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phuong-d-h-nguyen commited on Mar 1, 2024

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Dockerfile +5 -0
Salesforce-codegen2-1B-text-to-neurapy/added_tokens.json +945 -0
Salesforce-codegen2-1B-text-to-neurapy/config.json +49 -0
Salesforce-codegen2-1B-text-to-neurapy/generation_config.json +6 -0
Salesforce-codegen2-1B-text-to-neurapy/merges.txt +0 -0
Salesforce-codegen2-1B-text-to-neurapy/model.safetensors +3 -0
Salesforce-codegen2-1B-text-to-neurapy/special_tokens_map.json +6 -0
Salesforce-codegen2-1B-text-to-neurapy/tokenizer.json +0 -0
Salesforce-codegen2-1B-text-to-neurapy/tokenizer_config.json +0 -0
Salesforce-codegen2-1B-text-to-neurapy/training_args.bin +3 -0
Salesforce-codegen2-1B-text-to-neurapy/vocab.json +0 -0
finetuning.py +34 -0
finetuning_set_v1.0.json +1 -0
prepare_data_set.py +16 -0

Dockerfile ADDED Viewed

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+FROM python:3.9
+ADD finetuning.py .
+ADD finetuning_set_v1.0.json .
+RUN pip install trl transformers torch
+CMD ["python", "./finetuning.py"]

Salesforce-codegen2-1B-text-to-neurapy/added_tokens.json ADDED Viewed

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+{
+  "_name_or_path": "Salesforce/codegen2-1B",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "CodeGenForCausalLM"
+  ],
+  "attn_pdrop": 0.0,
+  "auto_map": {
+    "AutoConfig": "Salesforce/codegen2-1B--configuration_codegen.CodeGenConfig",
+    "AutoModel": "Salesforce/codegen2-1B--modeling_codegen.CodeGenModel",
+    "AutoModelForCausalLM": "Salesforce/codegen2-1B--modeling_codegen.CodeGenForCausalLM"
+  },
+  "bos_token_id": 1,
+  "embd_pdrop": 0.0,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "head_dim": 128,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "codegen",
+  "n_ctx": 2048,
+  "n_embd": 2048,
+  "n_head": 16,
+  "n_inner": null,
+  "n_layer": 16,
+  "n_positions": 2048,
+  "resid_pdrop": 0.0,
+  "rotary_dim": 64,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50,
+      "temperature": 1.0
+    }
+  },
+  "tie_word_embeddings": false,
+  "tokenizer_class": "GPT2Tokenizer",
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.36.0",
+  "trust_remote_code": true,
+  "use_cache": true,
+  "vocab_size": 51200
+}

Salesforce-codegen2-1B-text-to-neurapy/generation_config.json ADDED Viewed

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.36.0"
+}

Salesforce-codegen2-1B-text-to-neurapy/merges.txt ADDED Viewed

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Salesforce-codegen2-1B-text-to-neurapy/model.safetensors ADDED Viewed

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+version https://git-lfs.github.com/spec/v1
+oid sha256:211475d7246765b01297b815c5051f800beb338a69f76fcc3c7f25cea73c9ed1
+size 2030626696

Salesforce-codegen2-1B-text-to-neurapy/special_tokens_map.json ADDED Viewed

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+{
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "pad_token": "<|endoftext|>",
+  "unk_token": "<|endoftext|>"
+}

Salesforce-codegen2-1B-text-to-neurapy/tokenizer.json ADDED Viewed

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Salesforce-codegen2-1B-text-to-neurapy/tokenizer_config.json ADDED Viewed

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Salesforce-codegen2-1B-text-to-neurapy/training_args.bin ADDED Viewed

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+version https://git-lfs.github.com/spec/v1
+oid sha256:915dfafefeff57adab2c02d8df421e34d36811b373bf7f6884f2af1481345e89
+size 4728

