Adding numerical tests for Int8DynamicActivationIntxWeightConfig (#2065)

* init

* up

* up

* up

* up

Author: metascroy

torchao/experimental/tests/test_embedding_xbit_quantizer.py

@@ -9,6 +9,7 @@
 import unittest
 
 import torch
+from parameterized import param, parameterized
 from torch.testing import FileCheck
 
 from torchao.dtypes import (
@@ -19,8 +20,15 @@
     SharedEmbeddingQuantizer,
 )
 from torchao.quantization.granularity import PerAxis, PerGroup
+from torchao.quantization.qat import (
+    FakeQuantizeConfig,
+    FromIntXQuantizationAwareTrainingConfig,
+    Int4WeightOnlyEmbeddingQATQuantizer,
+    IntXQuantizationAwareTrainingConfig,
+)
 from torchao.quantization.quant_api import (
     Int8DynamicActivationIntxWeightConfig,
+    IntxWeightOnlyConfig,
     MappingType,
     quantize_,
 )
@@ -184,6 +192,184 @@ def test_shared_embedding(self):
                 exported_program.graph_module.code
             )
 
+    @parameterized.expand(
+        [
+            param(
+                weight_dtype=weight_dtype,
+                granularity=granularity,
+                mapping_type=mapping_type,
+                model_dtype=model_dtype,
+            )
+            for weight_dtype in [getattr(torch, f"int{x}") for x in range(1, 9)]
+            for granularity in [PerGroup(32), PerGroup(128), PerAxis(0)]
+            for mapping_type in [MappingType.SYMMETRIC, MappingType.ASYMMETRIC]
+            for model_dtype in [torch.float32]
+        ],
+        name_func=lambda f, _, params: f.__name__ + f"_{params.kwargs}",
+    )
+    def test_identical_to_IntxWeightOnlyConfig(
+        self, weight_dtype, granularity, mapping_type, model_dtype
+    ):
+        embedding_dim = 4096
+        num_embeddings = 131
+        model = torch.nn.Sequential(
+            *[torch.nn.Embedding(num_embeddings, embedding_dim)]
+        )
+        model = model.to(model_dtype)
+        indices = torch.randint(0, num_embeddings, (7,), dtype=torch.int32)
+
+        quantized_model = copy.deepcopy(model)
+        quantizer = EmbeddingQuantizer(
+            weight_dtype=weight_dtype,
+            granularity=granularity,
+            mapping_type=mapping_type,
+        )
+        quantized_model = quantizer.quantize(quantized_model)
+        actual_result = quantized_model(indices)
+
+        reference_model = copy.deepcopy(model)
+        quantize_(
+            reference_model,
+            IntxWeightOnlyConfig(
+                weight_dtype=weight_dtype,
+                granularity=granularity,
+                mapping_type=mapping_type,
+                scale_dtype=None,
+            ),
+            lambda m, fqn: isinstance(m, torch.nn.Embedding),
+        )
+        expected_result = reference_model(indices)
+        self.assertTrue(torch.allclose(actual_result, expected_result))
+
+    @parameterized.expand(
+        [
+            param(
+                weight_dtype=weight_dtype,
+                granularity=granularity,
+                mapping_type=mapping_type,
+                scale_dtype=scale_dtype,
+                model_dtype=model_dtype,
+            )
+            for weight_dtype in [getattr(torch, f"int{x}") for x in range(1, 9)]
+            for granularity in [PerGroup(32), PerGroup(128), PerAxis(0)]
+            for mapping_type in [MappingType.SYMMETRIC, MappingType.ASYMMETRIC]
+            for scale_dtype in [torch.float32, torch.bfloat16, torch.float16]
+            for model_dtype in [torch.float32, torch.bfloat16]
+        ],
+        name_func=lambda f, _, params: f.__name__ + f"_{params.kwargs}",
+    )
+    def test_identical_to_IntXQuantizationAwareTrainingConfig(
+        self, weight_dtype, granularity, mapping_type, scale_dtype, model_dtype
+    ):
+        # ASYMMETRIC in QAT is very different that PTQ configs
+        if mapping_type == MappingType.ASYMMETRIC:
+            return
