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/aosp_15_r20/external/pytorch/torch/ao/quantization/backend_config/
H A D_qnnpack_pt2e.py36 # addmm
43 # input - addmm
46 # input - observer - addmm
49 # input - observer - addmm
52 # addmm, bias, act, weight = node_pattern
53 # return addmm
56 …# BackendPatternConfig((torch.ops.aten.addmm.default, MatchAllNode, MatchAllNode, torch.ops.at…
62 BackendPatternConfig(torch.ops.aten.addmm.default)
/aosp_15_r20/external/pytorch/test/inductor/
H A Dtest_pattern_matcher.py720 torch.addmm(a, b, c),
721 torch.addmm(b, c, a),
722 torch.addmm(c, a, b),
1123 return torch.ops.aten.addmm(inp, a, b)
1126 FileCheck().check("extern_kernels.addmm(").run(code[0])
1130 return torch.nn.functional.gelu(torch.ops.aten.addmm(inp, a, b))
1133 FileCheck().check_not("extern_kernels.addmm(").run(code[0])
1138 torch.ops.aten.addmm(inp, a, b).unsqueeze(0)
1143 FileCheck().check_not("extern_kernels.addmm(").run(code[0])
1201 return torch.ops.aten.addmm(inp, a, b)
[all …]
H A Dtest_max_autotune.py299 Make sure autotuning addmm in sub processes work without crashes.
304 def addmm(x, a, b): function
305 return torch.addmm(x, a, b)
311 Y_compiled = torch.compile(addmm, dynamic=dynamic)(x, a, b)
312 Y = addmm(x, a, b)
318 Make sure autotuning addmm with zero-size input works without crashes.
321 def addmm(x, a, b): function
322 return torch.addmm(x, a, b)
328 torch.compile(addmm, dynamic=dynamic)(x, a, b)
422 torch.addmm(a, b, c),
[all …]
H A Dtest_pad_mm.py177 def addmm(x, a, b): function
178 return torch.addmm(x, a, b)
183 self.assertEqual(torch.compile(addmm)(x, a, b), addmm(x, a, b))
284 return torch.addmm(a, b, c)
313 return torch.addmm(a, b, c)
348 return torch.ops.aten.addmm(input, x, y)
358 out_eager = torch.ops.aten.addmm(*inps)
368 out_eager = torch.ops.aten.addmm(*inps)
H A Dtest_cutlass_backend.py454 Make sure autotuning addmm in sub processes work without crashes.
462 def addmm(x, a, b, alpha, beta): function
463 return torch.addmm(x, a, b, alpha=alpha, beta=beta)
471 y_expected = addmm(x, a, b, alpha, beta)
473 compiled_fn = torch.compile(addmm, dynamic=dynamic)
655 return torch.addmm(x, a, b, alpha=beta, beta=alpha)
685 assert op_name == "addmm"
702 def addmm(x, a, b, alpha, beta): function
703 return torch.addmm(x, a, b, alpha=alpha, beta=beta)
730 torch.compile(addmm, dynamic=False)(x, a, b, 1.0, 1.0)
[all …]
/aosp_15_r20/external/executorch/backends/xnnpack/partition/config/
H A Dgemm_configs.py48 GEMM-like ops like Convolution, Addmm, Linear, mostly behave in the same way, in which we
323 We will handle the legacy form of addmm partitioning which will include
327 target_name = "addmm.default"
363 # if addmm belongs to linear src partition, then partition the
375 from the linear src partition, and plug those in as the addmm node's args. We also
376 take the users of the src partitions output node as the addmm node's users. Finally
378 getting the deps, we return the addmm nodes users and args back.
393 # map addmm's args to the source partition's inputs
411 # map addmm's args to the source partition linear's inputs and users
416 # Reset addmm node back to old args and users
/aosp_15_r20/external/pytorch/torch/_inductor/kernel/
H A Dmm.py128 torch.addmm, "at::addmm_out", op_overload=aten.addmm.default
146 Giving torch.addmm a 1D tensor calls a different (faster) cublasLt
151 return torch.addmm(inp[0], mat1, mat2, out=out, alpha=alpha, beta=beta)
152 return torch.addmm(inp, mat1, mat2, out=out, alpha=alpha, beta=beta)
322 @register_lowering(aten.addmm, type_promotion_kind=None)
348 return autotune_select_algorithm("addmm", choices, [inp, mat1, mat2], layout)
369 # unexpand inp to make sure fused addmm from cublasLt is used
391 # in the linear GEMM epilogue used by addmm.
