[feat] allow fp16 optimizer state with Adam (#41)

Allow training with optimizer state in fp16. Use an enum to select from full-precision, mixed precision, memory efficient mixed precision and pure fp16. Improve clarity of testing code
Co-authored-by: Jun Ru Anderson <andersonic@fb.com>

benchmarks/transformer.py

@@ -11,7 +11,7 @@ from torchtext.data.utils import get_tokenizer
 from fairscale.nn import Pipe
 
 try:
-    from fairscale.optim.adam import Adam  # type: ignore
+    from fairscale.optim.adam import Adam, Precision  # type: ignore
 
     can_benchmark = True
 except ImportError:
@@ -135,7 +135,7 @@ def make_model(device, ntokens):
 
     criterion = nn.CrossEntropyLoss()
     lr = 0.01  # learning rate
-    optimizer = Adam(p.parameters(), lr=lr, mixed_precision=True)
+    optimizer = Adam(p.parameters(), lr=lr, precision=Precision.MIXED_PRECISION)
 
     return p, criterion, optimizer
 

fairscale/clib/fused_adam_cuda/fused_adam_cuda_kernel.cu

@@ -20,7 +20,7 @@ typedef enum{
     ADAM_MODE_1   =1  // eps outside square root
 } adamMode_t;
 
-template <int DEPTH, typename PARAM_T, typename GRAD_T>
+template <int DEPTH, typename PARAM_T, typename GRAD_T, typename OPTIM_T>
 struct AdamFunctor
 {
     __device__ __forceinline__ void operator()(
@@ -41,9 +41,9 @@ struct AdamFunctor
 
         PARAM_T* p = (PARAM_T *)tl.addresses[0][tensor_loc];
         p += chunk_idx*chunk_size;
-        float* m = (float *)tl.addresses[1][tensor_loc];
+        OPTIM_T* m = (OPTIM_T *)tl.addresses[1][tensor_loc];
         m += chunk_idx*chunk_size;
-        float* v = (float *)tl.addresses[2][tensor_loc];
+        OPTIM_T* v = (OPTIM_T *)tl.addresses[2][tensor_loc];
         v += chunk_idx*chunk_size;
         GRAD_T* g = (GRAD_T *)tl.addresses[3][tensor_loc];
         g += chunk_idx*chunk_size;
@@ -56,8 +56,8 @@ struct AdamFunctor
         n -= chunk_idx*chunk_size;
 
         PARAM_T incoming_p[ILP];
-        float incoming_m[ILP];
-        float incoming_v[ILP];
+        OPTIM_T incoming_m[ILP];
+        OPTIM_T incoming_v[ILP];
         GRAD_T incoming_g[ILP];
 
         for(int i_start = 0;
@@ -91,14 +91,16 @@ struct AdamFunctor
 
                 if(j < n && j < chunk_size) {
                     float scaled_grad = incoming_g[ii]/grad_scale;
-                    m[j] = b1*incoming_m[ii] + (1-b1)*scaled_grad;
-                    v[j] = b2*incoming_v[ii] + (1-b2)*scaled_grad*scaled_grad;
+                    float momentum = b1 * incoming_m[ii] + (1-b1)*scaled_grad;
+                    float velocity = b2 * incoming_v[ii] + (1-b2)*scaled_grad*scaled_grad;
+                    m[j] = static_cast<OPTIM_T>(momentum);
+                    v[j] = static_cast<OPTIM_T>(velocity);
                     float denom;
                     if (mode == ADAM_MODE_0)
-                        denom = sqrtf(v[j] + eps);
+                        denom = sqrtf(velocity + eps);
                     else // Mode 1
-                        denom = sqrtf(v[j]) + eps;
-                    float update = (m[j]/denom) + (decay*incoming_p[ii]);
+                        denom = sqrtf(velocity) + eps;
+                    float update = (momentum/denom) + (decay*incoming_p[ii]);
                     p[j] = (PARAM_T)(incoming_p[ii] - (step_size*update));
                     if (DEPTH == 5)  p_copy[j] = (at::Half) p[j];
                 }
@@ -135,6 +137,7 @@ void fused_adam_cuda(
 
     size_t tl_sz = tensor_lists.size();
     assert(tl_sz == 4 || tl_sz == 5);
+    assert(tensor_lists[1][0].scalar_type() == tensor_lists[2][0].scalar_type());
 
