| # Owner(s): ["module: dynamo"] |
| import functools |
| import unittest |
| from unittest.mock import patch |
| import torch |
| from torch._C import FileCheck |
| # for some reason importing functional collectives after dynamo breaks collectives handling! |
| import torch.distributed._functional_collectives as _functional_collectives |
| import torch._dynamo |
| import torch._dynamo.test_case |
| from torch._dynamo.utils import same |
| from torch._dynamo.testing import CompileCounter |
| from torch.distributed.distributed_c10d import GroupMember |
| from torch.fx.experimental.proxy_tensor import make_fx |
| from torch.testing._internal.common_distributed import ( |
| DynamoDistributedSingleProcTestCase, |
| DynamoDistributedMultiProcTestCase, |
| _dynamo_dist_per_rank_init, |
| requires_nccl, |
| skip_if_lt_x_gpu, |
| ) |
| from torch._inductor.compile_fx import compile_fx as inductor_compile_fx |
| from torch._inductor.utils import has_triton, run_and_get_triton_code |
| import torch._dynamo.logging |
| |
| @requires_nccl() |
| class TestCollectivesMultiProc(DynamoDistributedMultiProcTestCase): |
| """ |
| Run correctness checks in multi-proc runner, mark with minimum # GPUs to run under |
| """ |
| def get_world_trs(self): |
| return { |
| "tag": "", |
| "ranks": list(range(self.world_size)), |
| "group_size": self.world_size, |
| } |
| |
| @property |
| def world_size(self) -> int: |
| # hack: no matter whether we have 2 or 3 or 4 gpus, just run on 2 |
| # works around issue with skipif<2 and workers with unpredictable #s gpu |
| return 2 |
| |
| @unittest.skipIf(not has_triton(), "Inductor+gpu needs triton and recent GPU arch") |
| @skip_if_lt_x_gpu(2) |
| # TODO: somehow inductor bg compile threads are causing hangs at exit with distributed work dtor |
| @patch.object(torch._inductor.config, "compile_threads", 1) |
| def test_allreduce_inductor(self): |
| """ |
| This is matmul/cat/allreduce is a pattern we aim to optimize. |
| """ |
| |
| def matmul_cat_col(a, b, c, d, e, f, *, tag, ranks, group_size): |
| x = torch.matmul(a, b) |
| y = torch.matmul(c, d) |
| z = torch.cat((x, y)) |
| ar = torch.ops.c10d_functional.all_reduce(z, "sum", tag, ranks, group_size) |
| g = torch.matmul(e, f) |
| ar = torch.ops.c10d_functional.wait_tensor(ar) |
| out = torch.add(ar, g.repeat(2, 1)) |
| return (out, ) |
| |
| def compile(func, example_inputs): |
| graph = make_fx(func)(*example_inputs) |
| return inductor_compile_fx(graph, example_inputs) |
| |
| with _dynamo_dist_per_rank_init(self.rank, self.world_size): |
| |
| matmul_cat_col = functools.partial( |
| matmul_cat_col, |
| **self.get_world_trs(), |
| ) |
| inputs = (torch.ones(4, 4, device="cuda") + self.rank,) * 6 |
| |
| eager_out = matmul_cat_col(*inputs) |
| compiled_matmul_cat_col = compile(matmul_cat_col, inputs) |
| inductor_out = compiled_matmul_cat_col(*inputs) |
| self.assertTrue(same(eager_out, inductor_out, tol=0.001)) |
| |
| @unittest.skipIf(not has_triton(), "Inductor+gpu needs triton and recent GPU arch") |
| @skip_if_lt_x_gpu(2) |
| # TODO: somehow inductor bg compile threads are causing hangs at exit with distributed work dtor |
| @patch.object(torch._inductor.config, "compile_threads", 1) |
| def test_eager_allreduce_inductor_wait(self): |
| |
| def eager_func(a, b, c, d, *, tag, ranks, group_size): |
| x = torch.matmul(a, b) |
| y = torch.matmul(c, d) |
