Move get_quantizer_and_quant_params to quantizer_lib (#11056)

Summary:

Move get_quantizer_and_quant_params to quantizer_lib to extensions/ so that it's easier to create recipes

Reviewed By: larryliu0820

Differential Revision: D75179679

Author: tarun292

examples/models/llama/eval_llama_lib.py

@@ -12,11 +12,9 @@
 import torch
 
 from datasets import load_dataset
-from executorch.examples.models.llama.export_llama_lib import (
-    get_quantizer_and_quant_params,
-)
 
 from executorch.extension.llm.export.builder import LLMEdgeManager
+from executorch.extension.llm.export.quantizer_lib import get_quantizer_and_quant_params
 from lm_eval.evaluator import simple_evaluate
 from pytorch_tokenizers import get_tokenizer
 from pytorch_tokenizers.llama2c import Llama2cTokenizer as SentencePieceTokenizer

examples/models/llama/export_llama_lib.py

@@ -43,13 +43,7 @@
     get_xnnpack_partitioner,
 )
 
-from executorch.extension.llm.export.quantizer_lib import (
-    get_coreml_quantizer,
-    get_pt2e_quantization_params,
-    get_pt2e_quantizers,
-    get_qnn_quantizer,
-    get_vulkan_quantizer,
-)
+from executorch.extension.llm.export.quantizer_lib import get_quantizer_and_quant_params
 from executorch.util.activation_memory_profiler import generate_memory_trace
 
 from ..model_factory import EagerModelFactory
@@ -726,32 +720,6 @@ def _prepare_for_llama_export(args) -> LLMEdgeManager:
     return edge_manager
 
 
-def get_quantizer_and_quant_params(args):
-    pt2e_quant_params = get_pt2e_quantization_params(
-        args.pt2e_quantize, args.quantization_mode
-    )
-    quantizers = get_pt2e_quantizers(pt2e_quant_params, args.so_library)
-    quant_dtype = None
-    if args.qnn and args.pt2e_quantize:
-        assert len(quantizers) == 0, "Should not enable both xnnpack and qnn"
-        qnn_quantizer, quant_dtype = get_qnn_quantizer(
-            args.pt2e_quantize, args.quantization_mode
-        )
-        quantizers.append(qnn_quantizer)
-    if args.coreml and args.pt2e_quantize:
-        assert len(quantizers) == 0, "Should not enable both xnnpack / qnn and coreml"
-        coreml_quantizer = get_coreml_quantizer(args.pt2e_quantize)
-        quantizers.append(coreml_quantizer)
-    if args.vulkan and args.pt2e_quantize:
-        assert (
-            len(quantizers) == 0
-        ), "Should not enable both vulkan and other quantizers"
-        vulkan_quantizer = get_vulkan_quantizer(args.pt2e_quantize)
-        quantizers.append(vulkan_quantizer)
-    logging.info(f"Applying quantizers: {quantizers}")
-    return pt2e_quant_params, quantizers, quant_dtype
-
-
 def _qmode_type(value):
     choices = ["int8", "8da4w", "8da4w-gptq", "vulkan_4w"]
     patterns = [r"torchao:8da(\d+)w", r"torchao:fpa(\d+)w"]

examples/models/llava/export_llava.py

@@ -16,10 +16,7 @@
     get_symmetric_quantization_config,
     XNNPACKQuantizer,
 )
-from executorch.examples.models.llama.export_llama_lib import (
-    build_args_parser,
-    get_quantizer_and_quant_params,
-)
+from executorch.examples.models.llama.export_llama_lib import build_args_parser
 from executorch.examples.models.llama.source_transformation.custom_kv_cache import (
     replace_kv_cache_with_custom_kv_cache,
 )
@@ -44,6 +41,7 @@
     HintBasedSymShapeEvalPass,
 )
 from executorch.extension.llm.export.builder import DType, LLMEdgeManager
+from executorch.extension.llm.export.quantizer_lib import get_quantizer_and_quant_params
 from executorch.util.activation_memory_profiler import generate_memory_trace
 from pytorch_tokenizers.llama2c import Llama2cTokenizer as Tokenizer
 from torch.export import Dim

extension/llm/export/quantizer_lib.py

@@ -277,3 +277,29 @@ def get_vulkan_quantizer(pt2e_quantize: str):
 
     quantizer = VulkanQuantizer().set_global(config)
     return quantizer
+
+
+def get_quantizer_and_quant_params(args):
+    pt2e_quant_params = get_pt2e_quantization_params(
+        args.pt2e_quantize, args.quantization_mode
+    )
+    quantizers = get_pt2e_quantizers(pt2e_quant_params, args.so_library)
+    quant_dtype = None
+    if args.qnn and args.pt2e_quantize:
+        assert len(quantizers) == 0, "Should not enable both xnnpack and qnn"
+        qnn_quantizer, quant_dtype = get_qnn_quantizer(
+            args.pt2e_quantize, args.quantization_mode
+        )
+        quantizers.append(qnn_quantizer)
+    if args.coreml and args.pt2e_quantize:
+        assert len(quantizers) == 0, "Should not enable both xnnpack / qnn and coreml"
+        coreml_quantizer = get_coreml_quantizer(args.pt2e_quantize)
+        quantizers.append(coreml_quantizer)
+    if args.vulkan and args.pt2e_quantize:
+        assert (
+            len(quantizers) == 0
+        ), "Should not enable both vulkan and other quantizers"
+        vulkan_quantizer = get_vulkan_quantizer(args.pt2e_quantize)
+        quantizers.append(vulkan_quantizer)
+    logging.info(f"Applying quantizers: {quantizers}")
+    return pt2e_quant_params, quantizers, quant_dtype