     if(tl_sz == 5) {
         // Mixed precision case
@@ -146,7 +149,7 @@ void fused_adam_cuda(
             chunk_size,
             noop_flag,
             tensor_lists,
-            AdamFunctor<5, float, at::Half>(),
+            AdamFunctor<5, float, at::Half, float>(),
             beta1,
             beta2,
             eps,
@@ -160,12 +163,13 @@ void fused_adam_cuda(
         assert(tensor_lists[0][0].scalar_type() == tensor_lists[3][0].scalar_type());
         if(tensor_lists[0][0].scalar_type() == at::ScalarType::Float) {
             // Full precision case
+            assert(tensor_lists[1][0].scalar_type() == at::ScalarType::Float);
             multi_tensor_apply<4>(
                 BLOCK_SIZE,
                 chunk_size,
                 noop_flag,
                 tensor_lists,
-                AdamFunctor<4, float, float>(),
+                AdamFunctor<4, float, float, float>(),
                 beta1,
                 beta2,
                 eps,
@@ -175,13 +179,14 @@ void fused_adam_cuda(
                 decay
             );
         } else if (tensor_lists[0][0].scalar_type() == at::ScalarType::Half) {
-            // "Memory Efficient Training" case
+            if(tensor_lists[1][0].scalar_type() == at::ScalarType::Float) {
+                // FP16 model parameters and gradients; FP32 optimizer state
                 multi_tensor_apply<4>(
                     BLOCK_SIZE,
                     chunk_size,
                     noop_flag,
                     tensor_lists,
-                AdamFunctor<4, at::Half, at::Half>(),
+                    AdamFunctor<4, at::Half, at::Half, float>(),
                     beta1,
                     beta2,
                     eps,
@@ -190,6 +195,25 @@ void fused_adam_cuda(
                     (adamMode_t) mode,
                     decay
                 );
+            } else if (tensor_lists[1][0].scalar_type() == at::ScalarType::Half) {
+                // Pure FP16 case
+                multi_tensor_apply<4>(
+                    BLOCK_SIZE,
+                    chunk_size,
+                    noop_flag,
+                    tensor_lists,
+                    AdamFunctor<4, at::Half, at::Half, at::Half>(),
+                    beta1,
+                    beta2,
+                    eps,
+                    grad_scale,
+                    step_size,
+                    (adamMode_t) mode,
+                    decay
+                );
+            } else {
+                throw "Optimizer state must be of type float or half";
+            }
         } else {
             throw "Parameters must be of type float or half";
         }

fairscale/optim/adam.py

@@ -3,6 +3,7 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
+from enum import Enum, auto
 from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple
 
 import torch
@@ -15,6 +16,12 @@ else:
 try:
     from fairscale import fused_adam_cuda  # type: ignore
 
+    class Precision(Enum):
+        FULL_PRECISION = auto()
+        MIXED_PRECISION = auto()
+        MEMORY_EFFICIENT_MIXED_PRECISION = auto()
+        PURE_FP16 = auto()
+
     class Adam(torch.optim.Optimizer):
         state: dict
         defaults: dict
@@ -39,6 +46,10 @@ try:
                 adds eps to the bias-corrected second moment estimate before
                 evaluating square root instead of adding it to the square root of
                 second moment estimate as in the original paper. (default: False)
+            precision (Precision, optional): One of Precision.FULL_PRECISION,
+                Precision.MIXED_PRECISION, Precision.MEMORY_EFFICIENT_MIXED_PRECISION
+                or Precision.PURE_FP16. Inferred based on model parameter precision if
+                None. (default: None)
         .. _Adam: A Method for Stochastic Optimization:
             https://arxiv.org/abs/1412.6980
         .. _On the Convergence of Adam and Beyond:
@@ -56,11 +67,24 @@ try:
             weight_decay: Optional[float] = 0.0,
             max_grad_norm: Optional[float] = 0.0,
             amsgrad: Optional[bool] = False,
-            mixed_precision: Optional[bool] = False,
+            precision: Optional[Precision] = None,
         ):
-            self.mixed_precision = mixed_precision
             parameters: List[Any] = list(params)
 