| z = torch.cat((x, y)) |
| ar = torch.ops.c10d_functional.all_reduce(z, "sum", tag, ranks, group_size) |
| return ar |
| |
| def inductor_func(ar, e, f): |
| g = torch.matmul(e, f) |
| ar = torch.ops.c10d_functional.wait_tensor(ar) |
| out = torch.add(ar, g.repeat(2, 1)) |
| return (out, ) |
| |
| def compile(func, example_inputs): |
| graph = make_fx(func)(*example_inputs) |
| return inductor_compile_fx(graph, example_inputs) |
| |
| with _dynamo_dist_per_rank_init(self.rank, self.world_size): |
| |
| eager_func = functools.partial( |
| eager_func, |
| **self.get_world_trs(), |
| ) |
| eager_inputs = (torch.ones(4, 4, device="cuda") + self.rank,) * 4 |
| inductor_inputs = (torch.ones(4, 4, device="cuda") + self.rank,) * 2 |
| |
| eager_out = inductor_func(eager_func(*eager_inputs), *inductor_inputs) |
| compiled_inductor_func = compile(inductor_func, [eager_func(*eager_inputs)] + list(inductor_inputs)) |
| inductor_out = compiled_inductor_func(eager_func(*eager_inputs), *inductor_inputs) |
| print(f"eager_out, {eager_out}") |
| print(f"inductor_out, {inductor_out}") |
| self.assertTrue(same(eager_out, inductor_out, tol=0.001)) |
| |
| @unittest.skipIf(not has_triton(), "Inductor+gpu needs triton and recent GPU arch") |
| @skip_if_lt_x_gpu(2) |
| # TODO: somehow inductor bg compile threads are causing hangs at exit with distributed work dtor |
| @patch.object(torch._inductor.config, "compile_threads", 1) |
| def test_inductor_allreduce_eager_wait(self): |
| |
| def inductor_func(a, b, c, d, *, tag, ranks, group_size): |
| x = torch.matmul(a, b) |
| y = torch.matmul(c, d) |
| z = torch.cat((x, y)) |
| ar = torch.ops.c10d_functional.all_reduce(z, "sum", tag, ranks, group_size) |
| return ar |
| |
| def eager_func(ar, e, f): |
| g = torch.matmul(e, f) |
| ar = torch.ops.c10d_functional.wait_tensor(ar) |
| out = torch.add(ar, g.repeat(2, 1)) |
| return (out, ) |
| |
| def compile(func, example_inputs): |
| graph = make_fx(func)(*example_inputs) |
| return inductor_compile_fx(graph, example_inputs) |
| |
| with _dynamo_dist_per_rank_init(self.rank, self.world_size): |
| |
| inductor_func = functools.partial( |
| inductor_func, |
| **self.get_world_trs(), |
| ) |
| inductor_inputs = (torch.ones(4, 4, device="cuda") + self.rank,) * 4 |
| eager_inputs = (torch.ones(4, 4, device="cuda") + self.rank,) * 2 |
| |
| eager_out = eager_func(inductor_func(*inductor_inputs), *eager_inputs) |
| compiled_inductor_func = compile(inductor_func, inductor_inputs) |
| inductor_out = eager_func(compiled_inductor_func(*inductor_inputs), *eager_inputs) |
| self.assertTrue(same(eager_out, inductor_out, tol=0.001)) |
| |
| @unittest.skipIf(not has_triton(), "Inductor+gpu needs triton and recent GPU arch") |
| @skip_if_lt_x_gpu(2) |
| # TODO: somehow inductor bg compile threads are causing hangs at exit with distributed work dtor |
| @patch.object(torch._inductor.config, "compile_threads", 1) |
| def test_allgather_into_tensor_inductor(self): |
| """ |
| This is matmul/cat/allreduce is a pattern we aim to optimize. |
| """ |
| |
| def example(a, b, *, tag, ranks, group_size): |
| c = torch.matmul(a, b) |
| ag = torch.ops.c10d_functional.all_gather_into_tensor(c, tag, ranks, group_size) |
| ag = torch.ops.c10d_functional.wait_tensor(ag) |
| return (ag, ) |
| |
| def compile(func, example_inputs): |
| graph = make_fx(func)(*example_inputs) |
| return inductor_compile_fx(graph, example_inputs) |
