Add trace transform to replace uniform with stateless uniform(uniform…

…_philox) and RNG state query/updating (#114)

thunder/core/prims.py

@@ -251,6 +251,14 @@ class PrimIDs(Enum):
     # Memory access methods
     ITEM = auto()
     COPY_ = auto()
+    SET_SEED = auto()
+    SET_OFFSET=auto()
+    GET_SEED =auto()
+    GET_OFFSET = auto()
+    SET_RNG_STATE = auto()
+    GET_RNG_STATE = auto()
+    UNPACK_RNG_STATE = auto()
+    PACK_RNG_STATE = auto()
 
 
 class OpTags(Enum):
@@ -2451,6 +2459,84 @@ def _uniform_meta(
     tags=(OpTags.RANDOM_OP,),
 )
 
+def _set_seed_meta(s):
+    return None
+def _get_seed_meta():
+    # return numberproxy(int, 0)
+    return TensorProxy(shape=(), device=devices.cpu, dtype=dtypes.int64)
+def _set_offset_meta(s):
+    return None
+def _get_offset_meta():
+    return TensorProxy(shape=(), device=devices.cpu, dtype=dtypes.int64)
+    # return numberproxy(int, 0)
+def _set_rng_state_meta(new_state): # TODO, device
+    return None
+def _get_rng_state_meta(): #TODO device
+    # static const size_t seed_size = sizeof(uint64_t);
+    # static const size_t offset_size = sizeof(int64_t);
+    # static const size_t total_size = seed_size + offset_size;
+
+    # auto state_tensor = at::detail::empty_cpu({(int64_t)total_size}, ScalarType::Byte, c10::nullopt, c10::nullopt, c10::nullopt, c10::nullopt);
+    state_shape = dtypes.int64.bytes//dtypes.uint8.bytes * 2
+    return TensorProxy(shape=(state_shape,), dtype=dtypes.uint8, device=devices.cpu)
+
+set_rng_state = make_prim(
+    PrimIDs.SET_RNG_STATE,
+    "set_rng_state",
+    meta=_set_rng_state_meta,
+    tags=(OpTags.RANDOM_OP, OpTags.DONT_DCE),
+)
+get_rng_state = make_prim(
+    PrimIDs.GET_RNG_STATE,
+    "get_rng_state",
+    meta=_get_rng_state_meta,
+    tags=(OpTags.RANDOM_OP,),
+)
+def _unpack_rng_state_meta(state):
+    return numberproxy(int, 0), numberproxy(int, 0)
+    # return TensorProxy(shape=(), device=devices.cpu, dtype=dtypes.int64), TensorProxy(shape=(), device=devices.cpu, dtype=dtypes.int64)
+
+unpack_rng_state = make_prim(
+    PrimIDs.UNPACK_RNG_STATE,
+    "unpack_rng_state",
+    meta=_unpack_rng_state_meta,
+    tags=(OpTags.RANDOM_OP,),
+)
+def _pack_rng_state_meta(seed, offset):
+    state_shape = dtypes.int64.bytes//dtypes.uint8.bytes * 2
+    return TensorProxy(shape=(state_shape,), dtype=dtypes.uint8, device=devices.cpu)
+
+pack_rng_state = make_prim(
+    PrimIDs.PACK_RNG_STATE,
+    "pack_rng_state",
+    meta=_pack_rng_state_meta,
+    tags=(OpTags.RANDOM_OP,),
+)
+set_seed = make_prim(
+    PrimIDs.SET_SEED,
+    "set_seed",
+    meta=_set_seed_meta,
+    tags=(OpTags.RANDOM_OP,),
+)
+get_seed = make_prim(
+    PrimIDs.GET_SEED,
+    "get_seed",
+    meta=_get_seed_meta,
+    tags=(OpTags.RANDOM_OP,),
+)
+set_offset = make_prim(
+    PrimIDs.SET_OFFSET,
+    "set_offset",
+    meta=_set_offset_meta,
+    tags=(OpTags.RANDOM_OP,OpTags.DONT_DCE),
+)
+get_offset = make_prim(
+    PrimIDs.GET_OFFSET,
+    "get_offset",
+    meta=_get_offset_meta,
+    tags=(OpTags.RANDOM_OP,),
+)
+
 
 def _uniform_philox_meta(
     shape: Sequence[int],

thunder/executors/passes.py

@@ -25,6 +25,63 @@
 comment_symbols = {prims.PrimIDs.COMMENT, prims.PrimIDs.UNPACK_TRIVIAL}
 