+
+        embedding_dim = 4096
+        num_embeddings = 131
+        model = torch.nn.Sequential(
+            *[torch.nn.Embedding(num_embeddings, embedding_dim)]
+        )
+        model = model.to(model_dtype)
+        indices = torch.randint(0, num_embeddings, (7,), dtype=torch.int32)
+
+        is_symmetric = mapping_type == MappingType.SYMMETRIC
+        group_size = (
+            granularity.group_size
+            if isinstance(granularity, PerGroup)
+            else embedding_dim
+        )
+
+        embedding_filter = lambda m, fqn: isinstance(m, torch.nn.Embedding)
+        weight_config = FakeQuantizeConfig(
+            weight_dtype,
+            group_size=group_size,
+            is_symmetric=is_symmetric,
+            scale_precision=scale_dtype,
+        )
+        quantize_(
+            model,
+            IntXQuantizationAwareTrainingConfig(weight_config=weight_config),
+            embedding_filter,
+        )
+        expected_out = model(indices)
+
+        quantize_(model, FromIntXQuantizationAwareTrainingConfig(), embedding_filter)
+        quantize_(
+            model,
+            IntxWeightOnlyConfig(
+                weight_dtype=weight_dtype,
+                granularity=granularity,
+                mapping_type=mapping_type,
+                scale_dtype=scale_dtype,
+            ),
+            embedding_filter,
+        )
+        actual_out = model(indices)
+        self.assertTrue(torch.allclose(expected_out, actual_out))
+
+    @parameterized.expand(
+        [
+            param(
+                granularity=granularity,
+                scale_dtype=scale_dtype,
+                model_dtype=model_dtype,
+            )
+            for granularity in [PerGroup(32), PerGroup(128), PerAxis(0)]
+            for scale_dtype in [torch.float32, torch.bfloat16, torch.float16]
+            for model_dtype in [torch.float32, torch.bfloat16]
+        ],
+        name_func=lambda f, _, params: f.__name__ + f"_{params.kwargs}",
+    )
+    def test_identical_to_Int4WeightOnlyEmbeddingQATQuantizer(
+        self, granularity, scale_dtype, model_dtype
+    ):
+        embedding_dim = 4096
+        num_embeddings = 131
+        model = torch.nn.Sequential(
+            *[torch.nn.Embedding(num_embeddings, embedding_dim)]
+        )
+        model = model.to(model_dtype)
+        indices = torch.randint(0, num_embeddings, (7,), dtype=torch.int32)
+
+        group_size = (
+            granularity.group_size
+            if isinstance(granularity, PerGroup)
+            else embedding_dim
+        )
+
+        embedding_filter = lambda m, fqn: isinstance(m, torch.nn.Embedding)
+
+        qat_quantizer = Int4WeightOnlyEmbeddingQATQuantizer(
+            group_size=group_size,
+            scale_precision=scale_dtype,
+            zero_point_precision=torch.int32,
+        )
+        model = qat_quantizer.prepare(model)
+        expected_out = model(indices)
+
+        # Convert model method 1
+        quantize_(model, FromIntXQuantizationAwareTrainingConfig(), embedding_filter)
+        quantize_(
+            model,
+            IntxWeightOnlyConfig(
+                weight_dtype=torch.int4,
+                granularity=granularity,
+                mapping_type=MappingType.SYMMETRIC,
+                scale_dtype=scale_dtype,
+            ),
+            embedding_filter,
+        )
+        actual_out1 = model(indices)
+        self.assertTrue(torch.allclose(expected_out, actual_out1))
+
+        # TODO: method 2 does not work because the converted embedding op
+        # incorrectly casts output of to indices.dtype
+        # Convert model method 2
+        # qat_quantizer.convert(prepared_model_copy)
+        # actual_out2 = prepared_model_copy(indices)
+        # self.assertTrue(torch.allclose(expected_out, actual_out2))
+
 
 if __name__ == "__main__":
     unittest.main()