autocast ( device_type = "cuda", enabled = False ): # Calls e_float16.float() to ensure float32 execution # (necessary because e_float16 was created in an autocasted region) f_float32 = torch. autocast ( device_type = "cuda" ): e_float16 = torch. rand (( 8, 8 ), device = "cuda" ) with torch. rand (( 8, 8 ), device = "cuda" ) d_float32 = torch. rand (( 8, 8 ), device = "cuda" ) c_float32 = torch. rand (( 8, 8 ), device = "cuda" ) b_float32 = torch. # Creates some tensors in default dtype (here assumed to be float32) a_float32 = torch. In the subregion, inputs from the surrounding region Disabling autocast gives you explicit control over Locally disabling autocast can be useful, for example, if you want to force a subregion Type mismatch errors in an autocast-enabled region are a bug if this is what you observe,Īutocast(enabled=False) subregions can be nested in autocast-enabled regions. freeze ( model ) # Models Run for _ in range ( 3 ): model ( torch. autocast ( cache_enabled = False ): model = torch. _jit_set_autocast_mode ( False ) with torch. eval () # For now, we suggest to disable the Jit Autocast Pass, # As the issue: torch. fc1 ( x ) input_size = 2 num_classes = 2 model = TestModel ( input_size, num_classes ). Linear ( input_size, num_classes ) def forward ( self, x ): return self. Module ): def _init_ ( self, input_size, num_classes ): super (). tocast and are new in version 1.10.Ĭlass TestModel ( nn. For CPU, only lower precision floating point datatype of torch.bfloat16 is supported for now. tocast("cpu", args.) is equivalent to (args.). tocast("cuda", args.) is equivalent to (args.). However, tocast and are modular, and may be used separately if desired.Īs shown in the CPU example section of tocast, “automatic mixed precision training/inference” on CPU withĭatatype of torch.bfloat16 only uses tocast.įor CUDA and CPU, APIs are also provided separately: together, as shown in the CUDA Automatic Mixed Precision examplesĪnd CUDA Automatic Mixed Precision recipe. Ordinarily, “automatic mixed precision training” with datatype of torch.float16 uses tocast and Mixed precision tries to match each op to its appropriate datatype. Other ops, like reductions, often require the dynamic Some ops, like linear layers and convolutions,Īre much faster in lower_precision_fp. Use lower precision floating point datatype ( lower_precision_fp): torch.float16 ( half) or torch.bfloat16. Where some operations use the torch.float32 ( float) datatype and other operations Torch.amp provides convenience methods for mixed precision, Extending torch.func with autograd.FunctionĪutomatic Mixed Precision package - torch.amp ¶.CPU threading and TorchScript inference.CUDA Automatic Mixed Precision examples.
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