+            if precision is None:
+                precision = (
+                    Precision.FULL_PRECISION if parameters[0].dtype == torch.float32 else Precision.MIXED_PRECISION
+                )
+
+            self.mixed_precision = False
+            if precision is Precision.MIXED_PRECISION:
+                self.mixed_precision = True
+
+            if precision is not Precision.FULL_PRECISION:
+                assert parameters[0].dtype == torch.float16
+
+            self.optim_type = torch.float16 if precision is Precision.PURE_FP16 else torch.float32
+
             self._overflow_buf = torch.cuda.IntTensor([0])  # type: ignore
 
             if amsgrad:
@@ -76,7 +100,8 @@ try:
             super().__init__(parameters, defaults)
             self.eps_mode = 0 if eps_inside_sqrt else 1
 
-            if mixed_precision:
+            self.fp32_param_groups: List[Any] = []
+            if self.mixed_precision:
                 self._build_fp32_params(parameters)
 
         def _build_fp32_params(self, params: Any) -> None:
@@ -119,6 +144,21 @@ try:
         def _step_supports_amp_scaling(self) -> bool:
             return False
 
+        def state_dict(self) -> Dict[str, Any]:
+            d = super().state_dict()
+            d["optim_type"] = self.optim_type
+            d["mixed_precision"] = self.mixed_precision
+            d["fp32_param_groups"] = self.fp32_param_groups
+            d["state"] = self.state
+            return d
+
+        def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
+            super().load_state_dict(state_dict)
+            self.optim_type = state_dict["optim_type"]
+            self.mixed_precision = state_dict["mixed_precision"]
+            self.fp32_param_groups = state_dict["fp32_param_groups"]
+            self.state = state_dict["state"]
+
         def step(self, closure: Optional[Callable[[], float]] = None) -> Optional[float]:
             """Performs a single optimization step.
             Arguments:
@@ -161,9 +201,9 @@ try:
                     if len(state) == 0:
                         state["step"] = 0
                         # Exponential moving average of gradient values
-                        state["exp_avg"] = torch.zeros_like(p, dtype=torch.float32)
+                        state["exp_avg"] = torch.zeros_like(p, dtype=self.optim_type)
                         # Exponential moving average of squared gradient values
-                        state["exp_avg_sq"] = torch.zeros_like(p, dtype=torch.float32)
+                        state["exp_avg_sq"] = torch.zeros_like(p, dtype=self.optim_type)
 
                     exp_avg = state["exp_avg"]
                     exp_avg_sq = state["exp_avg_sq"]

tests/optim/test_adam.py

@@ -10,7 +10,7 @@ import pytest
 import torch
 
 try:
-    from fairscale.optim.adam import Adam
+    from fairscale.optim.adam import Adam, Precision
 
     imported_adam = True
 except ImportError:
@@ -20,17 +20,12 @@ skip_if_no_cuda = pytest.mark.skipif(not torch.cuda.is_available(), reason="cuda
 skip_if_no_adam = pytest.mark.skipif(not imported_adam, reason="Fairscale Adam not available")
 
 
-@skip_if_no_cuda
-@skip_if_no_adam
-def test_step():
-    weight = torch.randn(10, 5).cuda().requires_grad_()
-    bias = torch.randn(10).cuda().requires_grad_()
-    input = torch.randn(5).cuda()
-    optimizer = Adam([weight, bias], lr=1e-3)
+def assert_almost_zero(x):
+    assert abs(x) < 2 * 1e-3
+    return 1.0
 
-    # to check if the optimizer can be printed as a string
-    optimizer.__repr__()
 
+def step_test(optimizer, weight, bias, input):
     def fn():
         optimizer.zero_grad()
         y = weight.mv(input)
@@ -44,71 +39,67 @@ def test_step():
     for _i in range(5):
         optimizer.step(fn)
     assert fn().item() < initial_value
-    for group in optimizer.param_groups:
-        for p in group["params"]:
-            if p.requires_grad:
-                assert p.dtype == torch.float32
-    with pytest.raises(AttributeError):
-        optimizer.fp32_param_groups
+
+
+def state_dict_test(optimizer, weight, bias, input):
+    def fn_base(optimizer, weight, bias, input):
+        optimizer.zero_grad()
+        loss = (weight.mv(input) + bias).pow(2).sum()
+        loss.backward()
+        return loss
+
+    fn = functools.partial(fn_base, optimizer, weight, bias, input)
+
+    # Prime the optimizer
+    for _i in range(5):
+        optimizer.step(fn)
+    # Clone the weights and construct new optimizer for them
+    weight_c = weight.data.clone().requires_grad_()
+    bias_c = bias.data.clone().requires_grad_()
+    optimizer_c = Adam([weight_c, bias_c], lr=1e-3)
+    fn_c = functools.partial(fn_base, optimizer_c, weight_c, bias_c, input)
+    # Load state dict
+    state_dict = deepcopy(optimizer.state_dict())
+    optimizer_c.load_state_dict(state_dict)
+    # Run both optimizations in parallel
+    for _i in range(5):
+        optimizer.step(fn)
+        optimizer_c.step(fn_c)
+        (weight - weight_c).to("cpu").detach().apply_(assert_almost_zero)
+        (bias - bias_c).to("cpu").detach().apply_(assert_almost_zero)
 
