Add phi1 and phi4 models #1377
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Add phi1 and phi4 models #1377
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ashokkumarkannan1
requested changes
Mar 6, 2025
| module_name = build_module_name(variant, Source.HUGGINGFACE, Framework.PYTORCH, Task.CAUSAL_LM) | ||
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| # Record Forge Property | ||
| # record_forge_property("model_name", module_name) |
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| module_name = build_module_name(variant, Source.HUGGINGFACE, Framework.PYTORCH, Task.CAUSAL_LM) | ||
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| # Record Forge Property | ||
| # record_forge_property("model_name", module_name) |
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Please add below line to all priority models
record_forge_property("group", "priority")
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| # Record Forge Property | ||
| record_forge_property("model_name", module_name) |
| module_name = build_module_name(variant, Source.HUGGINGFACE, Framework.PYTORCH, Task.CAUSAL_LM) | ||
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| # Record Forge Property | ||
| # record_forge_property("model_name", module_name) |
| config_dict = config.to_dict() | ||
| config_dict["return_dict"] = False | ||
| config_dict["use_cache"] = False | ||
| # config_dict["resid_pdrop"] = 0.0 |
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| compiled_model = forge.compile(framework_model, sample_inputs, module_name) | ||
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| # Model Verification | ||
| # verify(sample_inputs, framework_model, compiled_model) |
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dsudhakarTT
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #1377 +/- ##
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Coverage 43.40% 43.40%
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Files 48 48
Lines 7860 7860
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Hits 3412 3412
Misses 4448 4448 ☔ View full report in Codecov by Sentry. |
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meenakshiramanathan1
requested changes
Mar 7, 2025
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| # PhiConfig from pretrained variant, disable return_dict and caching. | ||
| config = PhiConfig.from_pretrained(variant) | ||
| config_dict = config.to_dict() | ||
| config_dict["return_dict"] = False | ||
| config_dict["use_cache"] = False | ||
| config = PhiConfig(**config_dict) | ||
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| # Load tokenizer and model from HuggingFace | ||
| tokenizer = AutoTokenizer.from_pretrained(variant) | ||
| framework_model = PhiForCausalLM.from_pretrained(variant, config=config) | ||
| framework_model.eval() |
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Can you use download_model to load model and tokenizer so that we can get rid of the redundant code in each test?
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