| |
| with _dynamo_dist_per_rank_init(self.rank, self.world_size): |
| |
| example = functools.partial( |
| example, |
| **self.get_world_trs(), |
| ) |
| inputs = (torch.ones(4, 4, device="cuda") + self.rank,) * 2 |
| |
| eager_out = example(*inputs) |
| compiled_matmul_cat_col = compile(example, inputs) |
| inductor_out = compiled_matmul_cat_col(*inputs) |
| self.assertTrue(same(eager_out, inductor_out, tol=0.001)) |
| |
| @unittest.skipIf(not has_triton(), "Inductor+gpu needs triton and recent GPU arch") |
| @skip_if_lt_x_gpu(2) |
| # TODO: somehow inductor bg compile threads are causing hangs at exit with distributed work dtor |
| @patch.object(torch._inductor.config, "compile_threads", 1) |
| def test_reduce_scatter_tensor_inductor(self): |
| def example(a, b, *, tag, ranks, group_size): |
| c = torch.matmul(a, b) |
| ag = torch.ops.c10d_functional.reduce_scatter_tensor( |
| c, "sum", tag, ranks, group_size |
| ) |
| ag = torch.ops.c10d_functional.wait_tensor(ag) |
| return (ag,) |
| |
| def compile(func, example_inputs): |
| graph = make_fx(func)(*example_inputs) |
| return inductor_compile_fx(graph, example_inputs) |
| |
| with _dynamo_dist_per_rank_init(self.rank, self.world_size): |
| example = functools.partial( |
| example, |
| **self.get_world_trs(), |
| ) |
| inputs = (torch.ones(4, 4, device="cuda") + self.rank,) * 2 |
| |
| eager_out = example(*inputs) |
| compiled_fn = compile(example, inputs) |
| inductor_out = compiled_fn(*inputs) |
| self.assertTrue(same(eager_out, inductor_out, tol=0.001)) |
| |
| |
| @requires_nccl() |
| class TestCollectivesInductor(DynamoDistributedSingleProcTestCase): |
| """ |
| Prefer single-proc test runner for basic tests as it is easier to work with. |
| """ |
| def get_world_trs(self, world_size=1): |
| return { |
| "tag": "", |
| "ranks": list(range(world_size)), |
| "group_size": world_size, |
| } |
| |
| @unittest.skipIf(not has_triton(), "Inductor+gpu needs triton and recent GPU arch") |
| def test_inductor_single_op(self): |
| torch._inductor.config.debug = True |
| |
| def func(inp, *, tag, ranks, group_size): |
| ar = torch.ops.c10d_functional.all_reduce(inp, "sum", tag, ranks, group_size) |
| ar = torch.ops.c10d_functional.wait_tensor(ar) |
| return ar |
| |
| inputs = torch.ones(4, 4, device="cuda") |
| |
| compiled = torch.compile(func) |
| out = compiled(inputs, **self.get_world_trs()) |
| code = run_and_get_triton_code(compiled, inputs, **self.get_world_trs()) |
| FileCheck() \ |
| .check("buf0 = empty_strided") \ |
| .check("buf0.copy_(arg0_1)") \ |
| .check("buf1 = buf0") \ |
| .check("buf1_work = dist.all_reduce(buf1") \ |
| .check("fun_col_impl._register_tensor_work(buf1, buf1_work)") \ |
| .check("_wait_tensor(buf0)") \ |
| .check("return (buf2, )") \ |
| .run(code) |
| correct = func(inputs, **self.get_world_trs()) |
| self.assertTrue(same(out, correct)) |
| |
| @unittest.skipIf(not has_triton(), "Inductor+gpu needs triton and recent GPU arch") |
| def test_inductor_steal_buffer(self): |
| """ |
| it's ok and optimal if inductor allreduce mutates the buffer of an intermediate |
| that isn't going to be used again |
| """ |
| torch._inductor.config.debug = True |
| |
| def func(inp, *, tag, ranks, group_size): |
| x = inp + 1 |
| ar = torch.ops.c10d_functional.all_reduce(x, "sum", tag, ranks, group_size) |
| ar = torch.ops.c10d_functional.wait_tensor(ar) |
