Make `Adam`, `AdamW` work with nonzero-dim Tensor betas by zeshengzong · Pull Request #149939 · pytorch/pytorch

zeshengzong · 2025-03-25T07:16:09Z

Changes

Convert tensor betas using _to_scalar
Change annotation of betas param
Change param type in docs

Test Result

pytest -s test/test_optim.py -k test_tensor_lr -vv

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torch/testing/_internal/common_optimizers.py

zeshengzong · 2025-05-14T02:18:40Z

torch/testing/_internal/common_optimizers.py

+                "betas": (torch.tensor([[[0.9]]]), torch.tensor([[0.99]])),
+                "amsgrad": False,
+                "capturable": True,
+                "fused": True,


Hi @janeyx99 , looks like fused=True failed with complex type due to backend not supported yet, is there a way to control input type in test and skip complex type? Thanks

Failed in here

pytorch/torch/optim/optimizer.py

Lines 195 to 198 in d51bc27

if not (p.device.type in fused_supported_devices and torch.is_floating_point(p)):

raise RuntimeError(

"`fused=True` requires all the params to be floating point Tensors of "

f"supported devices: {fused_supported_devices} but {p.dtype} and {p.device.type}"

pytest -s test/test_optim.py -k test_complex_2d_AdamW_cuda_complex64 ______________________________________________________________________________________________ TestOptimRenewedCUDA.test_complex_2d_AdamW_cuda_complex64 _______________________________________________________________________________________________ Traceback (most recent call last): File "/home/zong/miniconda3/envs/torch/lib/python3.12/unittest/case.py", line 58, in testPartExecutor yield File "/home/zong/miniconda3/envs/torch/lib/python3.12/unittest/case.py", line 634, in run self._callTestMethod(testMethod) File "/home/zong/miniconda3/envs/torch/lib/python3.12/unittest/case.py", line 589, in _callTestMethod if method() is not None: ^^^^^^^^ File "/home/zong/code/pytorch/torch/testing/_internal/common_utils.py", line 3154, in wrapper method(*args, **kwargs) File "/home/zong/code/pytorch/torch/testing/_internal/common_utils.py", line 3154, in wrapper method(*args, **kwargs) File "/home/zong/code/pytorch/torch/testing/_internal/common_device_type.py", line 446, in instantiated_test raise rte File "/home/zong/code/pytorch/torch/testing/_internal/common_device_type.py", line 426, in instantiated_test result = test(self, **param_kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/zong/code/pytorch/torch/testing/_internal/common_utils.py", line 1612, in wrapper fn(*args, **kwargs) File "/home/zong/code/pytorch/torch/testing/_internal/common_optimizers.py", line 226, in test_wrapper return test(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/home/zong/code/pytorch/torch/testing/_internal/common_device_type.py", line 1215, in dep_fn return fn(slf, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/zong/code/pytorch/test/test_optim.py", line 664, in test_complex_2d optim1.step() File "/home/zong/code/pytorch/torch/optim/optimizer.py", line 507, in wrapper out = func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/home/zong/code/pytorch/torch/optim/optimizer.py", line 80, in _use_grad ret = func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/home/zong/code/pytorch/torch/optim/adam.py", line 237, in step has_complex = self._init_group( ^^^^^^^^^^^^^^^^^ File "/home/zong/code/pytorch/torch/optim/adam.py", line 163, in _init_group _device_dtype_check_for_fused(p) File "/home/zong/code/pytorch/torch/optim/optimizer.py", line 197, in _device_dtype_check_for_fused raise RuntimeError( RuntimeError: `fused=True` requires all the params to be floating point Tensors of supported devices: ['mps', 'cuda', 'xpu', 'hpu', 'cpu', 'privateuseone'] but torch.complex64 and cuda To execute this test, run the following from the base repo dir: python test/test_optim.py TestOptimRenewedCUDA.test_complex_2d_AdamW_cuda_complex64 This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 =============================================================================================================== short test summary info ================================================================================================================ FAILED [2.1007s] test/test_optim.py::TestOptimRenewedCUDA::test_complex_2d_AdamW_cuda_complex64 - RuntimeError: `fused=True` requires all the params to be floating point Tensors of supported devices: ['mps', 'cuda', 'xpu', 'hpu', 'cpu', 'privateuseone'] but torch.complex64 and cuda

Ah sorry, we shouldn't specify "fused" in these OptimInfos after all--that was my bad. (Please delete this config)

What I care about is that the fused optimizer is tested with these betas. Can you confirm such a configuration with Tensor betas and fused is tested in one of the existing fused tests?

janeyx99

Can you show some proof that tensor betas + fused AdamW has run in a test? That is the last thing blocking this PR for me

zeshengzong · 2025-05-20T01:25:02Z

Can you show some proof that tensor betas + fused AdamW has run in a test? That is the last thing blocking this PR for me

