BFloat16 is not supported on MPS

[lawyinking]

I got this at the step: goals = lida.goals(summary, n=5, persona="sales")

full error message: `--------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[43], line 2 1 # goals = lida.goals(summary, n=2, textgen_config=textgen_config) ----> 2 goals = lida.goals(summary, n=5, persona="sales")

File /opt/homebrew/lib/python3.11/site-packages/lida/components/manager.py:177, in Manager.goals(self, summary, textgen_config, n, persona) 174 if isinstance(persona, str): 175 persona = Persona(persona=persona, rationale="") --> 177 return self.goal.generate(summary=summary, text_gen=self.text_gen, 178 textgen_config=textgen_config, n=n, persona=persona)

File /opt/homebrew/lib/python3.11/site-packages/lida/components/goal.py:51, in GoalExplorer.generate(self, summary, textgen_config, text_gen, n, persona) 43 user_prompt += f"""\n The generated goals SHOULD BE FOCUSED ON THE INTERESTS AND PERSPECTIVE of a '{persona.persona} persona, who is insterested in complex, insightful goals about the data. \n""" 45 messages = [ 46 {"role": "system", "content": SYSTEM_INSTRUCTIONS}, 47 {"role": "assistant", 48 "content": 49 f"{user_prompt}\n\n {FORMAT_INSTRUCTIONS} \n\n. The generated {n} goals are: \n "}] ---> 51 result: list[Goal] = text_gen.generate(messages=messages, config=textgen_config) 53 try: 54 json_string = clean_code_snippet(result.text[0]["content"])

File /opt/homebrew/lib/python3.11/site-packages/llmx/generators/text/hf_textgen.py:213, in HFTextGenerator.generate(self, messages, config, kwargs) 200 gen_config = GenerationConfig( 201 max_new_tokens=max_new_tokens, 202 temperature=max(config.temperature, 0.01), (...) 210 repetition_penalty=repetition_penalty, 211 ) 212 with torch.no_grad(): --> 213 generated_ids = self.model.generate(batch, generation_config=gen_config) 215 text_response = self.tokenizer.batch_decode( 216 generated_ids, skip_special_tokens=False 217 ) 219 # print(text_response, "*****")

File /opt/homebrew/lib/python3.11/site-packages/torch/utils/_contextlib.py:115, in context_decorator..decorate_context(*args, kwargs) 112 @functools.wraps(func) 113 def decorate_context(*args, *kwargs): 114 with ctx_factory(): --> 115 return func(args, kwargs)

File /opt/homebrew/lib/python3.11/site-packages/transformers/generation/utils.py:1764, in GenerationMixin.generate(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, negative_prompt_ids, negative_prompt_attention_mask, kwargs) 1756 input_ids, model_kwargs = self._expand_inputs_for_generation( 1757 input_ids=input_ids, 1758 expand_size=generation_config.num_return_sequences, 1759 is_encoder_decoder=self.config.is_encoder_decoder, 1760 model_kwargs, 1761 ) 1763 # 13. run sample -> 1764 return self.sample( 1765 input_ids, 1766 logits_processor=logits_processor, 1767 logits_warper=logits_warper, 1768 stopping_criteria=stopping_criteria, 1769 pad_token_id=generation_config.pad_token_id, 1770 eos_token_id=generation_config.eos_token_id, 1771 output_scores=generation_config.output_scores, 1772 return_dict_in_generate=generation_config.return_dict_in_generate, 1773 synced_gpus=synced_gpus, 1774 streamer=streamer, 1775 **model_kwargs, 1776 ) 1778 elif generation_mode == GenerationMode.BEAM_SEARCH: 1779 # 11. prepare beam search scorer 1780 beam_scorer = BeamSearchScorer( 1781 batch_size=batch_size, 1782 num_beams=generation_config.num_beams, (...) 1787 max_length=generation_config.max_length, 1788 )