 
 @skip_if_no_cuda
 @skip_if_no_adam
-def test_step_me():
-    weight = torch.randn(10, 5).cuda().half().requires_grad_()
-    bias = torch.randn(10).cuda().half().requires_grad_()
-    input = torch.randn(5).half().cuda()
+def test_step_full_precision_inferred():
+    weight = torch.randn(10, 5).cuda().requires_grad_()
+    bias = torch.randn(10).cuda().requires_grad_()
+    input = torch.randn(5).cuda()
     optimizer = Adam([weight, bias], lr=1e-3)
 
     # to check if the optimizer can be printed as a string
     optimizer.__repr__()
+    step_test(optimizer, weight, bias, input)
 
-    def fn():
-        optimizer.zero_grad()
-        y = weight.mv(input)
-        if y.is_cuda and bias.is_cuda and y.get_device() != bias.get_device():
-            y = y.cuda(bias.get_device())
-        loss = (y + bias).pow(2).sum()
-        loss.backward()
-        return loss
-
-    initial_value = fn().item()
-    for _i in range(5):
-        optimizer.step(fn)
-    assert fn().item() < initial_value
     for group in optimizer.param_groups:
         for p in group["params"]:
             if p.requires_grad:
-                assert p.dtype == torch.float16
-    with pytest.raises(AttributeError):
-        optimizer.fp32_param_groups
+                assert p.dtype == torch.float32
+    assert not optimizer.fp32_param_groups
 
 
 @skip_if_no_cuda
 @skip_if_no_adam
-def test_step_mixed_precision():
+def test_step_mixed_precision_inferred():
     weight = torch.randn(10, 5).cuda().half().requires_grad_()
     bias = torch.randn(10).cuda().half().requires_grad_()
     input = torch.randn(5).half().cuda()
-    optimizer = Adam([weight, bias], lr=1e-3, mixed_precision=True)
+    optimizer = Adam([weight, bias], lr=1e-3)
 
     # to check if the optimizer can be printed as a string
     optimizer.__repr__()
+    step_test(optimizer, weight, bias, input)
 
-    def fn():
-        optimizer.zero_grad()
-        y = weight.mv(input)
-        if y.is_cuda and bias.is_cuda and y.get_device() != bias.get_device():
-            y = y.cuda(bias.get_device())
-        loss = (y + bias).pow(2).sum()
-        loss.backward()
-        return loss
-
-    initial_value = fn().item()
-    for _i in range(5):
-        optimizer.step(fn)
-
-    assert fn().item() < initial_value
     assert len(optimizer.fp32_param_groups) == len(optimizer.param_groups)
 
     for fp32_group, fp16_group in zip(optimizer.fp32_param_groups, optimizer.param_groups):
@@ -124,6 +115,44 @@ def test_step_mixed_precision():
                 (fp32_p - fp16_p).to("cpu").detach().apply_(assert_almost_zero)
 
