Deprecate torch.chain_matmul in favor of torch.linalg.multi_dot

aten/src/ATen/autocast_mode.cpp

@@ -281,7 +281,7 @@ TORCH_LIBRARY_IMPL(aten, Autocast, m) {
   KERNEL(ADD_NS(addbmm), "addbmm", Tensor (const Tensor &, const Tensor &, const Tensor &, Scalar, Scalar), fp16)
   KERNEL(ADD_NS(baddbmm), "baddbmm", Tensor (const Tensor &, const Tensor &, const Tensor &, Scalar, Scalar), fp16)
   KERNEL(ADD_NS(bmm), "bmm", Tensor (const Tensor &, const Tensor &), fp16)
-  KERNEL(ADD_NS(chain_matmul), "chain_matmul", Tensor (TensorList), fp16)
+  KERNEL(ADD_NS(linalg_multi_dot), "linalg_multi_dot", Tensor (TensorList), fp16)
   // The macro doesn't like these (I think it chokes on commas inside <>) so write them manually
   m.impl(TORCH_SELECTIVE_NAME("aten::_thnn_fused_lstm_cell"),
          TORCH_FN((&WrapFunction<CastPolicy::fp16,

aten/src/ATen/core/aten_interned_strings.h

@@ -228,7 +228,6 @@ _(aten, cat) \
 _(aten, cauchy) \
 _(aten, ceil) \
 _(aten, celu) \
-_(aten, chain_matmul) \
 _(aten, cholesky) \
 _(aten, cholesky_inverse) \
 _(aten, cholesky_solve) \

aten/src/ATen/core/interned_strings.h

@@ -196,6 +196,8 @@ namespace c10 {
   _(aten, clip_)                     \
   _(aten, det)                       \
   _(aten, linalg_det)                \
+  _(aten, chain_matmul)              \
+  _(aten, linalg_multi_dot)          \
   _(aten, linalg_norm)               \
   _(aten, append)                    \
   _(aten, item)                      \

docs/source/amp.rst

@@ -104,6 +104,7 @@ Ops that can autocast to ``float16``
 ``baddbmm``,
 ``bmm``,
 ``chain_matmul``,
+``multi_dot``,
 ``conv1d``,
 ``conv2d``,
 ``conv3d``,

test/test_autograd.py

@@ -2936,20 +2936,6 @@ def test_igammac(self):
         gradcheck(torch.igamma, (s, x))
         gradgradcheck(torch.igamma, (s, x))
 
-    def test_chain_matmul(self):
-        def gen_matrices(p):
-            matrices = []
-            for (pi, pi_1) in zip(p[:-1], p[1:]):
-                matrices.append(torch.randn(pi, pi_1).requires_grad_())
-            return matrices
-
-        gradcheck(torch.chain_matmul, gen_matrices([5, 10, 15, 5]))
-        gradcheck(torch.chain_matmul, gen_matrices([3, 5, 2, 6]))
-        gradcheck(torch.chain_matmul, gen_matrices([6, 2, 4, 8, 10]))
-        gradgradcheck(torch.chain_matmul, gen_matrices([5, 10, 15, 5]))
-        gradgradcheck(torch.chain_matmul, gen_matrices([3, 5, 2, 6]))
-        gradgradcheck(torch.chain_matmul, gen_matrices([6, 2, 4, 8, 10]))
-
     def test_profiler_tracing(self):
         t1, t2 = torch.ones(1), torch.ones(1)
         with torch.autograd.profiler.profile(use_kineto=kineto_available()) as prof:

test/test_cuda.py

@@ -2674,6 +2674,12 @@ def test_autocast_nn_fp32(self):
         for op, args in self.autocast_lists.nn_fp32:
             self._run_autocast_outofplace(op, args, torch.float32, module=torch._C._nn)
 
+    @unittest.skipIf(not TEST_CUDNN, 'CUDNN not available')
+    def test_autocast_linalg_fp16(self):
+        with torch.backends.cudnn.flags(enabled=True, deterministic=True):
+            for op, args in self.autocast_lists.linalg_fp16:
+                self._run_autocast_outofplace(op, args, torch.float16, module=torch._C._linalg)
+
     @unittest.skipIf(not TEST_CUDNN, 'CUDNN not available')
     def test_autocast_methods_fp16(self):
         with torch.backends.cudnn.flags(enabled=True, deterministic=True):

test/test_linalg.py

@@ -3274,8 +3274,21 @@ def test_old_matrix_rank(self, device, dtype):
             self.assertEqual(torch.matrix_rank(aaT, 0.01, True), np.linalg.matrix_rank(aaT.cpu().numpy(), 0.01, True))
 
     @onlyOnCPUAndCUDA
-    @dtypes(torch.float, torch.cfloat)
-    @precisionOverride({torch.float: 1e-02, torch.cfloat: 1e-02})
+    @dtypes(torch.double)
+    def test_chain_matmul(self, device, dtype):
+        # chain_matmul accepts a single input tensor while multi_dot does not
+        t = make_tensor((2, 2), device, dtype)
+        self.assertEqual(t, torch.chain_matmul(t))
+        with self.assertRaisesRegex(RuntimeError, r"chain_matmul\(\): Expected one or more matrices"):
+            torch.chain_matmul()
+
+        # chain_matmul expects all tensors to be 2D whereas multi_dot allows the first and last tensors to
+        # be either 1D or 2D
+        with self.assertRaisesRegex(RuntimeError, r"Tensor dimension is 1, expected 2 instead"):
+            torch.chain_matmul(make_tensor(1, device, dtype), make_tensor(1, device, dtype))
+
+    @onlyOnCPUAndCUDA
+    @dtypes(torch.double, torch.cdouble)
     def test_multi_dot(self, device, dtype):
         def check(*shapes, discontiguous=False):
             tensors = [make_tensor(shape, device, dtype, discontiguous=discontiguous) for shape in shapes]
@@ -3307,9 +3320,11 @@ def check(*shapes, discontiguous=False):
 