File /opt/homebrew/lib/python3.11/site-packages/transformers/generation/utils.py:2861, in GenerationMixin.sample(self, input_ids, logits_processor, stopping_criteria, logits_warper, max_length, pad_token_id, eos_token_id, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, synced_gpus, streamer, model_kwargs) 2858 model_inputs = self.prepare_inputs_for_generation(input_ids, model_kwargs) 2860 # forward pass to get next token -> 2861 outputs = self( 2862 **model_inputs, 2863 return_dict=True, 2864 output_attentions=output_attentions, 2865 output_hidden_states=output_hidden_states, 2866 ) 2868 if synced_gpus and this_peer_finished: 2869 continue # don't waste resources running the code we don't need

File /opt/homebrew/lib/python3.11/site-packages/torch/nn/modules/module.py:1518, in Module._wrapped_call_impl(self, *args, kwargs) 1516 return self._compiled_call_impl(*args, *kwargs) # type: ignore[misc] 1517 else: -> 1518 return self._call_impl(args, kwargs)

File /opt/homebrew/lib/python3.11/site-packages/torch/nn/modules/module.py:1527, in Module._call_impl(self, *args, *kwargs) 1522 # If we don't have any hooks, we want to skip the rest of the logic in 1523 # this function, and just call forward. 1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks 1525 or _global_backward_pre_hooks or _global_backward_hooks 1526 or _global_forward_hooks or _global_forward_pre_hooks): -> 1527 return forward_call(args, **kwargs) 1529 try: 1530 result = None

File /opt/homebrew/lib/python3.11/site-packages/accelerate/hooks.py:165, in add_hook_to_module..new_forward(module, *args, kwargs) 163 output = module._old_forward(*args, *kwargs) 164 else: --> 165 output = module._old_forward(args, kwargs) 166 return module._hf_hook.post_forward(module, output)

File /opt/homebrew/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py:1181, in LlamaForCausalLM.forward(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict) 1178 return_dict = return_dict if return_dict is not None else self.config.use_return_dict 1180 # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) -> 1181 outputs = self.model( 1182 input_ids=input_ids, 1183 attention_mask=attention_mask, 1184 position_ids=position_ids, 1185 past_key_values=past_key_values, 1186 inputs_embeds=inputs_embeds, 1187 use_cache=use_cache, 1188 output_attentions=output_attentions, 1189 output_hidden_states=output_hidden_states, 1190 return_dict=return_dict, 1191 ) 1193 hidden_states = outputs[0] 1194 if self.config.pretraining_tp > 1:

File /opt/homebrew/lib/python3.11/site-packages/torch/nn/modules/module.py:1518, in Module._wrapped_call_impl(self, *args, kwargs) 1516 return self._compiled_call_impl(*args, *kwargs) # type: ignore[misc] 1517 else: -> 1518 return self._call_impl(args, kwargs)

File /opt/homebrew/lib/python3.11/site-packages/torch/nn/modules/module.py:1527, in Module._call_impl(self, *args, *kwargs) 1522 # If we don't have any hooks, we want to skip the rest of the logic in 1523 # this function, and just call forward. 1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks 1525 or _global_backward_pre_hooks or _global_backward_hooks 1526 or _global_forward_hooks or _global_forward_pre_hooks): -> 1527 return forward_call(args, **kwargs) 1529 try: 1530 result = None

File /opt/homebrew/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py:1068, in LlamaModel.forward(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict) 1058 layer_outputs = self._gradient_checkpointing_func( 1059 decoder_layer.call, 1060 hidden_states, (...) 1065 use_cache, 1066 ) 1067 else: -> 1068 layer_outputs = decoder_layer( 1069 hidden_states, 1070 attention_mask=attention_mask, 1071 position_ids=position_ids, 1072 past_key_value=past_key_values, 1073 output_attentions=output_attentions, 1074 use_cache=use_cache, 1075 ) 1077 hidden_states = layer_outputs[0] 1079 if use_cache:

File /opt/homebrew/lib/python3.11/site-packages/torch/nn/modules/module.py:1518, in Module._wrapped_call_impl(self, *args, kwargs) 1516 return self._compiled_call_impl(*args, *kwargs) # type: ignore[misc] 1517 else: -> 1518 return self._call_impl(args, kwargs)

