Adding int4 quantized tensor subclass

README.md

@@ -32,63 +32,87 @@ Relevant APIs can be found in torchao.quantization.quant_api
 Note: While these techniques are designed to improve model performance, in some cases the opposite can occur.
 This is because quantization adds additional overhead to the model that is hopefully made up for by faster matmuls (dynamic quantization) or loading weights faster (weight-only quantization). If your matmuls are small enough or your non-quantized perf isn't bottlenecked by weight load time, these techniques may reduce performance.
 
+The following apis use quantized [tensor subclasses](https://pytorch.org/docs/stable/notes/extending.html#subclassing-torch-tensor). By taking a linear op/module and replacing the original weight with a q-tensor subclass, we're able to convert it into a quantized version of the op. Upon replacement, these q-tensor subclasses quantize the original weight and override the dispatch for linear ops to instead use the subclass' _quantized_op method.
+
+This tensor subclass method of quantization is preferred over older module swap based methods because it doesn't modify the graph and is generally more composable and flexible.
+
 ### A8W8 Dynamic Quantization
 
-Similar to the weight only api above, the `apply_dynamic_quant` function swaps all
-linear modules to dynamically quantized quantized linear modules.
+The `change_linear_weights_to_int8_dqtensors` function converts the linear weights in a model to a quantized tensor subclass `Int8DynamicallyQuantizedLinearWeight`. In practice this
+converts the floating point linear matmul of the original linear op to a dynamically quantized linear matmul.
 
 Example
 
 ```
+import torch
+from torchao.quantization import quant_api
 
 # some user model and example input
-...
+model = torch.nn.Sequential(torch.nn.Linear(32, 64)).cuda().to(torch.bfloat16)
+input = torch.randn(32,32, dtype=torch.bfloat16, device='cuda')
 
 # convert linear modules to quantized linear modules
-quant_api.apply_dynamic_quant(model)
+quant_api.change_linear_weights_to_int8_dqtensors(model)
 
 # compile the model to improve performance
-...
+torch.compile(model, mode='max-autotune')
+model(input)
 ```
 
 This technique works best when the torch._inductor.config.force_fuse_int_mm_with_mul option is enabled. This allows fusion of the int8*int8 -> int32 matmul and subsequent mul op, thereby avoiding materialization of the int32 intermediary tensor.
 
+
 ### A16W8 WeightOnly Quantization
 
-The `apply_weight_only_int8_quant` function swaps all
-linear modules to weight-only quantized linear modules.
+The `change_linear_weights_to_int8_woqtensors` function converts the linear weights in a model to a quantized tensor subclass `Int8WeightOnlyQuantizedLinearWeight`. In practice this
+converts the floating point linear matmul of the original linear op to a weight only quantized linear matmul
 
 Example
 
 ```
-import torch
-from torchao.quantization import quant_api
-
 # some user model and example input
-model = torch.nn.Sequential(torch.nn.Linear(32, 64)).cuda().to(torch.bfloat16)
-input = torch.randn(32,32, dtype=torch.bfloat16, device='cuda')
+...
 
 # convert linear modules to quantized linear modules
-quant_api.apply_weight_only_int8_quant(model)
+quant_api.change_linear_weights_to_int8_woqtensors(model)
 
 # compile the model to improve performance
-torch.compile(model, mode='max-autotune')
-model(input)
+...
 ```
 
 This technique works best when the torch._inductor.config.use_mixed_mm option is enabled. This avoids dequantizing the weight tensor before the matmul, instead fusing the dequantization into the matmul, thereby avoiding materialization of a large floating point weight tensor.
 
+
+### A16W4 WeightOnly Quantization
+
+The `change_linear_weights_to_int4_woqtensors` function converts the linear weights in a model to a quantized tensor subclass `Int4WeightOnlyQuantizedLinearWeight`. In practice this
+converts the floating point linear matmul of the original linear op to a weight only quantized linear matmul
+
+Example
+
+```
+# some user model and example input
+...
+
+# convert linear modules to quantized linear modules
+quant_api.change_linear_weights_to_int4_woqtensors(model)
+
+# compile the model to improve performance
+...
+```
+
+The quantization error incurred by applying int4 quantization to your model can be fairly significant, so using external techniques like GPTQ may be necessary to obtain a usable model.
+
 ## Other APIs
 
-### A8W8 Dynamic Quantization by subclasses
+### Module Swap APIs
 
-You can use [tensor subclasses](https://pytorch.org/docs/stable/notes/extending.html#subclassing-torch-tensor) to do dynamic quantization with the `change_linear_weights_to_dqtensors` function using the exact same formula as above. This avoids modifying the graph and can be more composable with
-other techniques.
+The `apply_dynamic_quant` and `apply_weight_only_int8_quant` apis can be used in the same formula as above to achieve dynamic and weight-only quantization using module swaps instead of quantized tensor subclasses.
 
 ### A8W8 Dynamic Quantization with Smoothquant
 
 We've also implemented a version of [smoothquant](https://arxiv.org/abs/2211.10438) with the same GEMM format as above.
-Due to requiring calibration, the API is slightly more complicated
+Due to requiring calibration, the API is slightly more complicated and currently only exists with a module swap api.
 
 Example
 
@@ -116,6 +140,8 @@ torch.compile(model, mode='max-autotune')
 model(input)
 ```
 
+like the other dynamic quantization apis, the torch._inductor.config.force_fuse_int_mm_with_mul option may significantly improve performance if enabled.
+
 ## License
 
 `torchao` is released under the [BSD 3](https://github.com/pytorch-labs/ao/blob/main/LICENSE) license.