| # ensure other is not incorrectly aliasing ar's buffer |
| other = torch.ones_like(inp) + 22 |
| return ar, other |
| |
| inputs = torch.ones(4, 4, device="cuda") |
| |
| compiled = torch.compile(func) |
| code = run_and_get_triton_code(compiled, inputs, **self.get_world_trs()) |
| FileCheck() \ |
| .check("buf1 = buf0; del buf0 # reuse") \ |
| .check_not("buf1.copy_(") \ |
| .check("buf2 = buf1") \ |
| .check("buf2_work = dist.all_reduce(buf2") \ |
| .check("fun_col_impl._register_tensor_work(buf2, buf2_work)") \ |
| .check("_wait_tensor(buf1)") \ |
| .check("buf3 = buf1") \ |
| .check("buf4 = empty_strided") \ |
| .check("return (buf3, buf4") \ |
| .run(code) |
| out = compiled(inputs, **self.get_world_trs()) |
| correct = func(inputs, **self.get_world_trs()) |
| self.assertTrue(same(out, correct)) |
| |
| @unittest.skipIf(not has_triton(), "Inductor+gpu needs triton and recent GPU arch") |
| @patch.object(torch._inductor.config.triton, "descriptive_names", False) |
| def test_inductor_doesnt_mutate_shared(self): |
| """ |
| make sure that an intermediate that's going to be reuse isn't mutated unless copied |
| """ |
| torch._inductor.config.debug = True |
| |
| def func(inp, *, tag, ranks, group_size): |
| x = inp + 1 |
| ar = torch.ops.c10d_functional.all_reduce(x, "sum", tag, ranks, group_size) |
| y = x + 2 |
| ar = torch.ops.c10d_functional.wait_tensor(ar) |
| # ensure other is not incorrectly aliasing ar's buffer |
| other = torch.ones_like(inp) + 22 |
| return ar, y, other |
| |
| inputs = torch.ones(4, 4, device="cuda") |
| |
| compiled = torch.compile(func) |
| code = run_and_get_triton_code(compiled, inputs, **self.get_world_trs()) |
| FileCheck() \ |
| .check("buf0 = empty_strided(") \ |
| .check("buf4 = empty_strided") \ |
| .check("triton_poi__0.run(arg0_1, buf0, buf4") \ |
| .check_not("copy_(") \ |
| .check("buf1 = buf0; del buf0 # reuse") \ |
| .check("buf2 = buf1") \ |
| .check("buf2_work = dist.all_reduce(buf2") \ |
| .check("fun_col_impl._register_tensor_work(buf2, buf2_work)") \ |
| .check("_wait_tensor(buf1)") \ |
| .check("buf3 = buf1") \ |
| .check("return (buf3, buf4, buf5") \ |
| .run(code) |
| out = compiled(inputs, **self.get_world_trs()) |
| correct = func(inputs, **self.get_world_trs()) |
| self.assertTrue(same(out, correct)) |
| |
| def test_dynamo_trace_allreduce(self): |
| |
| def func(inp, *, tag, ranks, group_size): |
| ar = _functional_collectives.all_reduce(inp, "sum", ranks, tag) |
| return ar |
| |
| inputs = torch.ones(4, 4, device="cuda") |
| counter = CompileCounter() |
| compiled = torch.compile(func, backend=counter) |
| out = compiled(inputs, **self.get_world_trs()) |
| correct = func(inputs, **self.get_world_trs()) |
| self.assertEqual(counter.frame_count, 1) |
| |
| # should test more precisely, but the 2 is supposed to be (all_reduce, wait) |
| self.assertEqual(counter.op_count, 2) |
| self.assertTrue(same(out, correct)) |
| |
| def test_dynamo_trace_all_gather_tensor(self): |
| |
| def func(inp, *, tag, ranks, group_size): |
| ar = _functional_collectives.all_gather_tensor(inp, 0, ranks, tag) |
| return ar |
| |
| inputs = torch.ones(4, 4, device="cuda") |
| counter = CompileCounter() |
| compiled = torch.compile(func, backend=counter) |
| out = compiled(inputs, **self.get_world_trs()) |
| correct = func(inputs, **self.get_world_trs()) |
| self.assertEqual(counter.frame_count, 1) |
| |