File /opt/homebrew/lib/python3.11/site-packages/torch/nn/modules/module.py:1527, in Module._call_impl(self, *args, *kwargs) 1522 # If we don't have any hooks, we want to skip the rest of the logic in 1523 # this function, and just call forward. 1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks 1525 or _global_backward_pre_hooks or _global_backward_hooks 1526 or _global_forward_hooks or _global_forward_pre_hooks): -> 1527 return forward_call(args, **kwargs) 1529 try: 1530 result = None

File /opt/homebrew/lib/python3.11/site-packages/accelerate/hooks.py:165, in add_hook_to_module..new_forward(module, *args, kwargs) 163 output = module._old_forward(*args, *kwargs) 164 else: --> 165 output = module._old_forward(args, kwargs) 166 return module._hf_hook.post_forward(module, output)

File /opt/homebrew/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py:793, in LlamaDecoderLayer.forward(self, hidden_states, attention_mask, position_ids, past_key_value, output_attentions, use_cache, kwargs) 787 warnings.warn( 788 "Passing padding_mask is deprecated and will be removed in v4.37. Please make sure use attention_mask instead.`" 789 ) 791 residual = hidden_states --> 793 hidden_states = self.input_layernorm(hidden_states) 795 # Self Attention 796 hidden_states, self_attn_weights, present_key_value = self.self_attn( 797 hidden_states=hidden_states, 798 attention_mask=attention_mask, (...) 803 kwargs, 804 )

File /opt/homebrew/lib/python3.11/site-packages/torch/nn/modules/module.py:1518, in Module._wrapped_call_impl(self, *args, kwargs) 1516 return self._compiled_call_impl(*args, *kwargs) # type: ignore[misc] 1517 else: -> 1518 return self._call_impl(args, kwargs)

File /opt/homebrew/lib/python3.11/site-packages/torch/nn/modules/module.py:1527, in Module._call_impl(self, *args, *kwargs) 1522 # If we don't have any hooks, we want to skip the rest of the logic in 1523 # this function, and just call forward. 1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks 1525 or _global_backward_pre_hooks or _global_backward_hooks 1526 or _global_forward_hooks or _global_forward_pre_hooks): -> 1527 return forward_call(args, **kwargs) 1529 try: 1530 result = None

File /opt/homebrew/lib/python3.11/site-packages/accelerate/hooks.py:160, in add_hook_to_module..new_forward(module, *args, kwargs) 159 def new_forward(module, *args, *kwargs): --> 160 args, kwargs = module._hf_hook.pre_forward(module, args, kwargs) 161 if module._hf_hook.no_grad: 162 with torch.no_grad():

File /opt/homebrew/lib/python3.11/site-packages/accelerate/hooks.py:294, in AlignDevicesHook.pre_forward(self, module, *args, **kwargs) 291 if self.weights_map[name].dtype == torch.int8: 292 fp16_statistics = self.weights_map[name.replace("weight", "SCB")] 293 set_module_tensor_to_device( --> 294 module, name, self.execution_device, value=self.weights_map[name], fp16_statistics=fp16_statistics 295 ) 297 return send_to_device(args, self.execution_device), send_to_device( 298 kwargs, self.execution_device, skip_keys=self.skip_keys 299 )

File /opt/homebrew/lib/python3.11/site-packages/accelerate/utils/offload.py:118, in PrefixedDataset.getitem(self, key) 117 def getitem(self, key): --> 118 return self.dataset[f"{self.prefix}{key}"]

File /opt/homebrew/lib/python3.11/site-packages/accelerate/utils/offload.py:169, in OffloadedWeightsLoader.getitem(self, key) 167 device = "cpu" if self.device is None else self.device 168 with safe_open(weight_info["safetensors_file"], framework="pt", device=device) as f: --> 169 tensor = f.get_tensor(weight_info.get("weight_name", key)) 171 if "dtype" in weight_info: 172 return tensor.to(getattr(torch, weight_info["dtype"]))

TypeError: BFloat16 is not supported on MPS`

when I use: goals = lida.goals(summary, n=2, textgen_config=textgen_config)

also getting this error.

