Update whisper transformer module to 4.48.0 #24382
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| @@ -7,6 +7,7 @@ | |||
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| import numpy as np | |||
| import torch | |||
| import transformers | |||
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Module is imported with 'import' and 'import from' Note
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AI 4 days ago
The best way to fix the problem is to remove the from transformers import AutoConfig, AutoTokenizer statement and use the transformers.AutoConfig and transformers.AutoTokenizer directly in the code. This approach maintains the existing functionality while eliminating the confusion caused by the dual import.
- Remove the
from transformers import AutoConfig, AutoTokenizerstatement. - Replace all instances of
AutoConfigandAutoTokenizerwithtransformers.AutoConfigandtransformers.AutoTokenizer, respectively.
| @@ -10,3 +10,2 @@ | ||
| import transformers | ||
| from transformers import AutoConfig, AutoTokenizer | ||
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| @@ -32,3 +31,3 @@ | ||
| def get_sample_inputs( | ||
| config: AutoConfig, | ||
| config: transformers.AutoConfig, | ||
| device: torch.device, | ||
| @@ -67,3 +66,3 @@ | ||
| def get_sample_with_past_kv_inputs( | ||
| config: AutoConfig, | ||
| config: transformers.AutoConfig, | ||
| device: torch.device, |
| import torch | ||
| import transformers |
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AI 4 days ago
To fix the problem, we should remove the from transformers import AutoConfig statement and use transformers.AutoConfig instead. This will ensure that the transformers module is only imported once, reducing confusion and potential namespace conflicts.
- Remove the
from transformers import AutoConfigstatement. - Replace all instances of
AutoConfigwithtransformers.AutoConfig.
| @@ -28,3 +28,3 @@ | ||
| from models.torch_export_patches.cache_helper import make_dynamic_cache | ||
| from transformers import AutoConfig | ||
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| @@ -35,3 +35,3 @@ | ||
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| def get_sequence_lengths(args: argparse.Namespace, config: AutoConfig): | ||
| def get_sequence_lengths(args: argparse.Namespace, config: transformers.AutoConfig): | ||
| past_sequence_length, curr_sequence_length = (8, 1) if args.use_past_kv else (0, 8) | ||
| @@ -41,3 +41,3 @@ | ||
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| def get_inputs(args: argparse.Namespace, config: AutoConfig): | ||
| def get_inputs(args: argparse.Namespace, config: transformers.AutoConfig): | ||
| # Dummy values for parity | ||
| @@ -104,3 +104,3 @@ | ||
| pytorch_model: None | torch.nn.Module = None, | ||
| config: None | AutoConfig = None, | ||
| config: None | transformers.AutoConfig = None, | ||
| ): |
| def _catch_produce_guards_and_solve_constraints( | ||
| previous_function: Callable, | ||
| fake_mode: "FakeTensorMode", | ||
| gm: "torch.fx.GraphModule", | ||
| dynamic_shapes: dict[str, Any] | tuple[Any] | list[Any] | None, | ||
| equalities_inputs: "EqualityConstraint", # noqa: F821 | ||
| original_signature: inspect.Signature, | ||
| _is_torch_jit_trace: bool = False, | ||
| verbose: int = 0, | ||
| ): |
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To fix the problem, we need to add an explicit return statement at the end of the _catch_produce_guards_and_solve_constraints function. This will ensure that the function always returns a value, even when an exception is caught and the if conditions are not met. The explicit return statement should return None to maintain the existing functionality.
| @@ -43,3 +43,3 @@ | ||
| ) | ||
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| return None | ||
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| def patch__check_input_constraints_for_graph( | ||
| previous_function: Callable, | ||
| input_placeholders: list[torch.fx.Node], | ||
| flat_args_with_path, | ||
| range_constraints, | ||
| verbose: int = 0, | ||
| ) -> None: |
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To fix the problem, we need to add an explicit return statement at the end of the function patch__check_input_constraints_for_graph. This ensures that the function consistently returns a value, making the code easier to read and understand. The explicit return value should be None to maintain the existing functionality.
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| ) | ||
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| return None | ||
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| # if config.print_specializations: | ||
| # self.log.warning( | ||
| # "Specializing %s to %s", self.var_to_sources[a][0].name(), tgt |
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To fix the problem, we should remove the commented-out code. This will make the code cleaner and reduce potential confusion for future developers. If the logging statement is needed in the future, it can be reintroduced with proper documentation.
- Remove the commented-out logging statement on lines 304-308.
- Ensure that the removal does not affect the existing functionality of the code.
| @@ -303,7 +303,7 @@ | ||
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| # if config.print_specializations: | ||
| # self.log.warning( | ||
| # "Specializing %s to %s", self.var_to_sources[a][0].name(), tgt | ||
| # ) | ||
| # self.log.debug("SPECIALIZATION", stack_info=True) | ||
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| assert msg != "range_refined_to_singleton", ( |
| # if input_ids.shape[1] == 0: | ||
| # inputs_embeds = inputs_embeds[:, -cache_position.shape[0] :] | ||
| # else: | ||
| # if cache_position[-1] >= input_ids.shape[1]: | ||
| # input_ids = input_ids[:, -cache_position.shape[0] :] | ||
| # else: | ||
| # if input_ids.shape[1] != cache_position.shape[0]: | ||
| # input_ids = input_ids[:, cache_position] |
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To fix the problem, we should remove the commented-out code. This will make the code cleaner and less confusing for future developers. The removal should be done in the _cache_dependant_input_preparation_exporting method, specifically lines 280 to 288.
| @@ -279,11 +279,3 @@ | ||
| else: | ||
| # This is the code we need to implemented with torch.cond. | ||
| # if input_ids.shape[1] == 0: | ||
| # inputs_embeds = inputs_embeds[:, -cache_position.shape[0] :] | ||
| # else: | ||
| # if cache_position[-1] >= input_ids.shape[1]: | ||
| # input_ids = input_ids[:, -cache_position.shape[0] :] | ||
| # else: | ||
| # if input_ids.shape[1] != cache_position.shape[0]: | ||
| # input_ids = input_ids[:, cache_position] | ||
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| def branch_1(inputs_embeds, cache_position): |
Description
Motivation and Context
Branched off from #24291