 
+@skip_if_no_cuda
+@skip_if_no_adam
+def test_step_memory_efficient():
+    weight = torch.randn(10, 5).cuda().half().requires_grad_()
+    bias = torch.randn(10).cuda().half().requires_grad_()
+    input = torch.randn(5).half().cuda()
+    optimizer = Adam([weight, bias], lr=1e-3, precision=Precision.MEMORY_EFFICIENT_MIXED_PRECISION)
+
+    # to check if the optimizer can be printed as a string
+    optimizer.__repr__()
+    step_test(optimizer, weight, bias, input)
+
+    for group in optimizer.param_groups:
+        for p in group["params"]:
+            if p.requires_grad:
+                assert p.dtype == torch.float16
+    assert not optimizer.fp32_param_groups
+
+
+@skip_if_no_cuda
+@skip_if_no_adam
+def test_step_pure_fp16():
+    weight = torch.randn(10, 5).half().cuda().requires_grad_()
+    bias = torch.randn(10).half().cuda().requires_grad_()
+    input = torch.randn(5).half().cuda()
+    optimizer = Adam([weight, bias], lr=1e-3, precision=Precision.PURE_FP16)
+
+    # to check if the optimizer can be printed as a string
+    optimizer.__repr__()
+    step_test(optimizer, weight, bias, input)
+
+    assert optimizer.state[weight]["exp_avg"].dtype == torch.float16
+    assert optimizer.state[weight]["exp_avg_sq"].dtype == torch.float16
+    assert optimizer.state[bias]["exp_avg"].dtype == torch.float16
+    assert optimizer.state[bias]["exp_avg_sq"].dtype == torch.float16
+    assert not optimizer.fp32_param_groups
+
+
 @skip_if_no_cuda
 @skip_if_no_adam
 def test_step_multigpu():
@@ -136,20 +165,7 @@ def test_step_multigpu():
 
     # to check if the optimizer can be printed as a string
     optimizer.__repr__()
-
-    def fn():
-        optimizer.zero_grad()
-        y = weight.mv(input)
-        if y.is_cuda and bias.is_cuda and y.get_device() != bias.get_device():
-            y = y.cuda(bias.get_device())
-        loss = (y + bias).pow(2).sum()
-        loss.backward()
-        return loss
-
-    initial_value = fn().item()
-    for _i in range(5):
-        optimizer.step(fn)
-    assert fn().item() < initial_value
+    step_test(optimizer, weight, bias, input)
 
 
 @skip_if_no_cuda
@@ -160,61 +176,75 @@ def test_step_multigpu_mixed_precision():
     weight = torch.randn(10, 5).cuda(0).half().requires_grad_()
     bias = torch.randn(10).cuda(1).half().requires_grad_()
     input = torch.randn(5).cuda(0).half()
-    optimizer = Adam([weight, bias], lr=1e-3, mixed_precision=True)
+    optimizer = Adam([weight, bias], lr=1e-3)
 
     # to check if the optimizer can be printed as a string
     optimizer.__repr__()
+    step_test(optimizer, weight, bias, input)
 
-    def fn():
-        optimizer.zero_grad()
-        y = weight.mv(input)
-        if y.is_cuda and bias.is_cuda and y.get_device() != bias.get_device():
-            y = y.cuda(bias.get_device())
-        loss = (y + bias).pow(2).sum()
-        loss.backward()
-        return loss
 
-    initial_value = fn().item()
-    for _i in range(5):
-        optimizer.step(fn)
-    assert fn().item() < initial_value
+@skip_if_no_cuda
+@skip_if_no_adam
+def test_step_pure_fp16_multigpu():
+    if not torch.cuda.device_count() > 1:
+        return
+    weight = torch.randn(10, 5).half().cuda(0).requires_grad_()
+    bias = torch.randn(10).half().cuda(1).requires_grad_()
+    input = torch.randn(5).half().cuda(0)
+    optimizer = Adam([weight, bias], lr=1e-3, precision=Precision.PURE_FP16)
+
+    # to check if the optimizer can be printed as a string
+    optimizer.__repr__()
+    step_test(optimizer, weight, bias, input)
+
+    assert optimizer.state[weight]["exp_avg"].dtype == torch.float16
+    assert optimizer.state[weight]["exp_avg_sq"].dtype == torch.float16
+    assert optimizer.state[bias]["exp_avg"].dtype == torch.float16
+    assert optimizer.state[bias]["exp_avg_sq"].dtype == torch.float16
 
 
 @skip_if_no_cuda
 @skip_if_no_adam
-def test_state_dict():
+def test_state_dict_full_precision():
     weight = torch.randn(10, 5).float().cuda().requires_grad_()
     bias = torch.randn(10).float().cuda().requires_grad_()
     input = torch.randn(5).float().cuda()
-
     optimizer = Adam([weight, bias], lr=1e-3)
 
-    def fn_base(optimizer, weight, bias, input):
-        optimizer.zero_grad()
-        loss = (weight.mv(input) + bias).pow(2).sum()
-        loss.backward()
-        return loss
+    state_dict_test(optimizer, weight, bias, input)
 
-    fn = functools.partial(fn_base, optimizer, weight, bias, input)
 