Many Thanks!

[lawyinking]

I solved this problem by modifying the accelerate package following this link: https://github.com/huggingface/accelerate/pull/2227/files also found from this link: https://github.com/huggingface/accelerate/issues/2226

hope it helps but another problem appeared:

my code: charts = lida.visualize(summary=summary, goal=goals, textgen_config=textgen_config, library=library)

Error messages: `--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[12], line 1 ----> 1 charts = lida.visualize(summary=summary, goal=goals, textgen_config=textgen_config, library=library)

File /opt/homebrew/lib/python3.11/site-packages/lida/components/manager.py:202, in Manager.visualize(self, summary, goal, textgen_config, library, return_error) 199 goal = Goal(question=goal, visualization=goal, rationale="") 201 self.check_textgen(config=textgen_config) --> 202 code_specs = self.vizgen.generate( 203 summary=summary, goal=goal, textgen_config=textgen_config, text_gen=self.text_gen, 204 library=library) 205 charts = self.execute( 206 code_specs=code_specs, 207 data=self.data, (...) 210 return_error=return_error, 211 ) 212 return charts

File /opt/homebrew/lib/python3.11/site-packages/lida/components/viz/vizgenerator.py:27, in VizGenerator.generate(self, summary, goal, textgen_config, text_gen, library) 23 def generate(self, summary: Dict, goal: Goal, 24 textgen_config: TextGenerationConfig, text_gen: TextGenerator, library='altair'): 25 """Generate visualization code given a summary and a goal""" ---> 27 library_template, library_instructions = self.scaffold.get_template(goal, library) 28 messages = [ 29 {"role": "system", "content": system_prompt}, 30 {"role": "system", "content": f"The dataset summary is : {summary} \n\n"}, (...) 33 "content": 34 f"Always add a legend with various colors where appropriate. The visualization code MUST only use data fields that exist in the dataset (field_names) or fields that are transformations based on existing field_names). Only use variables that have been defined in the code or are in the dataset summary. You MUST return a FULL PYTHON PROGRAM ENCLOSED IN BACKTICKS ``` that starts with an import statement. DO NOT add any explanation. \n\n THE GENERATED CODE SOLUTION SHOULD BE CREATED BY MODIFYING THE SPECIFIED PARTS OF THE TEMPLATE BELOW \n\n {library_template} \n\n.The FINAL COMPLETED CODE BASED ON THE TEMPLATE above is ... \n\n"}] 36 completions: TextGenerationResponse = text_gen.generate( 37 messages=messages, config=textgen_config)

File /opt/homebrew/lib/python3.11/site-packages/lida/components/scaffold.py:21, in ChartScaffold.get_template(self, goal, library) 19 def get_template(self, goal: Goal, library: str): ---> 21 general_instructions = f"If the solution requires a single value (e.g. max, min, median, first, last etc), ALWAYS add a line (axvline or axhline) to the chart, ALWAYS with a legend containing the single value (formatted with 0.2F). If using a where semantic_type=date, YOU MUST APPLY the following transform before using that column i) convert date fields to date types using data[''] = pd.to_datetime(data[], errors='coerce'), ALWAYS use errors='coerce' ii) drop the rows with NaT values data = data[pd.notna(data[])] iii) convert field to right time format for plotting. ALWAYS make sure the x-axis labels are legible (e.g., rotate when needed). Solve the task carefully by completing ONLY the AND section. Given the dataset summary, the plot(data) method should generate a {library} chart ({goal.visualization}) that addresses this goal: {goal.question}. DO NOT WRITE ANY CODE TO LOAD THE DATA. The data is already loaded and available in the variable data." 23 matplotlib_instructions = f" {general_instructions} DO NOT include plt.show(). The plot method must return a matplotlib object (plt). Think step by step. \n" 25 if library == "matplotlib":

AttributeError: 'list' object has no attribute 'visualization'`

Still solving.