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* initial track_pybuda; 2313 rc * fix typo in ViLT tests * Modify test case paths * Modify clean up command to include onnx and tflite file formats * Fix ONNX download paths for ResNet and RetinaNet * Add NotImplemented error to Fuyu-8B model * Fix ONNX model paths * Add clean up for .h5 files * Remove .png files from clean up * Add wideresnet in model_demos * Add Xception in model_demos * Add GhostNet in model_demos * Fix model demos table * Fix WideResNet and Xception file paths * Stream image and label files * Patch Xception variant for GS silicon * Skip Fuyu-8B (WIP) * Remove commented code
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57 changes: 57 additions & 0 deletions
57
model_demos/cv_demos/beit/pytorch_beit_classify_16_224_hf.py
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| # BeiT Model Demo | ||
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| import os | ||
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| import pybuda | ||
| import requests | ||
| from PIL import Image | ||
| from pybuda._C.backend_api import BackendDevice | ||
| from transformers import BeitForImageClassification, BeitImageProcessor | ||
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| def run_beit_classify_224_hf_pytorch(variant="microsoft/beit-base-patch16-224"): | ||
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| # Set PyBuda configuration parameters | ||
| compiler_cfg = pybuda.config._get_global_compiler_config() | ||
| available_devices = pybuda.detect_available_devices() | ||
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| compiler_cfg.enable_t_streaming = True | ||
| if variant == "microsoft/beit-base-patch16-224": | ||
| compiler_cfg.retain_tvm_python_files = True | ||
| compiler_cfg.enable_tvm_constant_prop = True | ||
| if available_devices[0] == BackendDevice.Grayskull: | ||
| os.environ["PYBUDA_ENABLE_STABLE_SOFTMAX"] = "1" | ||
| elif variant == "microsoft/beit-large-patch16-224": | ||
| if available_devices[0] == BackendDevice.Grayskull: | ||
| compiler_cfg.retain_tvm_python_files = True | ||
| compiler_cfg.enable_tvm_constant_prop = True | ||
| os.environ["PYBUDA_ENABLE_STABLE_SOFTMAX"] = "1" | ||
| else: | ||
| compiler_cfg.default_df_override = pybuda._C.DataFormat.Float16_b | ||
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| # Create PyBuda module from PyTorch model | ||
| image_processor = BeitImageProcessor.from_pretrained(variant) | ||
| model = BeitForImageClassification.from_pretrained(variant) | ||
| tt_model = pybuda.PyTorchModule("pt_beit_classif_16_224", model) | ||
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| # Get sample image | ||
| url = "http://images.cocodataset.org/val2017/000000039769.jpg" | ||
| sample_image = Image.open(requests.get(url, stream=True).raw) | ||
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| # Preprocessing | ||
| img_tensor = image_processor(sample_image, return_tensors="pt").pixel_values | ||
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| # Run inference on Tenstorrent device | ||
| output_q = pybuda.run_inference(tt_model, inputs=([img_tensor])) | ||
| output = output_q.get()[0].value().detach().float().numpy() | ||
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| # Postprocessing | ||
| predicted_class_idx = output.argmax(-1).item() | ||
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| # Print output | ||
| print("Predicted class:", predicted_class_idx) | ||
| print(model.config.id2label[predicted_class_idx]) | ||
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| if __name__ == "__main__": | ||
| run_beit_classify_224_hf_pytorch() |
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63 changes: 63 additions & 0 deletions
63
model_demos/cv_demos/efficientnet_lite/tflite_efficientnet_lite0_1x1.py
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,63 @@ | ||
| # EfficientNet-Lite0 1x1 demo | ||
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| import os | ||
| import shutil | ||
| import tarfile | ||
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| import pybuda | ||
| import requests | ||
| import torch | ||
| from pybuda import TFLiteModule | ||
| from pybuda._C.backend_api import BackendDevice | ||
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| def run_efficientnet_lite0_1x1(): | ||
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| # Device specific configurations | ||
| available_devices = pybuda.detect_available_devices() | ||
| if available_devices: | ||
| if available_devices[0] != BackendDevice.Wormhole_B0: | ||
| raise NotImplementedError("Model not supported on Grayskull") | ||
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| # Set PyBuda configuration parameters | ||
| compiler_cfg = pybuda.config._get_global_compiler_config() | ||
| compiler_cfg.balancer_policy = "Ribbon" | ||
| compiler_cfg.enable_t_streaming = True | ||
| compiler_cfg.enable_tvm_constant_prop = True | ||
| compiler_cfg.graph_solver_self_cut_type = "FastCut" | ||
| compiler_cfg.default_df_override = pybuda.DataFormat.Float16 | ||
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| # Set PyBDUA environment variable | ||
| os.environ["PYBUDA_OVERRIDE_DEVICE_YAML"] = "wormhole_b0_1x1.yaml" | ||
| os.environ["PYBUDA_FORCE_CONV_MULTI_OP_FRACTURE"] = "1" | ||
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| # Download model weights | ||
| MODEL = "efficientnet-lite0" | ||
| url = f"https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/lite/{MODEL}.tar.gz" | ||
| extract_to = "cv_demos/efficientnet_lite" | ||
| file_name = url.split("/")[-1] | ||
| response = requests.get(url, stream=True) | ||
| with open(file_name, "wb") as f: | ||
| f.write(response.content) | ||
| with tarfile.open(file_name, "r:gz") as tar: | ||
| tar.extractall(path=extract_to) | ||
| os.remove(file_name) | ||
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| # Load model path | ||
| tflite_path = f"cv_demos/efficientnet_lite/{MODEL}/{MODEL}-fp32.tflite" | ||
| tt_model = TFLiteModule("tflite_efficientnet_lite0", tflite_path) | ||
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| # Run inference on Tenstorrent device | ||
