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https://github.com/nod-ai/AMD-SHARK-Studio.git
synced 2026-02-19 11:56:43 -05:00
Fix load json input bug in SharkDownloader albert test
This commit is contained in:
Chi Liu
@@ -52,6 +52,21 @@ class SharkDownloader:
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print("Error. No tank_url, No model name,Please input either one.")
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return
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self.workdir = os.path.join(os.path.dirname(__file__), self.local_tank_dir)
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os.makedirs(self.workdir, exist_ok=True)
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print(f"TMP_MODEL_DIR = {self.workdir}")
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# use model name get dir.
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self.model_name_dir = os.path.join(self.workdir, str(self.model_name))
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if not os.path.exists(self.model_name_dir):
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print(
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"Model has not been download."
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"shark_downloader will automatically download by "
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"tank_url if provided. You can also manually to "
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"download the model from shark_tank by yourself."
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)
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os.makedirs(self.model_name_dir, exist_ok=True)
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print(f"TMP_MODELNAME_DIR = {self.model_name_dir}")
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# read inputs from json file
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self.load_json_input()
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# get milr model file
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@@ -66,42 +81,65 @@ class SharkDownloader:
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def load_json_input(self):
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print("load json inputs")
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if self.model_type in ["tflite-tosa"]:
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input_url = (
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self.tank_url
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+ "/"
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+ str(self.model_name)
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+ "/"
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+ "input.json"
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)
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input_file = "/".join(
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[self.model_name_dir, str(self.input_json)]
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)
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if os.path.exists(input_file):
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print("Input has been downloaded before.", input_file)
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else:
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print("Download input", input_url)
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urllib.request.urlretrieve(input_url, input_file)
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args = []
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with open(self.input_json, "r") as f:
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with open(input_file, "r") as f:
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args = json.load(f)
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self.inputs = [np.asarray(arg, dtype=self.input_type) for arg in args]
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else:
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print("No json input required for current model type. You could call setup_inputs(YOU_INPUTS).")
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print(
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"No json input required for current model type. "
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"You could call setup_inputs(YOU_INPUTS)."
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)
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return self.inputs
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def load_mlir_model(self):
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workdir = os.path.join(os.path.dirname(__file__), self.local_tank_dir)
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os.makedirs(workdir, exist_ok=True)
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print(f"TMP_MODEL_DIR = {workdir}")
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# use model name get dir.
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model_name_dir = os.path.join(workdir, str(self.model_name))
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if not os.path.exists(model_name_dir):
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print(
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"Model has not been download."
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"shark_downloader will automatically download by tank_url if provided."
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" You can also manually to download the model from shark_tank by yourself."
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)
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os.makedirs(model_name_dir, exist_ok=True)
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print(f"TMP_MODELNAME_DIR = {model_name_dir}")
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if self.model_type in ["tflite-tosa"]:
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self.mlir_url = self.tank_url + "/" + str(self.model_name) + "/" + str(self.model_name) + "_tflite.mlir"
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self.mlir_file = "/".join([model_name_dir, str(self.model_name) + "_tfite.mlir"])
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self.mlir_url = (
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self.tank_url
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+ "/"
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+ str(self.model_name)
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+ "/"
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+ str(self.model_name)
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+ "_tflite.mlir"
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)
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self.mlir_file = "/".join(
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[self.model_name_dir, str(self.model_name) + "_tfite.mlir"]
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)
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elif self.model_type in ["tensorflow"]:
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self.mlir_url = self.tank_url + "/" + str(self.model_name) + "/" + str(self.model_name) + "_tf.mlir"
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self.mlir_file = "/".join([model_name_dir, str(self.model_name) + "_tf.mlir"])
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self.mlir_url = (
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self.tank_url
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+ "/"
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+ str(self.model_name)
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+ "/"
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+ str(self.model_name)
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+ "_tf.mlir"
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)
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self.mlir_file = "/".join(
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[self.model_name_dir, str(self.model_name) + "_tf.mlir"]
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)
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elif self.model_type in ["torch", "jax", "mhlo", "tosa"]:
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self.mlir_url = (
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self.tank_url + "/" + str(self.model_name) + "/" + str(self.model_name) + "_" + str(self.model_type) + ".mlir"
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)
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self.mlir_file = "/".join(
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[
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model_name_dir,
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self.model_name_dir,
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str(self.model_name) + "_" + str(self.model_type) + ".mlir",
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]
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)
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@@ -21,7 +21,7 @@ class AlbertTfliteModuleTester:
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def create_and_check_module(self):
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shark_args.save_mlir = self.save_mlir
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shark_args.save_vmfb = self.save_vmfb
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self.shark_downloader = SharkDownloader(
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shark_downloader = SharkDownloader(
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model_name="albert_lite_base",
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tank_url="https://storage.googleapis.com/shark_tank",
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local_tank_dir="./../gen_shark_tank",
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@@ -29,18 +29,17 @@ class AlbertTfliteModuleTester:
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input_json="input.json",
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input_type="int32",
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)
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tflite_tosa_model = self.shark_downloader.get_mlir_file()
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inputs = self.shark_downloader.get_inputs()
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self.shark_module = SharkInference(
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tflite_tosa_model,
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inputs,
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tflite_tosa_model = shark_downloader.get_mlir_file()
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inputs = shark_downloader.get_inputs()
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shark_module = SharkInference(
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mlir_module=tflite_tosa_model,
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function_name="main",
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device=self.device,
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dynamic=self.dynamic,
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jit_trace=True,
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mlir_dialect="tflite",
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)
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self.shark_module.set_frontend("tflite-tosa")
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self.shark_module.compile()
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self.shark_module.forward(inputs)
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shark_module.compile()
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shark_module.forward(inputs)
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# print(shark_results)
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@@ -61,9 +60,9 @@ class AlbertTfliteModuleTest(unittest.TestCase):
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if __name__ == "__main__":
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unittest.main()
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# module_tester = AlbertTfliteModuleTester()
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# module_tester.create_and_check_module()
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# unittest.main()
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module_tester = AlbertTfliteModuleTester()
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module_tester.create_and_check_module()
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# TEST RESULT:
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# (shark.venv) nod% python albert_lite_base_tflite_mlir_test.py
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@@ -1 +0,0 @@
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