Fix params.json for llama models

Summary: #6374

Reviewed By: helunwencser

Differential Revision: D64632923

Pulled By: mergennachin

fbshipit-source-id: 0347da2d51bb16e86c6b042bd2e63451f89b5c64

Author: mergennachin

examples/models/llama/llama_transformer.py

@@ -116,6 +116,9 @@ class ModelArgs:
     bos_count: int = -1  # i.e., a single EOS is used as BOS
     eos_count: int = 2
 
+    quantization_args: Optional[dict] = None
+    lora_args: Optional[dict] = None
+
     def __post_init__(self):
         if self.n_kv_heads is None:
             self.n_kv_heads = self.n_heads

examples/models/llama/model.py

@@ -165,7 +165,7 @@ def __init__(self, **kwargs):
             )
         elif hasattr(self.args, "use_spin_quant") and self.args.use_spin_quant:
             print("Using SPIN quantization.")
-            self._transform_for_pre_quantization(checkpoint)
+            self._transform_for_pre_quantization(checkpoint, model_args)
 
             from .source_transformation.pre_quantization import (
                 sanitize_checkpoint_from_pre_quantization,
@@ -174,8 +174,9 @@ def __init__(self, **kwargs):
             sanitize_checkpoint_from_pre_quantization(checkpoint)
         elif hasattr(self.args, "use_qat") and self.args.use_qat:
             print("Using QAT quantization.")
-            self._transform_for_pre_quantization(checkpoint)
+            self._transform_for_pre_quantization(checkpoint, model_args)
             if hasattr(self.args, "use_lora") and self.args.use_lora:
+                assert model_args.lora_args["rank"] == self.args.use_lora
                 from .source_transformation.lora import (
                     transform_linear_for_lora_after_quantization,
                 )
@@ -251,7 +252,7 @@ def get_example_inputs_kvcache_sdpa(self):
                 ),  # start_pos, what token of output are we on.
             )
 
-    def _transform_for_pre_quantization(self, checkpoint):
+    def _transform_for_pre_quantization(self, checkpoint, model_args):
         assert hasattr(self.args, "preq_mode"), "preq_mode must be specified"
         assert self.args.preq_mode in [
             "8da4w",
@@ -265,6 +266,8 @@ def _transform_for_pre_quantization(self, checkpoint):
             transform_linear_for_pre_quantization,
         )
 
+        assert self.args.preq_group_size == model_args.quantization_args["group_size"]
+
         mapping = {
             "fp32": torch.float32,
             "fp16": torch.float16,