Salesforce-codegen2-1B-text-to-neurapy/vocab.json ADDED Viewed

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finetuning.py ADDED Viewed

	@@ -0,0 +1,34 @@

+from trl import SFTTrainer
+from transformers import TrainingArguments
+import json
+import torch
+with open("finetuning_set_v1.0.json") as f:
+    dataset = json.load(f)
+torch.cuda.empty_cache()
+torch.cuda.set_per_process_memory_fraction(0.9)
+args = TrainingArguments(
+    per_device_train_batch_size=1,
+    per_device_eval_batch_size=1,
+    output_dir="Salesforce-codegen2-1B-text-to-neurapy",
+    gradient_accumulation_steps=1,
+    num_train_epochs=8
+)
+trainer = SFTTrainer(
+    "Salesforce/codegen2-1B",
+    train_dataset=dataset,
+    dataset_text_field="completion",
+    max_seq_length=1024,
+    args=args,
+    model_init_kwargs={
+        "torch_dtype": torch.bfloat16,
+    },
+    packing=True
+)
+trainer.train()
+# testing without saving uncomment for actual training
+# trainer.save_model("Salesforce-codegen2-1B-text-to-neurapy")

finetuning_set_v1.0.json ADDED Viewed

	@@ -0,0 +1 @@

+ [{"prompt": "compute the forward kinematics", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> target_joint_angles = [0.2]*r.dof\n> tcp_pose = r.compute_forward_kinematics(joint_angles)\n> "}, {"prompt": "disable the collision detection", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> r.disable_collision_detection()\n> "}, {"prompt": "enable the reflexes of the robot", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> r.enable_reflex()\n> "}, {"prompt": "plan a move joint", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> planned_motion_ids = r.plan_move_joint([0]*r.dof) # Replace with your planned motion IDs\n> r.executor([planned_motion_ids])\n> "}, {"prompt": "get the current cartesian pose", "completion": "\nfrom neurapy.robot import Robot\nr = Robot()\ncartesian_pose = r.get_current_cartesian_pose()\n"}, {"prompt": "get the current joint angles with the corresponding timestamp", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> joint_angles, timestamp = r.get_current_joint_angles_with_timestamp()\n> "}, {"prompt": "get the current joint velocities", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> joint_velocites = r.get_current_joint_velocities()\n> "}, {"prompt": "get the current load side encoder values with the corresponding timestamp", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> encoder_ticks, timestamp = r.get_current_load_side_encoder_values_with_timestamp()\n> "}, {"prompt": "get the center of gravity of the current tool", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> tool_cog = r.get_current_tool_cogs()\n> "}, {"prompt": "get the properties of the current tool", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> tool_properties = r.get_current_tool_properties()\n> "}, {"prompt": "get robot diagnostics", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> print(r.get_diagnostics())\n> "}, {"prompt": "function to retrieve docs for a function", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> description = r.get_doc(\"move_joint\")\n> print(description)\n> "}, {"prompt": "get the gravity vector", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> vector = r.get_gravity_vector()\n> print(vector)\n> "}, {"prompt": "get the override value", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> override_value = r.get_override()\n> print(f\"Current override value: {override_value}\")\n> "}, {"prompt": "get the reference frame", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> frame = r.get_reference_frame(\"tool_frame\")\n> print(frame)\n> "}, {"prompt": "get the current tcp pose", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> tcp_pose = r.get_tcp_pose()\n> print(tcp_pose)\n> "}, {"prompt": "get the tools digital input", "completion": "\n> import time\n> from neurapy.robot import Robot\n> r = Robot()\n> io_get = r.get_tool_digital_input(1)\n> print(io_get)\n> "}, {"prompt": "get the list of available tools", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> tools = r.get_tools()\n> print(tools)\n> "}, {"prompt": "grasp the object", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> r.grasp()\n> "}, {"prompt": "get the io values on the robot", "completion": "\n> import time\n> from neurapy.robot import Robot\n> r = Robot()\n> io_get = r.io(\"get\", io_name = \"DO_1\")\n> io_set = r.io(\"set\", io_name = \"DO_2\", target_value = True)\n> print(io_get,io_set)\n> "}, {"prompt": "check if the reflex is enabled", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> if r.is_reflex_enabled():\n> print(\"Reflex collision avoidance is enabled.