 
+def replace_uniform(trace: TraceCtx) -> TraceCtx:
+    start_time_ns = time.time_ns()
+    from thunder.torch import uniform_philox
+
+    swapmap: dict[Variable, Proxy] = {}
+
+    def update_swapmap(o: Any, no: Any) -> None:
+        if isinstance(o, Proxy):
+            check(
+                isinstance(no, Proxy),
+                lambda: f"Expected an execution transform to produce outputs with the same type, but found {type(o)} and {type(no)}",
+            )
+
+            vo = variableify(o)
+            vno = variableify(no)
+            if vo == vno:
+                return
+            swapmap[vno] = o
+
+    def visit_(bsym: BoundSymbol) -> transforms.VISIT_TYPE:
+        import thunder.torch as ltorch
+        if bsym.sym.id == prims.PrimIDs.UNIFORM:
+            rng_state = prims.get_rng_state()
+            print(rng_state.shape, rng_state.dtype)
+            # seed, offset = ltorch.chunk(rng_state, 2)
+            seed, offset = prims.unpack_rng_state(rng_state)
+            # seed = prims.get_seed()
+            # offset = prims.get_offset()
+            out = uniform_philox(*bsym.args,**bsym.kwargs, seed=seed, offset=offset)
+            adv_offs = 4
+            new_offset = prims.add(offset, adv_offs)
+            new_state = prims.pack_rng_state(seed, new_offset)
+            prims.set_rng_state(new_state)
+            safe_map_flat(update_swapmap, bsym.output, out)
+            return transforms.VISIT_TYPE.REPLACE
+        else:
+            return transforms.VISIT_TYPE.NO_OP
+
+    extrace = transforms.visitor_transform(trace, visit_)
+
+    # Restores original variables
+    bound_symbols: list[BoundSymbol] = []
+    for bsym in extrace.bound_symbols:
+        nbsym: BoundSymbol = bsym.from_bsym_swap_proxies(swapmap)
+        bound_symbols.append(nbsym)
+
+    extrace.bound_symbols = bound_symbols
+
+    end_time_ns = time.time_ns()
+    elapsed_time_ns = end_time_ns - start_time_ns
+    elapsed_time_millis = elapsed_time_ns // 1000000
+    extrace.set_provenance(
+        TraceProvenance(f"Transform for replace uniform (took {elapsed_time_millis} milliseconds)")
+    )
+    return extrace
+
+
 # Transforms a trace by determining which execution transforms to call given the list of executors in priority order
 def _transform_for_operator_executor_execution(trace: TraceCtx, executors_list: Sequence[Executor]) -> TraceCtx:
     start_time_ns = time.time_ns()

thunder/executors/torch_autograd.py

@@ -110,6 +110,8 @@ def make_trace(func):
 
     fw_traces = [fw_trace]
     bw_traces = [bw_trace]
+    # print(primal_trace)
+    # print(fw_trace, bw_trace)
 
     from thunder.distributed import FSDPType
 
@@ -137,6 +139,11 @@ def make_trace(func):
         fw_trace = _fsdp_comm_bucketing.apply_bucketing_to_forward_trace(fw_trace, bw_trace.names)
         _fsdp_comm_bucketing.update_name_set(bw_trace)
 
