[Intel GPU] Xpu matmul implementation for complex dtype (#160867)

Enabling complex datatype support for 4 ops: `mm`, `bmm`, `addmm`, `baddbmm` for XPU. From now implementation will call functions created in: https://github.com/intel/torch-xpu-ops/pull/1992.

Additionally added complex datatype tests for matmul operators. More detailed tests are going to be enabled in: https://github.com/intel/torch-xpu-ops/pull/1993

CI runs have found that `test_comprehensive_linalg_eig_xpu` tests were calling internally matmul with complex datatype. With this PR test starts to pass so linalg.eig was removed from `inductor_expected_failures_single_sample["xpu"]` as otherwise it was failing with: `Unexpected success` message.

Part of: https://github.com/intel/torch-xpu-ops/issues/1853

Pull Request resolved: https://github.com/pytorch/pytorch/pull/160867
Approved by: https://github.com/guangyey, https://github.com/ZhiweiYan-96, https://github.com/gujinghui, https://github.com/EikanWang, https://github.com/Silv3S, https://github.com/CuiYifeng, https://github.com/jansel

aten/src/ATen/native/mkldnn/xpu/Blas.cpp

@@ -2,6 +2,7 @@
 #include <ATen/WrapDimUtilsMulti.h>
 #include <ATen/native/Resize.h>
 #include <ATen/native/mkldnn/xpu/detail/oneDNN.h>
+#include <ATen/native/xpu/Blas.h>
 #include <torch/library.h>
 #ifndef AT_PER_OPERATOR_HEADERS
 
@@ -50,9 +51,13 @@ Tensor& addmm_out(
       mat1.dtype(),
       " != ",
       mat2.dtype())
+
   // complex case
-  TORCH_CHECK(
-      !mat1.is_complex(), "Complex datatype matmul is not supported in oneDNN");
+  if (self.is_complex()) {
+    at::native::addmm_complex_out_xpu(self, mat1, mat2, beta, alpha, result);
+
+    return result;
+  }
 
   std::vector<int64_t> result_shape = {mat1.size(0), mat2.size(1)};
   result.resize_(result_shape);
@@ -167,8 +172,11 @@ Tensor& mm_out(const Tensor& self, const Tensor& mat2, Tensor& result) {
     return result;
   }
 
-  TORCH_CHECK(
-      !self.is_complex(), "Complex datatype matmul is not supported in oneDNN");
+  if (self.is_complex()) {
+    at::native::mm_complex_out_xpu(self, mat2, result);
+
+    return result;
+  }
 
   onednn::matmul(result, self, mat2, Tensor(), true, onednn::Attr());
   return result;
@@ -208,9 +216,12 @@ Tensor& baddbmm_out(
       input.sizes());
 
   // complex case
-  TORCH_CHECK(
-      !batch1.is_complex(),
-      "Complex datatype matmul is not supported in oneDNN");
+  if (input.is_complex()) {
+    at::native::baddbmm_complex_out_xpu(
+        input, batch1, batch2, beta, alpha, result);
+
+    return result;
+  }
 
   // general case
   onednn::Attr attr;
@@ -257,8 +268,13 @@ Tensor& bmm_out(const Tensor& self, const Tensor& batch2, Tensor& result) {
     return result;
   }
 
-  TORCH_CHECK(
-      !self.is_complex(), "Complex datatype matmul is not supported in oneDNN");
+  // complex case
+  if (self.is_complex()) {
+    at::native::bmm_complex_out_xpu(self, batch2, result);
+
+    return result;
+  }
+
   onednn::matmul(result, self, batch2, at::Tensor(), true, onednn::Attr());
   return result;
 }

test/inductor/test_torchinductor_opinfo.py

@@ -294,7 +294,6 @@ inductor_expected_failures_single_sample["xpu"] = {
         i32,
         i64,
     },  # align with cuda.
-    "linalg.eig": {f32, f64},
     ("linalg.pinv", "singular"): {f64},
     # could not create a primitive
     "addmv": {f64},

test/xpu/test_gemm.py

@@ -238,7 +238,7 @@ class TestBasicGEMM(TestCase):
                 )
 
     @precisionOverride({torch.float: 1e-4, torch.double: 1e-6, torch.half: 1e-1})
-    @dtypes(torch.float32, torch.half, torch.double)
+    @dtypes(torch.float32, torch.half, torch.double, torch.complex64)
     @tf32_on_and_off(0.05)
     def test_addmm(self, device, dtype):
         self._test_addmm_impl(torch.addmm, None, device, dtype)
@@ -313,6 +313,7 @@ class TestBasicGEMM(TestCase):
         torch.half,
         torch.float32,
         torch.float64,
+        torch.complex64,
     )
     @tf32_on_and_off(0.05)
     def test_mm(self, device, dtype):
@@ -416,7 +417,7 @@ class TestBasicGEMM(TestCase):
             _test_mm(n, m, p, dtype, genf)
 
     @precisionOverride({torch.half: 0.05, torch.bfloat16: 0.05})
-    @dtypes(torch.float32, torch.bfloat16, torch.half, torch.float64)
+    @dtypes(torch.float32, torch.bfloat16, torch.half, torch.float64, torch.complex64)
     @tf32_on_and_off(0.05)
     def test_bmm(self, device, dtype):
         batch_sizes = [1, 10]
@@ -533,7 +534,7 @@ class TestBasicGEMM(TestCase):
         self.assertEqual(res7, ref)
 
     @precisionOverride({torch.half: 0.05, torch.bfloat16: 0.05})
-    @dtypes(torch.float64, torch.float32, torch.bfloat16, torch.half)
+    @dtypes(torch.float64, torch.float32, torch.bfloat16, torch.half, torch.complex64)
     @tf32_on_and_off(0.005)
     def test_addbmm(self, device, dtype):
         num_batches = 2
@@ -637,7 +638,7 @@ class TestBasicGEMM(TestCase):
             self._test_addbmm_baddbmm("addbmm", b1, b2, ref, out_tensor)
 
     @precisionOverride({torch.half: 0.1, torch.bfloat16: 0.5, torch.float64: 1e-6})
-    @dtypes(torch.float64, torch.float32, torch.bfloat16, torch.half)
+    @dtypes(torch.float64, torch.float32, torch.bfloat16, torch.half, torch.complex64)
     @tf32_on_and_off(0.01)
     def test_baddbmm(self, device, dtype):
         num_batches = 10

third_party/xpu.txt

@@ -1 +1 @@
-ce9db15136c5e8ba1b51710aae574ce4791c5d73
+30dcaa83183b189b00dc827fb39234714fe4e46d