| # should test more precisely, but the 2 is supposed to be (all_gather, wait) |
| self.assertEqual(counter.op_count, 2) |
| self.assertTrue(same(out, correct)) |
| |
| def test_dynamo_trace_all_gather_tensor_pg(self): |
| |
| def func(inp, *, pg): |
| ar = _functional_collectives.all_gather_tensor(inp, 0, pg) |
| return ar |
| |
| inputs = torch.ones(4, 4, device=self.device) |
| counter = CompileCounter() |
| compiled = torch.compile(func, backend=counter, fullgraph=True) |
| out = compiled(inputs, pg=GroupMember.WORLD) |
| correct = func(inputs, pg=GroupMember.WORLD) |
| self.assertEqual(counter.frame_count, 1) |
| |
| # should test more precisely, but the 2 is supposed to be (all_gather, wait) |
| self.assertEqual(counter.op_count, 2) |
| self.assertTrue(same(out, correct)) |
| |
| def test_dynamo_rewrite_dist_all_gather(self): |
| |
| def func(inp, out, *, pg): |
| torch.distributed.all_gather_into_tensor( |
| out, |
| inp, |
| pg, |
| ) |
| local_size = [4, 4] |
| # single-proc test |
| global_size = local_size |
| |
| inputs = torch.ones(local_size, device=self.device) |
| outputs = torch.empty(global_size, device=self.device) |
| correct_outputs = torch.empty(global_size, device=self.device) |
| counter = CompileCounter() |
| compiled = torch.compile(func, backend=counter, fullgraph=True) |
| compiled(inputs, outputs, pg=GroupMember.WORLD) |
| func(inputs, correct_outputs, pg=GroupMember.WORLD) |
| assert counter.frame_count == 1 |
| |
| # should test more precisely, but the 3 is supposed to be (all_gather, wait, copy_) |
| assert counter.op_count == 3 |
| assert same(outputs, correct_outputs) |
| |
| def test_dynamo_rewrite_dist_reduce_scatter(self): |
| |
| def func(inp, out, *, pg): |
| torch.distributed.reduce_scatter_tensor( |
| out, |
| inp, |
| group=pg, |
| ) |
| local_size = [4, 4] |
| # single-proc test |
| global_size = local_size |
| |
| inputs = torch.ones(local_size, device=self.device) |
| outputs = torch.empty(global_size, device=self.device) |
| correct_outputs = torch.empty(global_size, device=self.device) |
| counter = CompileCounter() |
| compiled = torch.compile(func, backend=counter, fullgraph=True) |
| compiled(inputs, outputs, pg=GroupMember.WORLD) |
| func(inputs, correct_outputs, pg=GroupMember.WORLD) |
| assert counter.frame_count == 1 |
| |
| # should test more precisely, but the 3 is supposed to be (reduce_scatter, wait, copy_) |
| assert counter.op_count == 3 |
| assert same(outputs, correct_outputs) |
| |
| def test_dynamo_graphbreaks_unsupported_async_op(self): |
| |
| def func(inp, out, *, pg): |
| work = torch.distributed.reduce_scatter_tensor( |
| out, |
| inp, |
| group=pg, |
| async_op=True |
| ) |
| work.wait() |
| local_size = [4, 4] |
| # single-proc test |
| global_size = local_size |
| |
| inputs = torch.ones(local_size, device=self.device) |
| outputs = torch.empty(global_size, device=self.device) |
| correct_outputs = torch.empty(global_size, device=self.device) |
| counter = CompileCounter() |
| compiled = torch.compile(func, backend=counter) |
| compiled(inputs, outputs, pg=GroupMember.WORLD) |
| func(inputs, correct_outputs, pg=GroupMember.WORLD) |
| assert counter.frame_count == 0 |
| assert counter.op_count == 0 |
| assert same(outputs, correct_outputs) |
| |
| def test_dynamo_pg_var(self): |
| def func(inp, *, pg): |
| x = pg.rank() + 1 % pg.size() |
| return inp + x |
| |