+    print(fw_trace)
+    from thunder.executors.passes import replace_uniform
+    fw_trace = replace_uniform(fw_trace)
+    print(fw_trace)
+
     # Now we can run the optimization passes on the forward trace
     # TODO Restore request for no rematerialization
     fw_extrace = transform_for_execution(

thunder/executors/torchex.py

@@ -427,6 +427,86 @@ def _tensor_from_sequence_prims_transform(
     return tensor_from_sequence(seq_or_number, device=torch_device, dtype=torch_dtype)
 
 
+
+def _get_seed_prim_impl():
+    cuda_generator = torch.cuda.default_generators[torch.cuda.current_device()]
+    return torch.tensor(cuda_generator.initial_seed())
+    # print("++++: ",cuda_generator.initial_seed())
+    # return cuda_generator.initial_seed()
+get_seed_prim_impl = ex.register_operator(
+    "get_seed_prim_impl", meta=prims.get_seed.meta, fn=_get_seed_prim_impl
+)
+_register_implementation(prims.get_seed, get_seed_prim_impl, checker=_always_executable, )
+
+def _get_offset_prim_impl():
+    cuda_generator = torch.cuda.default_generators[torch.cuda.current_device()]
+    return torch.tensor(cuda_generator.get_offset())
+    # return cuda_generator.get_offset()
+get_offset_prim_impl = ex.register_operator(
+    "get_offset_prim_impl", meta=prims.get_offset.meta, fn=_get_offset_prim_impl
+)
+_register_implementation(prims.get_offset, get_offset_prim_impl, checker=_always_executable, )
+
+
+def _set_seed_prim_impl(s):
+    cuda_generator = torch.cuda.default_generators[torch.cuda.current_device()]
+    return cuda_generator.manual_seed(s)
+set_seed_prim_impl = ex.register_operator(
+    "set_seed_prim_impl", meta=prims.set_seed.meta, fn=_set_seed_prim_impl, tags=(prims.OpTags.RANDOM_OP,prims.OpTags.DONT_DCE)
+)
+_register_implementation(prims.set_seed, set_seed_prim_impl, checker=_always_executable, )
+
+
+def _set_offset_prim_impl(s):
+    cuda_generator = torch.cuda.default_generators[torch.cuda.current_device()]
+    cuda_generator.set_offset(s.item())
+set_offset_prim_impl = ex.register_operator(
+    "set_offset_prim_impl", meta=prims.set_offset.meta, fn=_set_offset_prim_impl, tags=(prims.OpTags.RANDOM_OP,prims.OpTags.DONT_DCE)
+)
+_register_implementation(prims.set_offset, set_offset_prim_impl, checker=_always_executable, )
+
+def _set_rng_state_prim_impl(s):
+    cuda_generator = torch.cuda.default_generators[torch.cuda.current_device()]
+    cuda_generator.set_state(s)
+set_rng_state_prim_impl = ex.register_operator(
+    "set_rng_state_prim_impl", meta=prims.set_rng_state.meta, fn=_set_rng_state_prim_impl, tags=(prims.OpTags.RANDOM_OP,prims.OpTags.DONT_DCE)
+)
+_register_implementation(prims.set_rng_state, set_rng_state_prim_impl, checker=_always_executable, )
+
+def _get_rng_state_prim_impl():
+    cuda_generator = torch.cuda.default_generators[torch.cuda.current_device()]
+    return cuda_generator.get_state()
+    # return cuda_generator.graphsafe_get_state()
+get_rng_state_prim_impl = ex.register_operator(
+    "get_rng_state_prim_impl", meta=prims.get_rng_state.meta, fn=_get_rng_state_prim_impl, tags=(prims.OpTags.RANDOM_OP,prims.OpTags.DONT_DCE)
+)
+_register_implementation(prims.get_rng_state, get_rng_state_prim_impl, checker=_always_executable, )
+
+def _unpack_rng_state_prim_impl(s):
+    seed, offset = torch.chunk(s, 2)
+    # return seed.view(torch.int64), offset.view(torch.int64)
+    return seed.view(torch.int64).item(), offset.view(torch.int64).item()
+unpack_rng_state_prim_impl = ex.register_operator(
+    "unpack_rng_state_prim_impl", meta=prims.unpack_rng_state.meta, fn=_unpack_rng_state_prim_impl, tags=(prims.OpTags.RANDOM_OP,prims.OpTags.DONT_DCE)
+)
+_register_implementation(prims.unpack_rng_state, unpack_rng_state_prim_impl, checker=_always_executable, )
+
+def _pack_rng_state_prim_impl(seed, offset):
+    seed = torch.tensor(seed)
+    offset = torch.tensor(offset)
+    seed_portion = seed.reshape([1]).view(torch.uint8)
+    offset_portion = offset.reshape([1]).view(torch.uint8)
+    new_state = torch.cat([seed_portion, offset_portion])
+    return new_state
+pack_rng_state_prim_impl = ex.register_operator(
+    "pack_rng_state_prim_impl", meta=prims.pack_rng_state.meta, fn=_pack_rng_state_prim_impl, tags=(prims.OpTags.RANDOM_OP,prims.OpTags.DONT_DCE)
+)
+_register_implementation(prims.pack_rng_state, pack_rng_state_prim_impl, checker=_always_executable, )
+
+
+
+
+
 _register_implementation(prims.full, checker=_always_executable, execution_transform=_full_transform)
 _register_implementation(prims.iota, checker=_always_executable, execution_transform=_iota_transform)
 _register_implementation(prims.uniform, checker=_always_executable, execution_transform=_uniform_transform)