Got it, let me check it, thanks!

zeshengzong · 2025-05-21T03:32:21Z

Compiler seems unhappy about the change, need resolve that first. 😿

zeshengzong · 2025-05-22T08:36:24Z

Test method invoke _get_optim_inputs_including_global_cliquey_kwargs which will enumerate fused and other flags to generate test-case with combine OptimizerInput and fused.

pytorch/test/test_optim.py

Lines 320 to 330 in c1b7dbc

    
           def test_tensor_lr(self, device, dtype, optim_info, num_dim): 
        
               optim_cls = optim_info.optim_cls 
        
               lr_devices = [device] 
        
               if _get_device_type(device) != "cpu": 
        
                   lr_devices.append("cpu") 
        
               # Skip differentiable testing for now, see https://github.com/pytorch/pytorch/issues/116490 
        
               all_optim_inputs = _get_optim_inputs_including_global_cliquey_kwargs( 
        
                   device, dtype, optim_info, skip=("differentiable",) 
        
               )

pytorch/torch/testing/_internal/common_optimizers.py

Lines 1303 to 1310 in c1b7dbc

    
           for flag in supported_impls: 
        
               new_kwargs = deepcopy(base_kwargs) 
        
               new_kwargs[flag] = True 
        
               all_optim_inputs.append( 
        
                   OptimizerInput( 
        
                       params=None, kwargs=new_kwargs, desc=f"{optim_input.desc} & {flag}" 
        
                   ) 
        
               )

So, there is a test case for tensor betas and fused=True, when add a OptimizerInput with tensor betas. Thanks!

zeshengzong · 2025-05-22T08:44:19Z

torch/optim/adam.py

@@ -84,6 +84,7 @@ def __init__(
                )
            if betas[1].numel() != 1:
                raise ValueError("Tensor betas[1] must be 1-element")
+        betas = tuple(map(_to_scalar, betas))


Need a convert in here, for tests create Adam using constructor and with compile. When invoke a compiled version of adam step method, it will skip step implementation in below and going to a compiled triton code (also skip _to_scalar in step following methods).

So the betas note converted into 0 dim tensor before invoke triton kernel, got errors.

ERROR: test_correctness_Adam_use_closure_False_cuda_float32 (__main__.CompiledOptimizerParityTestsCUDA.test_correctness_Adam_use_closure_False_cuda_float32) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/zong/code/pytorch/torch/_inductor/runtime/static_cuda_launcher.py", line 228, in run _StaticCudaLauncher._launch_kernel( RuntimeError: CUDA driver error: invalid argument The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/zong/code/pytorch/torch/testing/_internal/common_utils.py", line 3141, in wrapper method(*args, **kwargs) File "/home/zong/code/pytorch/torch/testing/_internal/common_utils.py", line 3141, in wrapper method(*args, **kwargs) File "/home/zong/code/pytorch/torch/testing/_internal/common_device_type.py", line 446, in instantiated_test raise rte File "/home/zong/code/pytorch/torch/testing/_internal/common_device_type.py", line 426, in instantiated_test result = test(self, **param_kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/zong/code/pytorch/torch/testing/_internal/common_optimizers.py", line 226, in test_wrapper return test(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/home/zong/code/pytorch/torch/testing/_internal/common_device_type.py", line 1215, in dep_fn return fn(slf, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/zong/code/pytorch/torch/testing/_internal/common_device_type.py", line 1215, in dep_fn return fn(slf, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/zong/code/pytorch/test/inductor/test_compiled_optimizers.py", line 664, in test_correctness fn() File "/home/zong/code/pytorch/torch/_dynamo/eval_frame.py", line 699, in _fn return fn(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^ File "/home/zong/code/pytorch/test/inductor/test_compiled_optimizers.py", line 654, in fn opt_compiled.step() File "/home/zong/code/pytorch/torch/optim/lr_scheduler.py", line 131, in wrapper return func.__get__(opt, opt.__class__)(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/zong/code/pytorch/torch/optim/optimizer.py", line 506, in wrapper out = func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/home/zong/code/pytorch/torch/optim/optimizer.py", line 80, in _use_grad ret = func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/home/zong/code/pytorch/torch/optim/adam.py", line 213, in step @_use_grad_for_differentiable File "/home/zong/code/pytorch/torch/_dynamo/eval_frame.py", line 893, in _fn return fn(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^ File "/home/zong/code/pytorch/torch/_functorch/aot_autograd.py", line 1231, in forward return compiled_fn(full_args) ^^^^^^^^^^^^^^^^^^^^^^ File "/home/zong/code/pytorch/torch/_functorch/_aot_autograd/runtime_wrappers.py", line 338, in runtime_wrapper all_outs = call_func_at_runtime_with_args( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/zong/code/pytorch/torch/_functorch/_aot_autograd/utils.py", line 126, in call_func_at_runtime_with_args out = normalize_as_list(f(args)) ^^^^^^^ File "/home/zong/code/pytorch/torch/_functorch/_aot_autograd/runtime_wrappers.py", line 696, in inner_fn outs = compiled_fn(args) ^^^^^^^^^^^^^^^^^ File "/home/zong/code/pytorch/torch/_functorch/_aot_autograd/runtime_wrappers.py", line 502, in wrapper return compiled_fn(runtime_args) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/zong/code/pytorch/torch/_inductor/output_code.py", line 582, in __call__ return self.current_callable(inputs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/zong/code/pytorch/torch/_inductor/utils.py", line 2661, in run return model(new_inputs) ^^^^^^^^^^^^^^^^^ File "/tmp/tmpl9cibdeh/jv/cjvt2dlgiwbtybi4lnwxvx47mx4g5jzyhquzxefp32ghvc3chpi4.py", line 590, in call triton_poi_fused_add_addcdiv_div_lerp_maximum_mul_neg_pow_reciprocal_rsub_sqrt_0.run(arg9_1, arg22_1, arg23_1, buf0, arg8_1, arg11_1, arg10_1, arg20_1.item(), arg21_1, arg0_1, arg13_1, arg25_1, arg12_1, arg15_1, arg14_1, arg2_1, arg8_1, arg9_1, arg11_1, arg0_1, arg12_1, arg13_1, arg15_1, arg2_1, 100, stream=stream0) File "/home/zong/code/pytorch/torch/_inductor/runtime/triton_heuristics.py", line 1069, in run return launcher( ^^^^^^^^^ File "<string>", line 5, in launcher File "/home/zong/code/pytorch/torch/_inductor/runtime/static_cuda_launcher.py", line 240, in run raise RuntimeError( RuntimeError: Failed to launch kernel triton_poi_fused_add_addcdiv_div_lerp_maximum_mul_neg_pow_reciprocal_rsub_sqrt_0 with args

