Introduce int8 quantization api (version 2)

docs/source/quantization_overview.rst

@@ -5,7 +5,7 @@ First we want to lay out the torchao stack::
 
   Quantization Algorithms/Flows: weight only/dynamic/static quantization, hqq, awq, gptq etc.
   ---------------------------------------------------------------------------------------------
-      Quantized Tensors (derived dtypes): Int4Tensor, Int4PreshuffledTensor, Float8Tensor
+      Quantized Tensors (derived dtypes): Int4Tensor, Int4PreshuffledTensor, Int8Tensor, Float8Tensor
   ---------------------------------------------------------------------------------------------
     Quantization Primitive Ops/Efficient Kernels: matmul, quantize, dequantize
   ---------------------------------------------------------------------------------------------
@@ -88,6 +88,8 @@ So in general we structure Tensor subclasses by dervied dtpype and packing forma
      - scaled int4
      - preshuffled (special format to optimize for loading)
      - float8 act + int4 weight dynamic quantization and int4 weight only quantization
+   * - Int8Tensor
+     - plain (no packing needed)
 
 .. note::
    We don't have granularity specific tensor subclasses, i.e. no Float8RowwiseTensor or Float8BlockwiseTensor, all granularities are implemented in the same Tensor, we typically use a general `block_size` attribute to distinguish between different granularities, and each Tensor is allowed to support only a subset of all possible granularity options.

