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.