Is there a way to get around this by changing the compile tests? How come it won't properly trace the updated step?

Probably not able to change tests, it's acquire test data from optim_db which might change all optim related test and user also may use like this way.

pytorch/test/inductor/test_compiled_optimizers.py

Lines 571 to 579 in c1055f4

@skipCUDAIf(not has_triton(), "torch.compile with cuda requires triton")

@skipXPUIf(not has_triton(), "torch.compile with xpu requires triton")

@optims(optim_db, dtypes=[torch.float32])

@parametrize("use_closure", [True, False])

def test_correctness(self, device, dtype, optim_info, use_closure):

optim_cls = optim_info.optim_cls

all_optim_inputs = _get_optim_inputs_including_global_cliquey_kwargs(

device, dtype, optim_info, skip=("differentiable",)

)

I also curious about why _to_scalar not included in compiled code, need more time figure it out. :D

@mlazos do you remember if there's a place we'd need to update when _init_group updates?

torch/testing/_internal/common_optimizers.py

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Fixes pytorch#147921 ## Changes - Convert tensor `betas` using `_to_scalar` - Change annotation of `betas` param - Change param type in docs ## Test Result ```bash pytest -s test/test_optim.py -k test_tensor_lr -vv ``` ![image](https://github.com/user-attachments/assets/312ee045-1e8b-4789-aa6e-ba63e6df7e81) ![image](https://github.com/user-attachments/assets/7e6ec274-645b-46b9-b1a6-2b340a685203) Pull Request resolved: pytorch#149939 Approved by: https://github.com/janeyx99 Co-authored-by: Jane (Yuan) Xu <31798555+janeyx99@users.noreply.github.com>

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zeshengzong marked this pull request as ready for review March 25, 2025 07:36

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	if not (p.device.type in fused_supported_devices and torch.is_floating_point(p)):
	raise RuntimeError(
	"`fused=True` requires all the params to be floating point Tensors of "
	f"supported devices: {fused_supported_devices} but {p.dtype} and {p.device.type}"

	@skipCUDAIf(not has_triton(), "torch.compile with cuda requires triton")
	@skipXPUIf(not has_triton(), "torch.compile with xpu requires triton")
	@optims(optim_db, dtypes=[torch.float32])
	@parametrize("use_closure", [True, False])
	def test_correctness(self, device, dtype, optim_info, use_closure):
	optim_cls = optim_info.optim_cls
	all_optim_inputs = _get_optim_inputs_including_global_cliquey_kwargs(
	device, dtype, optim_info, skip=("differentiable",)
	)

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