444 # unexpand inp to make sure fused addmm from cublasLt is used
453 "addmm", choices, [inp_expanded, mat1, mat2], layout
/aosp_15_r20/external/executorch/backends/qualcomm/_passes/
H A Dconvert_to_linear.py29 Handle missing quantization tag for addmm op after decomposing
37 addmm = exir_ops.edge.aten.addmm.default variable in ConvertToLinear
42 {view_copy: 2, permute_copy: 1, addmm: 1},
43 {permute_copy: 1, addmm: 1},
167 addmm_node = [n for n in partitioned_nodes if n.target == self.addmm][0]
168 # weight -> permute -> input of addmm
/aosp_15_r20/external/pytorch/aten/src/ATen/native/vulkan/ops/
H A DLstm.cpp9 #include <ATen/ops/addmm.h>
140 at::addmm(b_ii, x, w_ii.t()) + at::addmm(b_hi, h, w_hi.t())); in lstm_input()
142 at::addmm(b_if, x, w_if.t()) + at::addmm(b_hf, h, w_hf.t())); in lstm_input()
144 at::tanh(at::addmm(b_ig, x, w_ig.t()) + at::addmm(b_hg, h, w_hg.t())); in lstm_input()
146 at::addmm(b_io, x, w_io.t()) + at::addmm(b_ho, h, w_ho.t())); in lstm_input()
H A DGru.cpp9 #include <ATen/ops/addmm.h>
111 at::addmm(b_ir, x, w_ir.t()) + at::addmm(b_hr, h, w_hr.t())); in gru_input()
113 at::addmm(b_iz, x, w_iz.t()) + at::addmm(b_hz, h, w_hz.t())); in gru_input()
115 at::addmm(b_in, x, w_in.t()) + r * (at::addmm(b_hn, h, w_hn.t()))); in gru_input()
/aosp_15_r20/external/pytorch/test/distributed/_tensor/
H A Dtest_matrix_ops.py40 dist_res = torch.addmm(input, mat1, mat2)
41 local_res = torch.addmm(input_tensor, tensor_to_shard, tensor_to_replicate)
57 dist_res = torch.addmm(inp, mat1, mat2)
58 local_res = torch.addmm(input_tensor, tensor_to_shard, tensor_to_replicate)
75 local_res = torch.addmm(input_tensor, tensor_to_shard1, tensor_to_shard0)
76 dist_res = torch.addmm(input, mat1, mat2)
78 # test if addmm output is a partial
/aosp_15_r20/external/pytorch/test/export/
H A Dtest_experimental.py218 …addmm = torch.ops.aten.addmm.default(p_linear_bias, view, permute); p_linear_bias = permute = None
219 view_1 = torch.ops.aten.view.default(addmm, [3]); addmm = None
267 …addmm = torch.ops.aten.addmm.default(p_linear_bias, view, permute); p_linear_bias = permute = None
268 view_1 = torch.ops.aten.view.default(addmm, [3]); addmm = None
/aosp_15_r20/external/executorch/backends/cadence/aot/
H A Dfuse_ops.py51 Fuse X and Y into a single addmm node, after making sure that we can
73 # Our addmm implementation computes (mat1 * mat2 + bias). So the
74 # addmm node in the graph should have three args. We collectively
125 # Create a new addmm node, and insert it before add node. DCE should
130 exir_ops.edge.aten.addmm.default,
133 # Replace all the uses of add node with addmm node, and remove add
138 # As a finishing step, we want to ensure that the output of addmm is
146 # is the shape out of view node T2. However, the fused addmm will
151 # 2. The single successor of addmm is not a view op.