| local_size = [4, 4] |
| inputs = torch.ones(local_size, device=self.device) |
| correct_outputs = torch.empty(local_size, device=self.device) |
| counter = CompileCounter() |
| compiled = torch.compile(func, backend=counter, fullgraph=True) |
| outputs = compiled(inputs, pg=GroupMember.WORLD) |
| correct_outputs = func(inputs, pg=GroupMember.WORLD) |
| assert counter.frame_count == 1 |
| assert counter.op_count == 1 |
| assert same(outputs, correct_outputs) |
| |
| def test_dynamo_trace_reduce_scatter_tensor(self): |
| |
| def func(inp, *, tag, ranks, group_size): |
| ar = _functional_collectives.reduce_scatter_tensor(inp, "sum", 0, ranks, tag) |
| return ar |
| |
| inputs = torch.ones(4, 4, device="cuda") |
| counter = CompileCounter() |
| compiled = torch.compile(func, backend=counter) |
| out = compiled(inputs, **self.get_world_trs()) |
| correct = func(inputs, **self.get_world_trs()) |
| self.assertEqual(counter.frame_count, 1) |
| |
| # should test more precisely, but the 2 is supposed to be (reduce_scatter, wait) |
| self.assertEqual(counter.op_count, 2) |
| self.assertTrue(same(out, correct)) |
| |
| def test_dynamo_trace_allgather_coalesced(self): |
| def func(inp, *, tag, ranks, group_size): |
| ar = torch.ops.c10d_functional.all_gather_into_tensor_coalesced(inp, tag, ranks, group_size) |
| return ar |
| |
| inputs = [torch.ones(4, 4, device="cuda"), torch.ones(6, 6, device="cuda")] |
| counter = CompileCounter() |
| compiled = torch.compile(func, backend=counter) |
| out = compiled(inputs, **self.get_world_trs()) |
| correct = func(inputs, **self.get_world_trs()) |
| assert counter.frame_count == 1 |
| assert counter.op_count == 3 # It generates 2 getattr to unpack the array |
| assert same(out, correct) |
| |
| |
| def test_backwards(self): |
| """ |
| It's probably not that common to need backwards support for collectives. |
| |
| However, I wanted to at least see if it was possible to support it as a design goal. |
| """ |
| def func(inp, *, tag, ranks, group_size): |
| ar = _functional_collectives.all_reduce(inp, "sum", ranks, tag) |
| return ar |
| |
| input = torch.ones(4, 4, device="cuda", requires_grad=True) |
| # TODO implement backwards |
| with self.assertRaisesRegex(RuntimeError, "element 0 of tensors does not require grad and does not have a grad_fn"): |
| compiled = torch.compile(func, backend="aot_eager") # inductor bug with single-op allreduce graph |
| out = compiled(input, **self.get_world_trs()) |
| out.sum().backward() |
| |
| correct_input = input.clone().detach().requires_grad_() |
| correct = func(correct_input, **self.get_world_trs()) |
| correct.sum().backward() |
| self.assertTrue(same(out, correct)) |
| self.assertTrue(same(input.grad, correct_input.grad)) |
| |
| def test_meta(self): |
| x = torch.rand((2, 3, 4), device="meta") |
| out = torch.ops.c10d_functional.all_reduce(x, "sum", **self.get_world_trs()) |
| self.assertEqual(x.size(), out.size()) |
| |
| @unittest.skipIf(not has_triton(), "Inductor+gpu needs triton and recent GPU arch") |
| @patch.object(torch._inductor.config.triton, "descriptive_names", False) |
| def test_inductor_all_gather_coalesced(self): |
| """ |
| make sure that an intermediate that's going to be reuse isn't mutated unless copied |
| """ |
| torch._inductor.config.debug = True |
| |
| def func(inp, *, tag, ranks, group_size): |
| x = inp + 1 |
