Add INT8 SDPA path for CPU

setup.py

@@ -55,6 +55,10 @@ def read_version(file_path="version.txt"):
     and platform.system() == "Darwin"
 )
 
+use_cpp_avx512 = os.getenv("USE_AVX512", "1") == "1" and platform.system() == "Linux"
+
+from torchao.utils import TORCH_VERSION_AT_LEAST_2_7
+
 version_prefix = read_version()
 # Version is version.dev year month date if using nightlies and version if not
 version = (
@@ -284,6 +288,17 @@ def get_extensions():
             ["-O3" if not debug_mode else "-O0", "-fdiagnostics-color=always"]
         )
 
+        if use_cpp_avx512 and TORCH_VERSION_AT_LEAST_2_7:
+            if torch._C._cpu._is_avx512_supported():
+                extra_compile_args["cxx"].extend(
+                    [
+                        "-DCPU_CAPABILITY_AVX512",
+                        "-march=native",
+                        "-mfma",
+                        "-fopenmp",
+                    ]
+                )
+
         if debug_mode:
             extra_compile_args["cxx"].append("-g")
             if "nvcc" in extra_compile_args:
@@ -305,6 +320,12 @@ def get_extensions():
 
     # Collect C++ source files
     sources = list(glob.glob(os.path.join(extensions_dir, "**/*.cpp"), recursive=True))
+    if IS_WINDOWS:
+        # Remove csrc/cpu/*.cpp on Windows due to the link issue: unresolved external symbol PyInit__C
+        excluded_sources = list(
+            glob.glob(os.path.join(extensions_dir, "cpu/*.cpp"), recursive=True)
+        )
+        sources = [s for s in sources if s not in excluded_sources]
 
     extensions_cuda_dir = os.path.join(extensions_dir, "cuda")
     cuda_sources = list(