154 # Create a view node that correctly reshapes the output of addmm
[all …]
/aosp_15_r20/external/pytorch/tools/autograd/
H A Ddeprecated.yaml49 - name: addmm(Scalar beta, Tensor self, Scalar alpha, Tensor mat1, Tensor mat2) -> Tensor
50 aten: addmm(self, mat1, mat2, beta, alpha)
55 - name: addmm(Scalar beta, Tensor self, Scalar alpha, Tensor mat1, Tensor mat2, *, Tensor(a!) out) …
58 - name: addmm(Scalar beta, Tensor self, Tensor mat1, Tensor mat2) -> Tensor
59 aten: addmm(self, mat1, mat2, beta, 1)
64 - name: addmm(Scalar beta, Tensor self, Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)
/aosp_15_r20/external/executorch/backends/cadence/aot/tests/
H A Dtest_fusion_ops_passes.py51 # Assert that mm and add were fused to addmm
52 self.assertEqual(count_node(graph_module, exir_ops.edge.aten.addmm.default), 1)
73 # Assert that mm and add were fused to addmm
74 self.assertEqual(count_node(graph_module, exir_ops.edge.aten.addmm.default), 1)
93 # Assert that mm and add were fused to addmm
94 self.assertEqual(count_node(graph_module, exir_ops.edge.aten.addmm.default), 1)
116 # Assert that mm and add were not fused to addmm, since z cannot be
138 # Assert that mm and add were not fused to addmm, since add has multiple
/aosp_15_r20/external/pytorch/torch/csrc/jit/passes/
H A Dpeephole.cpp341 // z + x.mm(y) == z.addmm(x, y) == x.mm(y) + z in FuseAddMM()
346 // one of them is a scalar, but addmm will throw in that case, so we in FuseAddMM()
401 auto* addmm_node = graph->insertNode(graph->create(aten::addmm, 1)); in FuseAddMM()
437 // ops. However, we rely on seeing the fused version of AddMM for ONNX export,
441 // torch.addmm(a, b, c, out=a).
442 // That's because addmm dispatches internally to gemm, which computes:
444 // but aten::addmm(a, b, c, 1, 1) is really:
447 // explicit add for a copy inside the addmm function. Note that it
/aosp_15_r20/external/pytorch/torch/_inductor/fx_passes/
H A Dpad_mm.py141 return aten.addmm(input, mat1, mat2, beta=beta, alpha=alpha)
147 match, mat1, mat2, torch.ops.aten.addmm, input=input
184 res = aten.addmm(input, mat1, mat2, beta=beta, alpha=alpha)
332 aten.addmm.default,
378 if op is torch.ops.aten.mm or op is torch.ops.aten.addmm:
448 if ori_time is None and op is torch.ops.aten.addmm and input is not None:
449 # realize bias for addmm
492 if op is torch.ops.aten.addmm:
H A Dfreezing_patterns.py188 aten.addmm(b1, inp, w1),
189 aten.addmm(b2, inp, w2),
190 aten.addmm(b3, inp, w3),
196 return aten.addmm(cat_b, inp, cat_w).chunk(3, dim=1)
/aosp_15_r20/external/pytorch/aten/src/ATen/native/sparse/cuda/
H A DSparseCUDATensorMath.cu87 // addmm(Tensor, SparseTensor, Tensor, Scalar, Scalar) [broadcasts]
91 …TORCH_CHECK(t.is_cuda(), "Expected all tensors to be on the same device. addmm: expected 'self' to… in s_addmm_out_sparse_dense_cuda()
92 …TORCH_CHECK(r_.is_cuda(), "Expected all tensors to be on the same device. addmm: expected 'out' to… in s_addmm_out_sparse_dense_cuda()
93 …TORCH_CHECK(sparse_.is_cuda(), "Expected all tensors to be on the same device. addmm: expected 'ma… in s_addmm_out_sparse_dense_cuda()