test/quantization/quantize_/workflows/int8/test_int8_tensor.py

@@ -0,0 +1,217 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD 3-Clause license found in the
+# LICENSE file in the root directory of this source tree.
+
+import copy
+import unittest
+
+import torch
+from torch._inductor.utils import run_and_get_code
+from torch.testing import FileCheck
+from torch.testing._internal import common_utils
+
+from torchao.quantization import (
+    Int8DynamicActivationInt8WeightConfig,
+    Int8WeightOnlyConfig,
+    quantize_,
+)
+from torchao.quantization.granularity import PerRow, PerTensor
+from torchao.quantization.utils import compute_error, get_block_size
+from torchao.testing.model_architectures import ToyTwoLinearModel
+from torchao.testing.utils import TorchAOIntegrationTestCase
+
+
+@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
+@common_utils.instantiate_parametrized_tests
+class TestInt8Tensor(TorchAOIntegrationTestCase):
+    def setUp(self):
+        super().setUp()
+
+        self.test_shape = (32, 20)
+        self.dtype = torch.bfloat16
+        self.batch_size = 32
+
+        torch.manual_seed(42)
+
+    @common_utils.parametrize(
+        "config",
+        [
+            Int8DynamicActivationInt8WeightConfig(version=2),
+            Int8WeightOnlyConfig(version=2),
+        ],
+    )
+    def test_creation_and_attributes(self, config):
+        """Test tensor creation, dtypes, and ranges"""
+        linear = torch.nn.Linear(
+            self.test_shape[1],
+            self.test_shape[0],
+            bias=False,
+            dtype=self.dtype,
+            device="cuda",
+        )
+        quantize_(linear, config)
+
+        w = linear.weight
+
+        self.assertEqual(w.shape, self.test_shape)
+        self.assertEqual(w.qdata.dtype, torch.int8)
+        self.assertTrue(torch.all(w.qdata >= -128) and torch.all(w.qdata <= 127))
+
+    @common_utils.parametrize("dtype", [torch.bfloat16, torch.float32])
+    @common_utils.parametrize("compile", [True, False])
+    @common_utils.parametrize(
+        "config",
+        [
+            Int8DynamicActivationInt8WeightConfig(version=2),
+            Int8WeightOnlyConfig(version=2),
+        ],
+    )
+    @common_utils.parametrize(
+        "sizes",
+        [
+            ((128,), 256, 128),  # 2D
+            ((32, 128), 64, 256),  # 3D
+        ],
+    )
+    def test_int8_linear_variants(
+        self,
+        dtype: torch.dtype,
+        config,
+        compile: bool,
+        sizes: tuple,
+    ):
+        """Test linear operation supports including shape and compile"""
+        M, N, K = sizes
+        input_tensor = torch.randn(*M, K, dtype=dtype, device="cuda")
+        model = ToyTwoLinearModel(K, N, K, dtype=dtype, device="cuda").eval()
+        model_q = copy.deepcopy(model)
+
+        quantize_(model_q, config)
+
+        self.assertEqual(model_q.linear2.weight.scale.shape, (K,))
+        self.assertEqual(model_q.linear2.weight.scale.ndim, 1)
+
+        if compile:
+            model_q = torch.compile(model_q, fullgraph=True)
+
+        output_fp = model(input_tensor)
+        output_quantized = model_q(input_tensor)
+
+        assert compute_error(output_fp, output_quantized) > 20, (
+            f"Quantization error is too high got a SQNR of {compute_error(output_fp, output_quantized)}"
+        )
+
+    @common_utils.parametrize(
+        "config",
+        [
+            Int8DynamicActivationInt8WeightConfig(version=2),
+            Int8WeightOnlyConfig(version=2),
+        ],
+    )
+    @common_utils.parametrize("device", ["cpu", "cuda"])
+    @common_utils.parametrize("dtype", [torch.bfloat16, torch.float16])
+    def test_slice(self, config, device, dtype):
+        """Test tensor slicing with per-row quantization"""
+        tensor_size = 256
+        slice_sizes = (64, 128)
+
+        dummy = torch.nn.Linear(
+            tensor_size, tensor_size, bias=False, dtype=dtype, device=device
+        )
+        quantize_(dummy, config)
+
+        weight1 = dummy.weight.clone().narrow(0, 0, slice_sizes[0])
+        weight2 = dummy.weight.clone().narrow(1, 0, slice_sizes[1])
+
+        self.assertEqual(weight1.qdata, dummy.weight.qdata.narrow(0, 0, slice_sizes[0]))
+        self.assertEqual(weight2.qdata, dummy.weight.qdata.narrow(1, 0, slice_sizes[1]))
+        self.assertEqual(weight1.scale, dummy.weight.scale.narrow(0, 0, slice_sizes[0]))
+        self.assertEqual(weight2.scale, dummy.weight.scale)
+        with self.assertRaises(NotImplementedError):
+            _ = dummy.weight[::2]
+
+    @common_utils.parametrize(
+        "config",
+        [
+            Int8DynamicActivationInt8WeightConfig,
+            Int8WeightOnlyConfig,
+        ],
+    )
+    @common_utils.parametrize("granularity", [PerTensor(), PerRow()])
+    def test_index_select(self, config, granularity):
+        """test that `x_0 = x[0]` works when `x` is a 2D quantized tensor."""
+        N, K = 256, 512
+        x = torch.randn(N, K, device="cuda", dtype=torch.bfloat16)
+        linear = torch.nn.Linear(K, N, bias=False, dtype=torch.bfloat16, device="cuda")
+        linear.weight.data = x
+
+        config = config(version=2, granularity=granularity)
+        quantize_(linear, config)
+
+        x_int8 = linear.weight
+        x_int8_0 = x_int8[0]
+
+        # Test dequantization consistency
+        torch.testing.assert_close(
+            x_int8.dequantize()[0], x_int8_0.dequantize(), atol=0, rtol=0
+        )
+
+        # Test block_size granularity
+        if isinstance(granularity, PerRow):
+            self.assertEqual(
+                list(get_block_size(x_int8.shape, x_int8.granularity)), [1, K]
+            )
+        elif isinstance(granularity, PerTensor):
+            self.assertEqual(
+                list(get_block_size(x_int8.shape, x_int8.granularity)), [N, K]
+            )
+
+    @common_utils.parametrize(
+        "config",
+        [
+            Int8DynamicActivationInt8WeightConfig(version=2),
+            Int8WeightOnlyConfig(version=2),
+        ],
+    )
+    def test_dequantization_accuracy(self, config):
+        """Test dequantization accuracy separately"""
+        linear = torch.nn.Linear(
+            256, 512, bias=False, dtype=torch.bfloat16, device="cuda"
+        )
+        weight_fp = copy.deepcopy(linear.weight)
+        quantize_(linear, config)
+
+        tensor = linear.weight
+        dequantized = tensor.dequantize()
+        self.assertEqual(dequantized.shape, weight_fp.shape)
+        assert compute_error(dequantized, weight_fp) > 20, (
+            f"Dequantization error is too high to get a SQNR of {compute_error(dequantized, weight_fp)}"
+        )
+
+    def test_available_gpu_kernels(self):
+        """Check which GPU kernels are used"""
+        torch.compiler.reset()
+
+        M, K, N = 128, 256, 512
+        m = torch.nn.Sequential(
+            torch.nn.Linear(K, N, device="cuda", dtype=torch.bfloat16)
+        )
+
+        config = Int8DynamicActivationInt8WeightConfig(version=2)
+        quantize_(m, config)
+
+        m = torch.compile(m)
+        x = torch.randn(M, K, device="cuda", dtype=torch.bfloat16)
+
+        out, code = run_and_get_code(m, x)
+
+        # Check expected kernels are present
+        FileCheck().check_count("triton_per_fused", 1).check_count(
+            "extern_kernels._int_mm", 1
+        ).check_count("triton_poi_fused", 1).run(code[0])
+
+
+if __name__ == "__main__":
+    common_utils.run_tests()