[3/n][Optimus][Auto-AC][reland] Support any fp8 quantization type and set scaling as the default" (#154057)

Summary:
This is a reland of D74910193.
We change the dtype to torch.float8_e5m2 in unit test since it is not supported.

Test Plan:
```
buck2 test 'fbcode//mode/dev-nosan' fbcode//caffe2/test/inductor:quantization
```

Differential Revision: D75169792

Pull Request resolved: https://github.com/pytorch/pytorch/pull/154057
Approved by: https://github.com/Mingming-Ding

test/inductor/test_quantization.py

@@ -47,6 +47,21 @@ class FeedforwardNN(torch.nn.Module):
         return x
 
 
+class LayernormNN(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, input, normalized_shape, weight, bias):
+        x = torch.nn.functional.layer_norm(
+            input=input,
+            normalized_shape=normalized_shape,
+            weight=weight,
+            bias=bias,
+            eps=1e-5,
+        )
+        return x
+
+
 class TestQuantization(TestCase):
     def compare_dict_tensors(self, ref_dict, res_dict, rtol=1e-3, atol=1e-3):
         if len(set(ref_dict.keys())) != len(set(res_dict.keys())):
@@ -106,10 +121,11 @@ class TestQuantization(TestCase):
                 "use_scaling": True,
                 "size_in_mb": 0.0,
                 "exclude_primals": True,
+                "allowed_dtypes": "torch.bfloat16;torch.float32",
             },
         },
     )
-    def test_activation_quantization_aten(self):
+    def test_activation_quantization_aten_with_scaling(self):
         counters.clear()
         module = TargetCPModule().to(GPU_TYPE)
         input = [
@@ -159,6 +175,59 @@ class TestQuantization(TestCase):
         self.assertTrue(torch.allclose(ref, res))
         counters.clear()
 
+    @requires_gpu()
+    @torch._inductor.config.patch(
+        pre_grad_fusion_options={},
+        post_grad_fusion_options={
+            "activation_quantization_aten_pass": {
+                "quant_type": "torch.float8_e5m2",
+                "use_scaling": False,
+                "size_in_mb": 0.0,
+                "exclude_primals": True,
+                "allowed_dtypes": "torch.bfloat16;torch.float32",
+            },
+        },
+    )
+    def test_activation_quantization_aten_without_scaling(self):
+        counters.clear()
+
+        module = LayernormNN().to(GPU_TYPE)
+        normalized_shape = [256]
+        input = [
+            torch.randn(
+                (1, 3, 256), requires_grad=True, device=GPU_TYPE, dtype=torch.bfloat16
+            ),
+            normalized_shape,
+            torch.randn(
+                *normalized_shape,
+                requires_grad=True,
+                device=GPU_TYPE,
+                dtype=torch.bfloat16,
+            ),
+            torch.randn(
+                *normalized_shape,
+                requires_grad=True,
+                device=GPU_TYPE,
+                dtype=torch.bfloat16,
+            ),
+        ]
+        traced = torch.compile(module)
+        ref = module(*input)
+        res = traced(*input)
+        self.compare_pred(module, traced, input)
+        ref.sum().backward()
+        res.sum().backward()
+        self.compare_parameters(module, traced)
+        self.compare_gradients(module, traced)
+        self.assertEqual(
+            counters["inductor"]["activation_quantization_fwd_aten_pass"], 1
+        )
+        self.assertEqual(
+            counters["inductor"]["activation_quantization_bwd_aten_pass"], 1
+        )
+        self.assertTrue(torch.allclose(ref, res))
+        counters.clear()
+
 
 if __name__ == "__main__":
     if IS_LINUX and HAS_GPU:

torch/_functorch/partitioners.py

@@ -379,7 +379,7 @@ def calculate_quantization_scaling(
         scale_node = graph.call_function(
             torch.ops.prims.convert_element_type.default,
             args=(mul_node, torch.float32),
-            name="scale_" + str(node.name),
+            name="fp8_scale_" + str(node.name),
         )
         scale_node.meta["val"] = torch.ops.prims.convert_element_type.default(
             mul_node.meta["val"], torch.float32
@@ -444,7 +444,7 @@ def perform_quantization(
         quant_activation_node = graph.call_function(
             torch.ops.prims.convert_element_type.default,
             args=(clamp_max_scaled_node, quant_type),
-            name="quant_" + str(node.name),
+            name="fp8_quant_" + str(node.name),
         )
         quant_activation_node.meta[
             "val"
@@ -513,13 +513,8 @@ def calculate_range(dtype: torch.dtype) -> tuple:
     Returns:
         tuple: A tuple containing the minimum and maximum values.
     """
-    if dtype == torch.float8_e5m2:
-        # 8-bit floating-point format with e5m2 layout
-        min_val = -57344.0
-        max_val = 57344.0
-    else:
-        raise ValueError(f"Unsupported dtype: {dtype}")
-    return min_val, max_val
+    info = torch.finfo(dtype)
+    return info.min, info.max
 