thunder/tests/test_randomness.py

@@ -22,6 +22,64 @@ def func(shape, dtype, device, rng_seed, rng_offset):
 
     cf = lc_compile(func, disable_preprocessing=True, executors_list=executor.executors_list())
 
+    import torch
+    rng_seed_tensor = torch.tensor(rng_seed)
+    rng_offset_tensor = torch.tensor(rng_offset)
+    # outputs = [cf(shape, dtype, device, rng_seed_tensor, rng_offset_tensor) for _ in range(3)]
     outputs = [cf(shape, dtype, device, rng_seed, rng_offset) for _ in range(3)]
+    import thunder
+    print(thunder.last_traces(cf)[-1])
+    # print(thunder.last_backward_traces(cf)[-1])
     for o in outputs:
         assert_close(o, outputs[0])
+
+
+@instantiate(
+    dtypes=(dtypes.float32, dtypes.float16, dtypes.float64),
+    devicetypes=(devices.DeviceType.CUDA,),
+)
+def test_rng_state_uniform_philox(executor, device: str, dtype: dtypes.dtype):
+    import torch
+    import thunder
+    def func(a):
+        b = thunder.torch.uniform_like(a, device=a.device, dtype=a.dtype)
+        # b = torch.nn.functional.dropout(a, p=0.5)
+        c = thunder.torch.uniform_like(a, device=a.device, dtype=a.dtype)
+        # b = torch.uniform(a.shape, device=a.device, dtype=a.dtype)
+        return c*b
+
+    cuda_generator = torch.cuda.default_generators[torch.cuda.current_device()]
+
+    a = torch.randn(2, 2, device="cuda", requires_grad=True)
+    a1 = a.detach().clone()
+    a1.requires_grad_()
+
+    jfunc = thunder.jit(func)
+    cuda_generator.manual_seed(20)
+    expects = []
+    for _ in range(4):
+        out = jfunc(a)
+        print("b: ", out)
+        out.sum().backward()
+        print(a.grad)
+        expects.append(out)
+        expects.append(a.grad)
+    print("------------------")
+
+    results = []
+    cuda_generator.manual_seed(20)
+
+    # a = torch.randn(2, 2, device="cuda", requires_grad=True)
+    print(a1)
+    for _ in range(4):
+        out = jfunc(a1)
+        print("b: ", out)
+        out.sum().backward()
+        print(a1.grad)
+        results.append(out)
+        results.append(a1.grad)
+
+    print(thunder.last_traces(jfunc)[-1])
+    print(thunder.last_backward_traces(jfunc)[-1])
+    for expected, result in zip(expects, results):
+        assert_close(expected, result)