\")\n> else:\n> print(\"Reflex collision avoidance is not enabled.\")\n> "}, {"prompt": "check if robot is in semi automatic mode", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> if r.is_robot_in_semi_automatic_mode():\n> print(\"Robot is in semi automatic mode\")\n> else:\n> print(\"Robot is not in semi automatic mode\")\n> "}, {"prompt": "jog the robot", "completion": "\n> import time\n> from neurapy.robot import Robot\n> r = Robot()\n\n> '''\n> jog_velocity - velocity ranging from [-1,1] for all joints. Signifies percentage of total speed used (1 = 100%)\n> jog_type - can be either cartesian or joint jogging\n> turn_on_jog sets the parameters of external jogging, jogging velocity, jogging type and sets the mode for jogging to external mode.\n> '''\n> r.turn_on_jog(jog_velocity=[0.2, 0.2, 0.2, 0.2, 0.2, 0.2], jog_type='Joint')\n> r.jog(set_jogging_external_flag=1)\n> i = 0\n> '''\n> Requires minimum number of cycles in the loop for performing jogging.\n> Depends upon jogging velocity, override.\n> '''\n> while i < 500:\n> #command to set flag to start jogging in external mode. This command has to be used each time external jog command has to be sent\n> r.jog(set_jogging_external_flag=1)\n> i += 1\n\n> #Command to switch back to internal jogging mode (GUI)\n> r.turn_off_jog()\n> "}, {"prompt": "perform a circular move", "completion": "\n> from neurapy.robot import Robot\n\n> r = Robot()\n> circular_property = {\n> \"speed\": 0.25,\n> \"acceleration\": 0.1,\n> \"target_pose\": [\n> [\n> 0.3744609827431085,\n> -0.3391784988266481,\n> 0.23276604279256016,\n> 3.14119553565979,\n> -0.00017731254047248513,\n> -0.48800110816955566\n> ],\n> [\n> 0.37116786741831503,\n> -0.19686307684994242,\n> 0.23300456855796453,\n> 3.141423225402832,\n> -0.00020668463548645377,\n> -0.48725831508636475\n> ],\n> [\n> 0.5190337951593321,\n> -0.1969996948428492,\n> 0.23267853691809767,\n> 3.1414194107055664,\n> -0.00017726201622281224,\n> -0.48750609159469604\n> ]\n> ],\n> \"current_joint_angles\": r.robot_status(\"jointAngles\")\n> }\n> r.move_circular(**circular_property)\n> r.stop() # if there are multiple motions than,this needs to be called only once at the end of the script\n> "}, {"prompt": "perform a linear move", "completion": "\n> from neurapy.robot import Robot\n\n> r = Robot()\n> linear_property = {\n> \"speed\": 0.25,\n> \"acceleration\": 0.1,\n> \"blend_radius\": 0.005,\n> \"target_pose\": [\n> [\n> 0.3287228886,\n> -0.1903355329,\n> 0.4220780352,\n> 0.08535207028439847,\n> -2.797181496822229,\n> 2.4713321627410485\n> ],\n> [\n> 0.2093363791501374,\n> -0.31711250784165884,\n> 0.422149168855134,\n> -3.0565555095672607,\n> -0.3447442352771759,\n> -1.1323236227035522\n> ],\n> [\n> 0.2090521916195534,\n> -0.5246753336643587,\n> 0.4218773613553828,\n> -3.0569007396698,\n> -0.3448921740055084,\n> -1.1323626041412354\n> ],\n> [\n> 0.3287228886,\n> -0.1903355329,\n> 0.4220780352,\n> 0.08535207028439847,\n> -2.797181496822229,\n> 2.4713321627410485\n> ]\n> ],\n> \"current_joint_angles\": r.robot_status(\"jointAngles\")\n> }\n> r.move_linear(**linear_property)\n> r.stop() # if there are multiple motions than,this needs to be called only once at the end of the script\n> "}, {"prompt": "send an error message", "completion": "\nfrom neurapy.robot import Robot\nr = Robot()\nif r.notify_error(\"Something went wrong!\"):\n print(\"Error message sent successfully.\")\nelse:\n print(\"Failed to send error message.