85178 --- /dev/null +++ b/models/experimental/functional_UFLD_v2/weights_download.sh @@ -0,0 +1,29 @@ +#!/bin/bash + +# Check if gdown is installed, install it if not +if ! python -c "import gdown" &> /dev/null; then + echo "gdown not found, installing..." + pip install gdown +else + echo "gdown is already installed." +fi + +# Output filename +OUTPUT="models/experimental/functional_UFLD_v2/tusimple_res34.pth" + +# Create output directory if it doesn't exist +mkdir -p "$(dirname "$OUTPUT")" + +# Google Drive URL and File ID for downloading +google_drive_url="https://drive.google.com/file/d/1pkz8homK433z39uStGK3ZWkDXrnBAMmX/view" +file_id=$(echo $google_drive_url | grep -oP "(?<=/d/)[^/]+") + +google_drive_download_url="https://drive.google.com/uc?id=$file_id" + +# Download the file using gdown +if gdown "$google_drive_download_url" -O "${OUTPUT}"; then + echo "File downloaded successfully: ${OUTPUT}" +else + echo "Error downloading the file." + exit 1 +fi diff --git a/tests/scripts/run_performance.sh b/tests/scripts/run_performance.sh index bf5886838a3..ce5b5c04aa4 100755 --- a/tests/scripts/run_performance.sh +++ b/tests/scripts/run_performance.sh @@ -25,6 +25,8 @@ run_perf_models_other() { env WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest models/demos/wormhole/distilbert/tests/test_perf_distilbert.py -m $test_marker env WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest -n auto models/demos/whisper/tests/test_performance.py -m $test_marker + + env WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest models/experimental/functional_UFLD_v2/tests/test_UFLD_v2.py -m $test_marker fi env pytest -n auto tests/ttnn/integration_tests/bert/test_performance.py -m $test_marker @@ -133,6 +135,8 @@ run_device_perf_models() { env WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest models/demos/yolov4/tests/ -m $test_marker env WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest models/demos/wormhole/distilbert/tests -m $test_marker + + env WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest -n auto models/experimental/functional_UFLD_v2/tests/ -m $test_marker fi ## Merge all the generated reports diff --git a/tests/scripts/run_python_model_tests.sh b/tests/scripts/run_python_model_tests.sh index 576ef139fc7..de6a4d4d782 100755 --- a/tests/scripts/run_python_model_tests.sh +++ b/tests/scripts/run_python_model_tests.sh @@ -43,6 +43,9 @@ run_python_model_tests_wormhole_b0() { # Mamba WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest -svv models/demos/wormhole/mamba/tests/test_residual_block.py -k "pretrained_weight_false" + #UFLD_V2 + WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest /tests/ttnn/integration_tests/UFLD_v2/test_ttnn_UFLD_v2.py::test_UFD_V2_Model + # Llama3.1-8B llama8b=/mnt/MLPerf/tt_dnn-models/llama/Meta-Llama-3.1-8B-Instruct/ # Llama3.2-1B diff --git a/tests/scripts/single_card/run_single_card_demo_tests.sh b/tests/scripts/single_card/run_single_card_demo_tests.sh index 8535f751c76..f233151c1eb 100755 --- a/tests/scripts/single_card/run_single_card_demo_tests.sh +++ b/tests/scripts/single_card/run_single_card_demo_tests.sh @@ -61,6 +61,9 @@ run_common_func_tests() { #RoBERTa pytest --disable-warnings models/demos/roberta/demo/demo.py --timeout 600; fail+=$? + #UFLD_v2 + + WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest --disable-warnings models/experimental/functional_UFLD_v2/demo/demo.py::test_tu_simple_res34_inference --timeout 600; fail+=$? return $fail } diff --git a/tests/ttnn/integration_tests/UFLD_v2/test_UFLD_v2_performant.py b/tests/ttnn/integration_tests/UFLD_v2/test_UFLD_v2_performant.py new file mode 100644 index 00000000000..67f902d6ee9 --- /dev/null +++ b/tests/ttnn/integration_tests/UFLD_v2/test_UFLD_v2_performant.py @@ -0,0 +1,52 @@ +# SPDX-FileCopyrightText: © 2025 Tenstorrent Inc. + +# SPDX-License-Identifier: Apache-2.0 + +import pytest +from models.experimental.functional_UFLD_v2.tests.UFLD_v2_performant