-    # Prime the optimizer
-    for _i in range(5):
-        optimizer.step(fn)
-    # Clone the weights and construct new optimizer for them
-    weight_c = weight.data.clone().requires_grad_()
-    bias_c = bias.data.clone().requires_grad_()
-    optimizer_c = Adam([weight_c, bias_c], lr=1e-3)
-    fn_c = functools.partial(fn_base, optimizer_c, weight_c, bias_c, input)
-    # Load state dict
-    state_dict = deepcopy(optimizer.state_dict())
-    state_dict_c = deepcopy(optimizer.state_dict())
-    optimizer_c.load_state_dict(state_dict_c)
-    # Run both optimizations in parallel
-    for _i in range(5):
-        optimizer.step(fn)
-        optimizer_c.step(fn_c)
-        assert torch.equal(weight, weight_c)
-        assert torch.equal(bias, bias_c)
+@skip_if_no_cuda
+@skip_if_no_adam
+def test_state_dict_mixed_precision():
+    weight = torch.randn(10, 5).half().cuda().requires_grad_()
+    bias = torch.randn(10).half().cuda().requires_grad_()
+    input = torch.randn(5).half().cuda()
+    optimizer = Adam([weight, bias], lr=1e-3, precision=Precision.MIXED_PRECISION)
+
+    state_dict_test(optimizer, weight, bias, input)
+
+
+@skip_if_no_cuda
+@skip_if_no_adam
+def test_state_dict_memory_efficient():
+    weight = torch.randn(10, 5).half().cuda().requires_grad_()
+    bias = torch.randn(10).half().cuda().requires_grad_()
+    input = torch.randn(5).half().cuda()
+    optimizer = Adam([weight, bias], lr=1e-3, precision=Precision.MEMORY_EFFICIENT_MIXED_PRECISION)
+
+    state_dict_test(optimizer, weight, bias, input)
+
+
+@skip_if_no_cuda
+@skip_if_no_adam
+def test_state_dict_pure_fp16():
+    weight = torch.randn(10, 5).half().cuda().requires_grad_()
+    bias = torch.randn(10).half().cuda().requires_grad_()
+    input = torch.randn(5).half().cuda()
+    optimizer = Adam([weight, bias], lr=1e-3, precision=Precision.PURE_FP16)
+
+    state_dict_test(optimizer, weight, bias, input)
 
 
 @skip_if_no_cuda
@@ -226,11 +256,6 @@ def test_build_fp32_params():
     optimizer._build_fp32_params([weight, bias])
     for fp32_group, fp16_group in zip(optimizer.fp32_param_groups, optimizer.param_groups):
         for fp32_p, fp16_p in zip(fp32_group["params"], fp16_group["params"]):
-
-            def assert_almost_zero(x):
-                assert abs(x) < 1e-3
-                return 1.0
-
             assert fp32_p.dtype == torch.float32
             if fp16_p.requires_grad:
                 assert fp16_p.dtype == torch.float16
@@ -262,3 +287,30 @@ def test_amsgrad():
     bias = torch.randn(10, requires_grad=True).float().cuda()
     with pytest.raises(RuntimeError):
         Adam([weight, bias], lr=1e-2, amsgrad=True)
+
+
+@skip_if_no_cuda
+@skip_if_no_adam
+def test_mixed_precision_with_full_precision_parameters():
+    weight = torch.randn(10, 5, requires_grad=True).float().cuda()
+    bias = torch.randn(10, requires_grad=True).float().cuda()
+    with pytest.raises(AssertionError):
+        Adam([weight, bias], lr=1e-2, precision=Precision.MIXED_PRECISION)
+
+
+@skip_if_no_cuda
+@skip_if_no_adam
+def test_memory_efficient_with_full_precision_parameters():
+    weight = torch.randn(10, 5, requires_grad=True).float().cuda()
+    bias = torch.randn(10, requires_grad=True).float().cuda()
+    with pytest.raises(AssertionError):
+        Adam([weight, bias], lr=1e-2, precision=Precision.MEMORY_EFFICIENT_MIXED_PRECISION)
+
+
+@skip_if_no_cuda
+@skip_if_no_adam
+def test_pure_fp16_with_full_precision_parameters():
+    weight = torch.randn(10, 5, requires_grad=True).float().cuda()
+    bias = torch.randn(10, requires_grad=True).float().cuda()
+    with pytest.raises(AssertionError):
+        Adam([weight, bias], lr=1e-2, precision=Precision.PURE_FP16)