| input_shape = (1, 224, 224, 3) | ||
| input_tensor = torch.rand(input_shape) | ||
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| output_q = pybuda.run_inference(tt_model, inputs=([input_tensor])) | ||
| output = output_q.get()[0].value().detach().float().numpy() | ||
| print(output) | ||
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| # Remove remanent files | ||
| shutil.rmtree(extract_to + "/" + MODEL) | ||
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| if __name__ == "__main__": | ||
| run_efficientnet_lite0_1x1() |
63 changes: 63 additions & 0 deletions
63
model_demos/cv_demos/efficientnet_lite/tflite_efficientnet_lite4_1x1.py
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,63 @@ | ||
| # EfficientNet-Lite4 1x1 demo | ||
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| import os | ||
| import shutil | ||
| import tarfile | ||
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| import pybuda | ||
| import requests | ||
| import torch | ||
| from pybuda import TFLiteModule | ||
| from pybuda._C.backend_api import BackendDevice | ||
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| def run_efficientnet_lite4_1x1(): | ||
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| # Device specific configurations | ||
| available_devices = pybuda.detect_available_devices() | ||
| if available_devices: | ||
| if available_devices[0] != BackendDevice.Wormhole_B0: | ||
| raise NotImplementedError("Model not supported on Grayskull") | ||
|
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| # Set PyBuda configuration parameters | ||
| compiler_cfg = pybuda.config._get_global_compiler_config() | ||
| compiler_cfg.balancer_policy = "Ribbon" | ||
| compiler_cfg.enable_t_streaming = True | ||
| compiler_cfg.enable_tvm_constant_prop = True | ||
| compiler_cfg.graph_solver_self_cut_type = "FastCut" | ||
| compiler_cfg.default_df_override = pybuda.DataFormat.Float16 | ||
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| # Set PyBDUA environment variable | ||
| os.environ["PYBUDA_OVERRIDE_DEVICE_YAML"] = "wormhole_b0_1x1.yaml" | ||
| os.environ["PYBUDA_FORCE_CONV_MULTI_OP_FRACTURE"] = "1" | ||
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| # Download model weights | ||
| MODEL = "efficientnet-lite4" | ||
| url = f"https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/lite/{MODEL}.tar.gz" | ||
| extract_to = "cv_demos/efficientnet_lite" | ||
| file_name = url.split("/")[-1] | ||
| response = requests.get(url, stream=True) | ||
| with open(file_name, "wb") as f: | ||
| f.write(response.content) | ||
| with tarfile.open(file_name, "r:gz") as tar: | ||
| tar.extractall(path=extract_to) | ||
| os.remove(file_name) | ||
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| # Load model path | ||
| tflite_path = f"cv_demos/efficientnet_lite/{MODEL}/{MODEL}-fp32.tflite" | ||
| tt_model = TFLiteModule("tflite_efficientnet_lite4", tflite_path) | ||
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| # STEP 3: Run inference on Tenstorrent device | ||
| input_shape = (1, 320, 320, 3) | ||
| input_tensor = torch.rand(input_shape) | ||
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| output_q = pybuda.run_inference(tt_model, inputs=([input_tensor])) | ||
| output = output_q.get()[0].value().detach().float().numpy() | ||
| print(output) | ||
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| # Remove remanent files | ||
| shutil.rmtree(extract_to + "/" + MODEL) | ||
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| if __name__ == "__main__": | ||
| run_efficientnet_lite4_1x1() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,53 @@ | ||
| # Ghostnet | ||
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| import os | ||
| import urllib | ||
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| import pybuda | ||
| import requests | ||
| import timm | ||
| import torch | ||
| from PIL import Image | ||
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| def run_ghostnet_timm(): | ||
| # Set PyBuda configuration parameters | ||
| compiler_cfg = pybuda.config._get_global_compiler_config() # load global compiler config object | ||
| compiler_cfg.enable_t_streaming = True | ||
| compiler_cfg.balancer_policy = "Ribbon" | ||
| compiler_cfg.default_df_override = pybuda.DataFormat.Float16_b | ||
| os.environ["PYBUDA_RIBBON2"] = "1" | ||
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| model = timm.create_model("ghostnet_100", pretrained=True) | ||
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| # Create PyBuda module from PyTorch model | ||
| tt_model = pybuda.PyTorchModule("ghostnet_100_timm_pt", model) | ||
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| data_config = timm.data.resolve_data_config({}, model=model) | ||
| transforms = timm.data.create_transform(**data_config) | ||
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| url = "https://raw.githubusercontent.com/pytorch/hub/master/images/dog.jpg" | ||
| img = Image.open(requests.get(url, stream=True).raw).convert("RGB") | ||
| img_tensor = transforms(img).unsqueeze(0) | ||
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| # Run inference on Tenstorrent device | ||
| output_q = pybuda.run_inference(tt_model, inputs=([img_tensor])) | ||
| output = output_q.get()[0].value() | ||
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| top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) | ||
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| # Get imagenet class mappings | ||
| url = "https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt" | ||
| image_classes = urllib.request.urlopen(url) | ||
| categories = [s.decode("utf-8").strip() for s in image_classes.readlines()] | ||
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| for i in range(top5_probabilities.size(1)): | ||
| class_idx = top5_class_indices[0, i].item() | ||
| class_prob = top5_probabilities[0, i].item() | ||
| class_label = categories[class_idx] | ||
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| print(f"{class_label} : {class_prob}") | ||
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| if __name__ == "__main__": | ||
| run_ghostnet_timm() |
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