import ( + run_ufld_v2_inference, + run_ufld_v2_trace_inference, + ufld_v2_trace_2cqs_inference, +) + + +@pytest.mark.parametrize("device_params", [{"l1_small_size": 79104}], indirect=True) +@pytest.mark.parametrize("device_batch_size", [(1)]) +def test_run_ufld_v2_inference( + device, + device_batch_size, + use_program_cache, +): + run_ufld_v2_inference( + device, + device_batch_size, + ) + + +@pytest.mark.parametrize("device_params", [{"l1_small_size": 32768, "trace_region_size": 3686400}], indirect=True) +@pytest.mark.parametrize("device_batch_size", [(1)]) +def test_run_ufld_v2_trace_inference( + device, + device_batch_size, + use_program_cache, +): + run_ufld_v2_trace_inference( + device, + device_batch_size, + ) + + +# @pytest.mark.skip(reason="Skipping test_run_ufld_v2_trace_2cq_inference for now") +@pytest.mark.parametrize( + "device_params", [{"l1_small_size": 24576, "trace_region_size": 3686400, "num_command_queues": 2}], indirect=True +) +@pytest.mark.parametrize("device_batch_size", [(1)]) +def test_run_ufld_v2_trace_2cq_inference( + device, + device_batch_size, + use_program_cache, +): + ufld_v2_trace_2cqs_inference( + device=device, + device_batch_size=device_batch_size, + ) diff --git a/tests/ttnn/integration_tests/UFLD_v2/test_ttnn_UFLD_v2.py b/tests/ttnn/integration_tests/UFLD_v2/test_ttnn_UFLD_v2.py index d3db05dca7f..04df1fa81b9 100644 --- a/tests/ttnn/integration_tests/UFLD_v2/test_ttnn_UFLD_v2.py +++ b/tests/ttnn/integration_tests/UFLD_v2/test_ttnn_UFLD_v2.py @@ -3,6 +3,7 @@ # SPDX-License-Identifier: Apache-2.0 import ttnn +import os import pytest import torch import torch.nn as nn @@ -15,11 +16,7 @@ ) from models.experimental.functional_UFLD_v2.reference.UFLD_v2_model import Tu_Simple, BasicBlock from models.experimental.functional_UFLD_v2.ttnn.ttnn_UFLD_v2 import ttnn_UFLD_V2, ttnn_Basic_Block, ttnn_UFLD_V2_Conv2D -from models.experimental.functional_UFLD_v2.demo.demo import attempt_download from tests.ttnn.utils_for_testing import assert_with_pcc -from models.utility_functions import ( - skip_for_grayskull, -) def custom_preprocessor_conv(model, name): @@ -321,7 +318,6 @@ def custom_preprocessor_whole_model(model, name): return parameters -@skip_for_grayskull() @pytest.mark.parametrize( "batch_size,input_channels,height,width", [ @@ -353,10 +349,9 @@ def test_UFLD_V2_conv(device, batch_size, input_channels, height, width): ttnn_output = ttnn.to_torch(ttnn_output[0]) ttnn_output = ttnn_output.permute(0, 3, 1, 2) ttnn_output = ttnn_output.reshape(torch_out.shape) - assert_with_pcc(ttnn_output, torch_out, 0.999) + assert_with_pcc(ttnn_output, torch_out, 0.99) -@skip_for_grayskull() @pytest.mark.parametrize( "batch_size,input_channels,height,width", [ @@ -370,7 +365,13 @@ def test_ufld_v2_basic_block(device, batch_size, input_channels, height, width): torch_model.eval() torch_input_tensor = torch.randn((batch_size, input_channels, height, width), dtype=torch.bfloat16) ttnn_input_tensor = torch.permute(torch_input_tensor, (0, 2, 3, 1)) - ttnn_input_tensor = ttnn.from_torch(ttnn_input_tensor, dtype=ttnn.bfloat16, layout=ttnn.TILE_LAYOUT) + ttnn_input_tensor = ttnn_input_tensor.reshape( + 1, + 1, + (ttnn_input_tensor.shape[0] * ttnn_input_tensor.shape[1] * ttnn_input_tensor.shape[2]), + ttnn_input_tensor.shape[3], + ) + ttnn_input_tensor = ttnn.from_torch(ttnn_input_tensor, dtype=ttnn.bfloat16, layout=ttnn.TILE_LAYOUT, device=device) parameters = preprocess_model_parameters( initialize_model=lambda: torch_model, custom_preprocessor=custom_preprocessor_basic_block, @@ -382,30 +383,29 @@ def test_ufld_v2_basic_block(device, batch_size, input_channels, height, width): ) ttnn_model = ttnn_Basic_Block(parameters.conv_args, parameters, device=device) torch_out = torch_model(torch_input_tensor) - ttnn_output = ttnn_model(device=device, input=ttnn_input_tensor) + ttnn_output = ttnn_model(ttnn_input_tensor) ttnn_output = ttnn.to_torch(ttnn_output) ttnn_output = ttnn_output.permute(0, 3, 1, 2) ttnn_output = ttnn_output.reshape(torch_out.shape) - assert_with_pcc(ttnn_output, torch_out, 0.999) + assert_with_pcc(ttnn_output, torch_out, 0.99) @pytest.mark.parametrize( "batch_size,input_channels,height,width", [ - # (1, 3, 320, 800), - (2, 3, 320, 800), + (1, 3, 320, 800), + # (2, 3, 320, 800), ], ) -@skip_for_grayskull() @pytest.mark.parametrize( "use_pretrained_weight", [ - # False, - True + False, + # True ], # uncomment to run the model for real weights ids=[ - # "pretrained_weight_false", - "pretrained_weight_true", # uncomment to run the model for real weights + "pretrained_weight_false", + # "pretrained_weight_true", # uncomment to run the model for real weights ], ) @pytest.mark.parametrize("device_params", [{"l1_small_size": 79104}], indirect=True) @@ -416,18 +416,25 @@ def test_UFD_V2_Model(device, batch_size, input_channels, height, width, use_pre torch_input_tensor = torch.randn((batch_size, input_channels, height, width), dtype=torch.bfloat16) torch_output = torch_model(torch_input_tensor) if use_pretrained_weight: - downloaded_file_path = attempt_download("tusimple_res34.pth", key="reference") - state_dict = torch.load(downloaded_file_path) - new_state_dict = {} - for key, value in state_dict["model"].items(): - new_key = key.replace("model.", "res_model.") - new_state_dict[new_key] = value - torch_model.load_state_dict(new_state_dict) + weights_path = "models/experimental/functional_UFLD_v2/tusimple_res34.pth" + if not os.path.exists(weights_path): + os.system("bash models/experimental/functional_UFLD_v2/weights_download.sh") + state_dict = torch.load(weights_path) + new_state_dict = {} + for key, value in state_dict["model"].items(): + new_key = key.replace("model.", "res_model.") + new_state_dict[new_key] = value + torch_model.load_state_dict(new_state_dict) ttnn_input_tensor = torch.permute(torch_input_tensor, (0, 2, 3, 1)) - ttnn_input_tensor = ttnn.from_torch( - ttnn_input_tensor, dtype=ttnn.bfloat16, layout=ttnn.ROW_MAJOR_LAYOUT, device=device + ttnn_input_tensor = ttnn_input_tensor.reshape( + 1, + 1, + (ttnn_input_tensor.shape[0] * ttnn_input_tensor.shape[1] * ttnn_input_tensor.shape[2]), + ttnn_input_tensor.shape[3], ) + + ttnn_input_tensor = ttnn.from_torch(ttnn_input_tensor, dtype=ttnn.bfloat16, layout=ttnn.ROW_MAJOR_LAYOUT) parameters = preprocess_model_parameters( initialize_model=lambda: torch_model, custom_preprocessor=custom_preprocessor_whole_model, @@ -439,17 +446,14 @@ def test_UFD_V2_Model(device, batch_size, input_channels, height, width, use_pre ) ttnn_model = ttnn_UFLD_V2(conv_args=parameters.conv_args, conv_pth=parameters, device=device) torch_output, pred_list = torch_model(torch_input_tensor) - ttnn_output, tt_pred_list = ttnn_model(input=ttnn_input_tensor) + ttnn_output, tt_pred_list = ttnn_model(input=ttnn_input_tensor, batch_size=batch_size) ttnn_output = ttnn.to_torch(ttnn_output) tt_pred_list["loc_row"] = ttnn.to_torch(tt_pred_list["loc_row"]) tt_pred_list["loc_col"] = ttnn.to_torch(tt_pred_list["loc_col"]) tt_pred_list["exist_row"] = ttnn.to_torch(tt_pred_list["exist_row"]) tt_pred_list["exist_col"] = ttnn.to_torch(tt_pred_list["exist_col"]) - if use_pretrained_weight: - assert_with_pcc(torch_output, ttnn_output, 0.998) - else: - assert_with_pcc(torch_output, ttnn_output, 0.999) - assert_with_pcc(tt_pred_list["loc_row"], pred_list["loc_row"], 0.999) - assert_with_pcc(tt_pred_list["loc_col"], pred_list["loc_col"], 0.999) - assert_with_pcc(tt_pred_list["exist_row"], pred_list["exist_row"], 0.999) - assert_with_pcc(tt_pred_list["exist_col"], pred_list["exist_col"], 0.999) + assert_with_pcc(torch_output, ttnn_output, 0.998) + assert_with_pcc(tt_pred_list["loc_row"], pred_list["loc_row"], 0.998) + assert_with_pcc(tt_pred_list["loc_col"], pred_list["loc_col"], 0.998) + assert_with_pcc(tt_pred_list["exist_row"], pred_list["exist_row"], 0.998) + assert_with_pcc(tt_pred_list["exist_col"], pred_list["exist_col"], 0.998)