\")\n"}, {"prompt": "pause the robot", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> r.pause()\n> "}, {"prompt": "plan a composite move", "completion": "\n> from neurapy.robot import Robot\n\n> r = Robot()\n> composite_motion_property = {\n> \"speed\": 0.432,\n> \"acceleration\": 0.2,\n> \"current_joint_angles\": r.robot_status('jointAngles'),\n> \"commands\": [\n> {\n> \"linear\": {\n> \"blend_radius\": 0.005,\n> \"targets\": [\n> [\n> -0.000259845199876027,\n> -0.5211437049195536,\n> 0.4429382717719519,\n> 3.14123272895813,\n> -0.0007908568368293345,\n> -1.570908784866333\n> ],\n> [\n> -0.16633498440272945,\n> -0.5201452059140722,\n> 0.4427486025872017,\n> 3.140937089920044,\n> -0.0005319403717294335,\n> -1.571555495262146\n> ]\n> ]\n> }\n> },\n> {\n> \"circular\": {\n> \"targets\": [\n> [\n> -0.16633498440272945,\n> -0.5201452059140722,\n> 0.4427486025872017,\n> 3.140937089920044,\n> -0.0005319403717294335,\n> -1.571555495262146\n> ],\n> [\n> -0.16540090985202305,\n> -0.3983552679378624,\n> 0.44267608017426174,\n> 3.1407113075256348,\n> -0.00036628879024647176,\n> -1.5714884996414185\n> ],\n> [\n> -0.33446498807559716,\n> -0.3989652352814891,\n> 0.4421152856242009,\n> 3.1402060985565186,\n> 0.00030071483342908323,\n> -1.572899580001831\n> ]\n> ]\n> }\n> }\n> ],\n> \"store_id\":234,\n> \"current_joint_angles\": r.robot_status(\"jointAngles\")\n> }\n> plan_id= r.plan_move_composite(**composite_motion_property)\n> execute = r.executor([plan_id])\n> "}, {"prompt": "power on", "completion": "\n from time import sleep\n from neurapy.robot import Robot\n\n r = Robot()\n r.power('on')\n sleep(2)\n r.power('off')\n "}, {"prompt": "get the program status", "completion": "\n> from neurapy.robot import Robot\n\n> r = Robot()\n> print(r.program_status())\n> "}, {"prompt": "release the object", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> r.release()\n> "}, {"prompt": "reset errors", "completion": "\n> import time\n> from neurapy.robot import Robot\n> r = Robot()\n> r.reset_error()\n> "}, {"prompt": "calculate rpy from quaternion representation", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> rpy = r.quaternion_to_rpy(0.85,0,0.52,0)\n> "}, {"prompt": "set the encoder offsets", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> r.set_encoder_offsets([#encoder offsets from the provided robot manual/data sheet])\n> "}, {"prompt": "send a message to opcua", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> message = \"Hello, OPC UA Server!\"\n> register_index = 1 # Replace with the index of the target OPC UA register\n> if r.set_opcua_msg(message, register_index):\n> print(\"Message sent and stored in OPC UA register successfully.\")\n> else:\n> print(\"Failed to send the message or store it in the OPC UA register.\")\n> "}, {"prompt": "set the tool", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> r.set_tool(tool_name=\"name given during tool creation in GUI\")\n> "}, {"prompt": "switch to automatic mode", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> if r.switch_to_automatic_mode():\n> print(\"Robot switched to Automatic mode.\")\n> else:\n> print(\"Failed to switch to Automatic mode.\")\n> "}, {"prompt": "switch to simulation", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> r.switch_to_simulation()\n> "}, {"prompt": "turn jogging on", "completion": "\n> import time\n> from neurapy.robot import Robot\n> r = Robot()\n> r.turn_on_jog(jog_velocity=[0.2, 0.2, 0.2, 0.2, 0.2, 0.2], jog_type='Joint')\n> r.jog(set_jogging_external_flag=1)\n> i = 0\n> while i < 500:\n> r.jog(set_jogging_external_flag=1)\n> i += 1\n> r.turn_off_jog()\n> "}, {"prompt": "unpause", "completion": "\n> from neurapy.robot import Robot\n> r = Robot()\n> r.unpause()\n> "}, {"prompt": "wait for a digital input", "completion": "\n> import time\n> from neurapy.robot import Robot\n> r = Robot()\n> io_wait = r.wait_for_digital_input(1,True) #waits till digital input 1 is True\n> print(io_wait)\n> "}, {"prompt": "wait for analog input", "completion": "\n> import time\n> from neurapy.robot import Robot\n> r = Robot()\n> io_wait = r.wait_for_tool_analog_input(1,2.3v) #waits till tool analog input 1 is 2.3v\n> print(io_wait)\n> "}, {"prompt": "delayed wait for tool digital input", "completion": "\n> import time\n> from neurapy.robot import Robot\n> r = Robot()\n> io_wait = r.wait_for_tool_digital_input_timer_on_delay(1,10) #waits for 10sec and returns after tool digital input 1 reaches high\n> print(io_wait)\n> "}]

prepare_data_set.py ADDED Viewed

	@@ -0,0 +1,16 @@

+import json
+def create_instruction(sample):
+    return {
+        "prompt": sample[0],
+        "completion": sample[1]
+    }
+# load dataset
+with open("../evaluation_kit/dataset/set_v1.0.json") as f:
+    dataset = json.load(f)
+dataset = list(map(create_instruction, dataset))
+with open("finetuning_set_v1.0.json", "w") as f:
+    json.dump(dataset, f)