| tensor_list = torch.ops.c10d_functional.all_gather_into_tensor_coalesced([x, inp], tag, ranks, group_size) |
| y = x + 2 |
| ar0 = torch.ops.c10d_functional.wait_tensor(tensor_list[0]) |
| ar1 = torch.ops.c10d_functional.wait_tensor(tensor_list[1]) |
| # ensure other is not incorrectly aliasing ar's buffer |
| other = torch.ones_like(inp) + 22 |
| return ar0, y, other, ar1 |
| |
| inputs = torch.ones(4, 4, device="cuda") |
| |
| compiled = torch.compile(func) |
| code = run_and_get_triton_code(compiled, inputs, **self.get_world_trs()) |
| FileCheck() \ |
| .check("buf0 = empty_strided(") \ |
| .check("buf5 = empty_strided(") \ |
| .check("triton_poi__0.run(arg0_1, buf0, buf5") \ |
| .check("buf1 = empty_strided") \ |
| .check("buf2 = empty_strided") \ |
| .check_not("copy_(") \ |
| .check("buf3_inputs = [buf0,arg0_1]") \ |
| .check("buf3 = [buf1,buf2]") \ |
| .check("buf3_work = fun_col_impl._all_gather_into_tensor_coalesced_fallback(" |
| "output_tensors=buf3, input_tensors=buf3_inputs") \ |
| .check("fun_col_impl._register_tensor_work(buf3, buf3_work)") \ |
| .check("_wait_tensor(buf1)") \ |
| .check("buf4 = buf1") \ |
| .check("buf6 = buf0; del buf0 # reuse") \ |
| .check("_wait_tensor(buf2)") \ |
| .check("buf7 = buf2") \ |
| .check("return (buf4, buf5, buf6, buf7") \ |
| .run(code) |
| out = compiled(inputs, **self.get_world_trs()) |
| correct = func(inputs, **self.get_world_trs()) |
| assert same(out, correct), f"{out} va {correct}" |
| |
| @unittest.skipIf(not has_triton(), "Inductor+gpu needs triton and recent GPU arch") |
| @patch.object(torch._inductor.config.triton, "descriptive_names", False) |
| def test_inductor_reduce_scatter_coalesced(self): |
| """ |
| make sure that an intermediate that's going to be reuse isn't mutated unless copied |
| """ |
| torch._inductor.config.debug = True |
| |
| def func(inp, *, tag, ranks, group_size): |
| x = inp + 1 |
| tensor_list = torch.ops.c10d_functional.reduce_scatter_tensor_coalesced([x, inp], "sum", tag, ranks, group_size) |
| y = x + 2 |
| ar0 = torch.ops.c10d_functional.wait_tensor(tensor_list[0]) |
| ar1 = torch.ops.c10d_functional.wait_tensor(tensor_list[1]) |
| # ensure other is not incorrectly aliasing ar's buffer |
| other = torch.ones_like(inp) + 22 |
| return ar0, y, other, ar1 |
| |
| inputs = torch.ones(4, 4, device="cuda") |
| |
| compiled = torch.compile(func) |
| code = run_and_get_triton_code(compiled, inputs, **self.get_world_trs()) |
| FileCheck() \ |
| .check("buf0 = empty_strided(") \ |
| .check("buf5 = empty_strided(") \ |
| .check("triton_poi__0.run(arg0_1, buf0, buf5") \ |
| .check("buf1 = empty_strided") \ |
| .check("buf2 = empty_strided") \ |
| .check_not("copy_(") \ |
| .check("buf3 = [buf1,buf2]") \ |
| .check("buf3_work = fun_col_impl._reduce_scatter_tensor_coalesced_fallback(" |
| "output_tensors=buf3, input_tensors=buf3_inputs") \ |
| .check("fun_col_impl._register_tensor_work(buf3, buf3_work)") \ |
| .check("_wait_tensor(buf1)") \ |
| .check("buf4 = buf1") \ |
| .check("buf6 = buf0; del buf0 # reuse") \ |
| .check("_wait_tensor(buf2)") \ |
| .check("buf7 = buf2") \ |
| .check("return (buf4, buf5, buf6, buf7") \ |
| .run(code) |
| out = compiled(inputs, **self.get_world_trs()) |
| correct = func(inputs, **self.get_world_trs()) |
| assert same(out, correct), f"{out} va {correct}" |
| |
| |
| if __name__ == "__main__": |
| from torch._dynamo.test_case import run_tests |
| run_tests() |