         # test large tensors
         check([10, 100], [100, 5], [5, 50])
+        check([10, 20], [20, 30], [30, 5])
 
         # test discontiguous input
         check([3, 2], [2, 2], [2, 3], [3, 4], discontiguous=True)
+        check([15, 5], [5, 10], [10, 20], [20, 25], discontiguous=True)
 
     @onlyOnCPUAndCUDA
     @dtypes(torch.float)
@@ -5800,25 +5815,6 @@ def run_test(*n):
         run_test(3, 3, 4, 4)
         run_test(3, 3, 5, 5)
 
-    @dtypes(torch.double)
-    def test_chain_matmul(self, device, dtype):
-        def product(matrices):
-            for mat in matrices[1:]:
-                matrices[0] = matrices[0].mm(mat)
-            return matrices[0]
-
-        def run_test(p):
-            matrices = []
-            for (pi, pi_1) in zip(p[:-1], p[1:]):
-                matrices.append(torch.randn(pi, pi_1, dtype=dtype, device=device))
-            self.assertEqual(torch.chain_matmul(*matrices), product(matrices))
-
-        run_test([10, 20, 30, 5])
-        run_test([15, 5, 10, 20, 25])
-
-        with self.assertRaisesRegex(RuntimeError, r"chain_matmul\(\): Expected one or more matrices"):
-            torch.chain_matmul()
-
     @skipCUDAIfNoMagma
     @skipCUDAIfRocm
     @skipCPUIfNoLapack

torch/functional.py

@@ -1438,6 +1438,10 @@ def chain_matmul(*matrices):
     needs to be greater than or equal to 2; if equal to 2 then a trivial matrix-matrix product is returned.
     If :math:`N` is 1, then this is a no-op - the original matrix is returned as is.
 
+    .. note::
+        :func:`torch.chain_matmul` is deprecated, use :func:`torch.linalg.multi_dot` instead. The differences are
+        that :func:`torch.linalg.multi_dot` requires at least 2 input tensors and allows the first and last
+        tensor to be either 1D or 2D.
 
     Args:
         matrices (Tensors...): a sequence of 2 or more 2-D tensors whose product is to be determined.

torch/testing/_internal/autocast_test_lists.py

@@ -109,7 +109,6 @@ def __init__(self, dev):
             ("matmul", mat0_fp32 + mat1_fp32),
             ("mm", mat0_fp32 + mat1_fp32),
             ("mv", mat0_fp32 + pointwise0_fp32),
-            ("chain_matmul", mat0_fp32 + mat1_fp32 + mat2_fp32),
             ("addbmm", mat0_fp32 + (torch.randn((n, n, n), device=dev, dtype=torch.float32),
                                     torch.randn((n, n, n), device=dev, dtype=torch.float32))),
             ("baddbmm", (torch.randn((n, n, n), device=dev, dtype=torch.float32),
@@ -221,6 +220,9 @@ def __init__(self, dev):
             ("soft_margin_loss", mat0_fp16 + (torch.ones((n, n), device=dev, dtype=torch.long),)),
             ("multi_margin_loss", mat0_fp16 + (torch.ones((n,), device=dev, dtype=torch.long),)),
         ]
+        self.linalg_fp16 = [
+            ("linalg_multi_dot", (mat0_fp32 + mat1_fp32 + mat2_fp32,)),
+        ]
         self.methods_fp16 = [
             ("__matmul__", mat0_fp32 + mat1_fp32)
         ]

torch/testing/_internal/common_methods_invocations.py

@@ -390,18 +390,22 @@ def sample_inputs_tensor_split(op_info, device, dtype, requires_grad):
                         kwargs=dict(dim=1)),)
 
 def sample_inputs_linalg_multi_dot(op_info, device, dtype, requires_grad):
-    test_cases: List[List[Tuple[int, ...]]] = [
-        [(S,), (S,)],
-        [(1, S), (S, 1)],
-        [(S, 0), (0, S)],
-        [(0, S), (S, 2)],
-        [(2, S), (S, 2), (2, S)],
+    # Each test case consists of the sizes in the chain of multiplications
+    # e.g. [2, S, 2, S] generates matrices (2, S) @ (S, 2) @ (2, S)
+    test_cases = [
+        [1, S, 1],
+        [S, 0, S],
+        [0, S, 2],
+        [2, S, 2, S],
+        [2, 3, 4, 5],
+        [5, 4, 0, 2],
+        [2, 4, 3, 5, 3, 2]
     ]
 
     result = []
-    for test_case in test_cases:
+    for sizes in test_cases:
         tensors = []
-        for size in test_case:
+        for size in zip(sizes[:-1], sizes[1:]):
             t = make_tensor(size, device, dtype, requires_grad=requires_grad)
             tensors.append(t)
         result.append(SampleInput(tensors))