 
 def quantize_activation_fw(graph: torch.fx.Graph) -> None:
@@ -535,7 +530,7 @@ def quantize_activation_fw(graph: torch.fx.Graph) -> None:
             # case: use scaling
             if torch._inductor.config.post_grad_fusion_options[
                 "activation_quantization_aten_pass"
-            ].get("use_scaling", False):
+            ].get("use_scaling", True):
                 # calculating the scale
                 scale_node = calculate_quantization_scaling(
                     graph, node, clamp_max, 1e-12
@@ -554,7 +549,7 @@ def quantize_activation_fw(graph: torch.fx.Graph) -> None:
                     quant_node = graph.call_function(
                         torch.ops.prims.convert_element_type.default,
                         args=(node, quant_type),
-                        name="quant_" + str(node.name),
+                        name="fp8_quant_" + str(node.name),
                     )
                     quant_node.meta[
                         "val"
@@ -595,12 +590,13 @@ def quantize_activation_bw(graph: torch.fx.Graph) -> None:
                 # case: use scaling
                 with graph.inserting_after(node):
                     # find corresponding scale node
-                    scale_name = "scale_" + node.name.replace("quant_", "")
+                    scale_name = "fp8_scale_" + node.name.replace("fp8_quant_", "")
                     scale_node = next(
                         bwd_input
                         for bwd_input in bw_inputs
                         if bwd_input.name == scale_name
                     )
+                with graph.inserting_after(scale_node):
                     activation_node = graph.call_function(
                         torch.ops.prims.convert_element_type.default,
                         args=(node, dequant_type),
@@ -613,7 +609,7 @@ def quantize_activation_bw(graph: torch.fx.Graph) -> None:
                     activation_node.meta["tensor_meta"] = extract_tensor_metadata(
                         activation_node.meta["val"]
                     )
-                with graph.inserting_after(scale_node):
+                with graph.inserting_after(activation_node):
                     divided_target_node_32 = graph.call_function(
                         torch.ops.aten.div.Tensor,
                         args=(activation_node, scale_node),
@@ -725,11 +721,22 @@ def enable_activation_quantization(
         ),
     )
 
+    trace_structured(
+        "artifact",
+        metadata_fn=lambda: {
+            "name": "before_activation_quantization_bwd_aten_pass",
+            "encoding": "string",
+        },
+        payload_fn=lambda: bwd_module.print_readable(
+            print_output=False, include_stride=True, include_device=True
+        ),
+    )
+
     quant_fwd_module_outputs = fwd_module.graph.find_nodes(op="output")[0].args[0]
     # update the corresponding bwd_inputs due to the fwd_outputs quantization
     for fwd_node in quant_fwd_module_outputs:
-        if "quant_" in fwd_node.name:
-            bwd_input = bwd_module_inputs[fwd_node.name.replace("quant_", "")]
+        if "fp8_quant_" in fwd_node.name:
+            bwd_input = bwd_module_inputs[fwd_node.name.replace("fp8_quant_", "")]
             with bwd_module.graph.inserting_after(bwd_input):
                 quant_bwd_input = bwd_module.graph.placeholder(name=fwd_node.name)
             dequant_type = bwd_input.meta["dequant_type"]
@@ -741,34 +748,24 @@ def enable_activation_quantization(
     # update the bwd_inputs if quantization with scaling is used
     if torch._inductor.config.post_grad_fusion_options[
         "activation_quantization_aten_pass"
-    ].get("use_scaling", False):
+    ].get("use_scaling", True):
+        quant_bwd_module_inputs = list(bwd_module.graph.find_nodes(op="placeholder"))
         # update the corresponding bwd input nodes find the last non-tangent node
-        bwd_input_loc = list(bwd_module_inputs.values())[-1]
-        for bw_input in reversed(bwd_module_inputs.values()):
+        bwd_input_loc = quant_bwd_module_inputs[-1]
+        for bw_input in reversed(quant_bwd_module_inputs):
             if not _is_tangent(bw_input):
                 bwd_input_loc = bw_input
                 break
 
         scaled_fwd_module_outputs = fwd_module.graph.find_nodes(op="output")[0].args[0]
         for fwd_node in scaled_fwd_module_outputs:
-            if "scale_" in fwd_node.name:
+            if "fp8_scale_" in fwd_node.name:
                 # fwd node is a scale node
                 with bwd_module.graph.inserting_after(bwd_input_loc):
                     scale_bwd_input = bwd_module.graph.placeholder(name=fwd_node.name)
                 scale_bwd_input.meta.update(fwd_node.meta)
                 bwd_input_loc = scale_bwd_input
 
-    trace_structured(
-        "artifact",
-        metadata_fn=lambda: {
-            "name": "before_activation_quantization_bwd_aten_pass",
-            "encoding": "string",
-        },
-        payload_fn=lambda: bwd_module.print_readable(
-            print_output=False, include_stride=True, include_device=True
-        ),
-    )
-
     quantize_activation_bw(bwd_module.graph)
 
     trace_structured(