test/prototype/inductor/test_int8_sdpa_fusion.py

@@ -0,0 +1,217 @@
+import itertools
+
+import pytest
+import torch
+import torch.utils.checkpoint
+from torch._dynamo.utils import counters
+from torch._inductor import config
+from torch._inductor.test_case import TestCase, run_tests
+from torch._inductor.utils import run_and_get_code
+from torch.testing._internal.common_utils import IS_LINUX, skipIfRocm
+from torch.testing._internal.inductor_utils import HAS_CPU
+from torch.utils.cpp_extension import IS_WINDOWS
+
+import torchao
+from torchao.prototype.inductor.fx_passes.int8_sdpa_fusion import _int8_sdpa_init
+from torchao.utils import TORCH_VERSION_AT_LEAST_2_7
+
+
+class SelfAttnLikeModule(torch.nn.Module):
+    def __init__(
+        self,
+        input_dim,
+        has_mask,
+        num_attention_heads=None,
+        attention_head_size=None,
+    ) -> None:
+        super().__init__()
+        self.input_dim = input_dim
+        self.q_proj = torch.nn.Linear(input_dim, input_dim, bias=False)
+        self.k_proj = torch.nn.Linear(input_dim, input_dim, bias=False)
+        self.v_proj = torch.nn.Linear(input_dim, input_dim, bias=False)
+        self.softmax = torch.nn.Softmax(dim=-1)
+        assert num_attention_heads is not None
+        assert attention_head_size is not None
+        self.num_attention_heads = num_attention_heads
+        self.attention_head_size = attention_head_size
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+        self.dense = torch.nn.Linear(self.all_head_size, self.all_head_size)
+        self.dropout = torch.nn.Dropout(0)
+        self.has_mask = has_mask
+
+    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
+        new_x_shape = x.size()[:-1] + (
+            self.num_attention_heads,
+            self.attention_head_size,
+        )
+        x = x.view(new_x_shape)
+        return x.permute([0, 2, 1, 3])
+
+    def forward(self, x, mask):
+        q = self.q_proj(x)
+        k = self.k_proj(x)
+        v = self.v_proj(x)
+        q = self.transpose_for_scores(q)
+        k = self.transpose_for_scores(k)
+        v = self.transpose_for_scores(v)
+        scores = torch.matmul(q, k.transpose(-1, -2)) / (self.input_dim**0.5)
+        if self.has_mask and mask.dtype != scores.dtype:
+            scores = scores + mask
+        attention = self.softmax(scores)
+        attention = self.dropout(attention)
+        context_layer = torch.matmul(attention, v)
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        context_layer = context_layer.view(
+            context_layer.size()[:-2] + (self.all_head_size,)
+        )
+        return self.dense(context_layer)
+
+
+class TestSDPAPatternRewriterTemplate(TestCase):
+    def _clone_inputs(self, inputs):
+        def clone(x):
+            if not isinstance(x, torch.Tensor):
+                return x
+            return x.clone()
+
+        return [clone(x) for x in inputs]
+
+    def _check_common(
+        self,
+        dot_prod_attention,
+        args1=None,
+        contains=True,
+        atol=1e-5,
+        has_fuse_pattern=True,
+        has_dropout=False,
+        check_train=True,
+        override_check_equal=False,
+        dtype=torch.float,
+        rtol=1.3e-6,
+    ):
+        if args1 is None:
+            tensor_shape = (4, 2, 16, 32)
+            args1 = [
+                torch.randn(tensor_shape, device=self.device, dtype=dtype),
+                torch.randn(tensor_shape, device=self.device, dtype=dtype),
+                torch.randn(tensor_shape, device=self.device, dtype=dtype),
+            ]
+        else:
+            args1 = list(args1)
+        args2 = self._clone_inputs(args1)
+
+        for training in [False, True] if check_train else [False]:
+            for x in itertools.chain(args1[:], args2[:]):
+                if isinstance(x, torch.Tensor) and x.is_floating_point():
+                    x.requires_grad = training
+
+            dropout_arg = [training] if has_dropout else []
+            torch.manual_seed(1234)
+            result1 = dot_prod_attention(*(args1 + dropout_arg))
+
+            counters.clear()
+            torch.manual_seed(1234)
+            compiled_model = torch.compile(dot_prod_attention, fullgraph=True)
+            result2, source_code = run_and_get_code(
+                compiled_model,
+                *(args2 + dropout_arg),
+            )
+            source_code = "\n".join(source_code)
+            if has_fuse_pattern:
+                self.assertGreaterEqual(counters["inductor"]["int8_fuse_attention"], 1)
+            if contains:
+                # many of the patterns get re-expanded in dispatcher
+                self.assertIn(
+                    "torchao.scaled_dot_product_int8",
+                    source_code,
+                )
+
+            # some tests configured with very low dropout where we still want to check equality
+            if not has_dropout or override_check_equal:
+                self.assertEqual(result1, result2, atol=atol, rtol=1.3e-6)
+
+            if training:
+                result1.sum().backward()
+                result2.sum().backward()
+                for arg1, arg2 in zip(args1, args2):
+                    if (
+                        isinstance(arg1, torch.Tensor)
+                        and arg1.is_floating_point()
+                        and (not has_dropout or override_check_equal)
+                    ):
+                        self.assertEqual(arg1.grad, arg2.grad, atol=atol, rtol=rtol)
+
+    @skipIfRocm
+    @pytest.mark.skipif(
+        not TORCH_VERSION_AT_LEAST_2_7, reason="int8 sdpa requires torch 2.7 or later"
+    )
+    @pytest.mark.skipif(IS_WINDOWS, reason="int8 sdpa does not support windows yet")
+    @config.patch({"freezing": True})
+    def _test_sdpa_int8_rewriter(self):
+        from torch.export import export_for_training
+
+        import torchao.quantization.pt2e.quantizer.x86_inductor_quantizer as xiq
+        from torchao.quantization.pt2e.quantize_pt2e import convert_pt2e, prepare_pt2e
+        from torchao.quantization.pt2e.quantizer.x86_inductor_quantizer import (
+            X86InductorQuantizer,
+        )
+
+        # pattern is different for bs=1
+        torch.manual_seed(1234)
+        for dtype, has_mask, bs in itertools.product(
+            [torch.float32, torch.bfloat16], [True, False], [56, 1]
+        ):
+            seqlen, numhead, headsize = 197, 16, 64
+            mod = SelfAttnLikeModule(
+                input_dim=headsize * numhead,
+                has_mask=has_mask,
+                num_attention_heads=numhead,
+                attention_head_size=headsize,
+            ).eval()
+            inputs = (
+                torch.randn(
+                    (bs, seqlen, headsize * numhead), device=self.device, dtype=dtype
+                ),
+                torch.randn((bs, 1, 1, seqlen), device=self.device)
+                if has_mask
+                else None,
+            )
+            enable_autocast = dtype == torch.bfloat16
+            with (
+                torch.no_grad(),
+                torch.amp.autocast(
+                    self.device, enabled=enable_autocast, dtype=torch.bfloat16
+                ),
+            ):
+                _int8_sdpa_init()
+                quantizer = X86InductorQuantizer()
+                quantizer.set_global(xiq.get_default_x86_inductor_quantization_config())
+                quantizer.set_function_type_qconfig(
+                    torch.matmul, quantizer.get_global_quantization_config()
+                )
+                export_model = export_for_training(
+                    mod,
+                    inputs,
+                    strict=True,
+                ).module()
+                prepare_model = prepare_pt2e(export_model, quantizer)
+                prepare_model(*inputs)
+                convert_model = convert_pt2e(prepare_model)
+                torchao.quantization.pt2e.move_exported_model_to_eval(convert_model)
+                self._check_common(
+                    convert_model, args1=inputs, check_train=False, atol=1.0
+                )
+
+
+if HAS_CPU:
+
+    class SDPAPatternRewriterCpuTests(TestSDPAPatternRewriterTemplate):
+        device = "cpu"
+        test_sdpa_int8_rewriter_cpu = (
+            TestSDPAPatternRewriterTemplate._test_sdpa_int8_rewriter
+        )
+
+
+if __name__ == "__main__":
+    if IS_LINUX:
+        run_tests()