94 …TORCH_CHECK(dense.is_cuda(), "Expected all tensors to be on the same device. addmm: expected 'mat2… in s_addmm_out_sparse_dense_cuda()
98 TORCH_CHECK(dense.dim() == 2, "addmm: 2D tensor expected, got ", dense.dim(), "D tensor"); in s_addmm_out_sparse_dense_cuda()
99 …TORCH_CHECK(sparse_.sparse_dim() == 2, "addmm: expected first two dims to be sparse (indices has s… in s_addmm_out_sparse_dense_cuda()
108 "addmm: Argument #1 (t): Expected dim 0 size ", m, ", got ", t.size(0)); in s_addmm_out_sparse_dense_cuda()
110 "addmm: Argument #1 (t): Expected dim 1 size ", n, ", got ", t.size(1)); in s_addmm_out_sparse_dense_cuda()
112 "addmm: Argument #3 (dense): Expected dim 0 size ", k, ", got ", dense.size(0)); in s_addmm_out_sparse_dense_cuda()
[all …]
/aosp_15_r20/external/executorch/backends/apple/mps/runtime/operations/
H A DLinearAlgebra.mm54 … name:@"addmm/matmul"];
63 name:@"addmm/alpha*matmul"];
73 name:@"addmm/beta*bias"];
78 … name:@"addmm/beta*bias*alpha*matmul"];
/aosp_15_r20/external/pytorch/aten/src/ATen/test/
H A Dbroadcast_test.cpp146 ASSERT_TRUE(a.addmm(b, c).equal(a.expand({5, 7}).addmm(b, c))); in TestExplicitDimBasic()
155 ASSERT_TRUE(aScalar.addmm(b, c).equal(aScalar.expand({5, 7}).addmm(b, c))); in TestExplicitDimWithScalar()
164 ASSERT_ANY_THROW(a.addmm(b, c)); in TestExplicitDimWithMismatchedSizes()
/aosp_15_r20/external/pytorch/test/
H A Dtest_sparse_csr.py95 where f is `torch.addmv` or `torch.addmm`.
1473 torch.addmm(s, csr, m2)
1476 torch.addmm(s, csr, m2)
1526 … expected = [torch.addmm(c[i], a, b[i], alpha=alpha, beta=beta) for i in range(c.shape[0])]
1709 …self.run_test_block_addmm_addmv(torch.addmm, c, a, b, op_b, op_out, dtype=dtype, device=device, re…
1710 self.run_test_block_addmm_addmv(make_transposed_addmm_op(torch.addmm),
1845 # TODO: addmm doesn't support strided result for sparse inputs.
1847 res = torch.addmm(t, x, y, beta=beta, alpha=alpha)
1848 expected = torch.addmm(t, x.to_dense(), y.to_dense(), beta=beta, alpha=alpha)
1851 res = torch.addmm(t, x, y)
[all …]
/aosp_15_r20/external/pytorch/test/xpu/
H A Dtest_gemm.py137 self._test_addmm_impl(torch.addmm, None, device, dtype)
666 fns = ["baddbmm", "addbmm", "addmm", "addmv", "addr"]
687 elif fn == "addmm":
792 self._test_addmm_addmv(torch.addmm, M, m1, m2)
799 lambda: torch.addmm(M, m1, m2),
890 torch.addmm(M, m2.t(), m1)
895 torch.addmm(M, m2, m1)
923 # mm, addmm
936 self.assertEqual((0, 0), fn(torch.addmm, (0, 0), (0, 0), (0, 0)).shape)
937 self.assertEqual((0, 1), fn(torch.addmm, (1,), (0, 17), (17, 1)).shape)
[all …]
/aosp_15_r20/external/executorch/backends/xnnpack/partition/
H A Dconfigs.py65 exir_ops.edge.aten.addmm.default, # TODO(T163877189) add constraint for addmm
101 exir_ops.edge.aten.addmm.default, # TODO(T163877189) add constraint for addmm
/aosp_15_r20/external/executorch/backends/transforms/
H A Daddmm_mm_to_linear.py104 Replace calls to addmm/mm with linear node
116 node.target == ops.aten.mm.default or node.target == ops.aten.addmm.default
119 if node.target == ops.aten.addmm.default:
125 # Skip this node as it appears to be a standalone `addmm`

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