Add Ops(_native_batch_norm_legit_functional) | feat(torchlib)

onnxscript/function_libs/torch_lib/ops/core.py

@@ -2046,7 +2046,7 @@ def aten_convolution_overrideable(
     raise NotImplementedError()
 
 
-@torch_op("aten::copy")
+@torch_op(("aten::copy", "aten::_to_copy"))
 def aten_copy(
     self: TTensor, src: TTensor, non_blocking: bool = False  # pylint: disable=unused-argument
 ) -> TTensor:
@@ -5456,6 +5456,20 @@ def aten__native_batch_norm_no_training(
     )
 
 
+@torch_op("aten::_native_batch_norm_legit.no_stats", trace_only=True)
+def aten__native_batch_norm_no_stats(
+    input: TFloat,
+    weight: Optional[TFloat] = None,
+    bias: Optional[TFloat] = None,
+    training: bool = False,
+    momentum: float = 0.9,
+    eps: float = 1e-05,
+) -> Tuple[TFloat, TFloat, TFloat]:
+    """_native_batch_norm_legit.no_stats(Tensor input, Tensor? weight, Tensor? bias, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)"""
+
+    return aten_native_batch_norm(input, weight, bias, None, None, training, momentum, eps)
+
+
 @torch_op(("aten::native_batch_norm", "aten::_native_batch_norm_legit"), trace_only=True)
 def aten_native_batch_norm(
     input: TFloat,
@@ -5556,12 +5570,131 @@ def _aten_native_batch_norm_inference_onnx(
         momentum=momentum,
         training_mode=training,
     )
+    # NOTE: mean and var are omitted in inference mode
     # Cannot return 2 dup output, so have to do twice with different variable name
-    empty_mean = op.Cast(op.Shape(input, start=0, end=0), to=FLOAT.dtype)
-    empty_var = op.Cast(op.Shape(input, start=0, end=0), to=FLOAT.dtype)
+    empty_mean = op.CastLike(op.Shape(input, start=0, end=0), norm)
+    empty_var = op.CastLike(op.Shape(input, start=0, end=0), norm)
     return norm, empty_mean, empty_var
 
 
+# TODO: This op is using duplicated code from aten_native_batch_norm,
+#       need to refactor it later. https://github.com/microsoft/onnxscript/issues/1125
+# NOTE: This op is invoked by PyTorch Functionalization, and not in
+# native_functions.yaml, It can be found in torch/_decomp/decompositions.py
+@torch_op("aten::_native_batch_norm_legit_functional", trace_only=True)
+def aten__native_batch_norm_legit_functional(
+    input: TFloat,
+    weight: Optional[TFloat] = None,
+    bias: Optional[TFloat] = None,
+    running_mean: Optional[TFloat] = None,
+    running_var: Optional[TFloat] = None,
+    training: bool = False,
+    momentum: float = 0.9,
+    eps: float = 1e-05,
+) -> Tuple[TFloat, TFloat, TFloat, TFloat, TFloat]:
+    if weight is None:  # Set to 1.0 as default
+        weight = op.Expand(op.Constant(value_floats=[1.0]), op.Shape(input, start=1, end=2))
+
+    if bias is None:  # Set to 0.0 as default
+        bias = op.Expand(op.Constant(value_floats=[0.0]), op.Shape(input, start=1, end=2))
+
+    axes = list(range(len(input.shape)))
+    axes.pop(1)
+    axes = op.Constant(value_ints=axes)
+    if running_mean is None:  # Using input mean
+        running_mean = op.Squeeze(op.ReduceMean(input, axes))
+
+    if running_var is None:  # Using input var
+        mean = op.ReduceMean(input, axes)
+        input_sub_mean = op.Sub(input, mean)
+        sqr_input_sub_mean = op.Mul(input_sub_mean, input_sub_mean)
+        running_var = op.Squeeze(op.ReduceMean(sqr_input_sub_mean, axes))
+
+    # Have to split to 2 private functions, because training_function return 3 outputs
+    # While inference_function return 1 output
+    if training is True:
+        norm, mean, var, new_mean, new_var = _aten__native_batch_norm_training_functional_onnx(
+            input, weight, bias, running_mean, running_var, axes, training, momentum, eps
+        )
+    else:
+        (
+            norm,
+            mean,
+            var,
+            new_mean,
+            new_var,
+        ) = _aten__native_batch_norm_inference_functional_onnx(
+            input, weight, bias, running_mean, running_var, training, momentum, eps
+        )
+    return norm, mean, var, new_mean, new_var
+
+
+@torch_op("aten::_native_batch_norm_legit_functional", private=True)
+def _aten__native_batch_norm_training_functional_onnx(
+    input: TFloat,
+    weight: TFloat,
+    bias: TFloat,
+    running_mean: TFloat,
+    running_var: TFloat,
+    axes: INT64,
+    training: bool,
+    momentum: float,
+    eps: float,
+) -> Tuple[TFloat, TFloat, TFloat, TFloat, TFloat]:
+    # Assert(training is True)
+    norm, running_mean, running_var = op.BatchNormalization(
+        input,
+        weight,
+        bias,
+        running_mean,
+        running_var,
+        epsilon=eps,
+        momentum=momentum,
+        training_mode=training,
+    )
+    # Compute var and rstd
+    mean = op.ReduceMean(input, axes)
+    input_sub_mean = op.Sub(input, mean)
+    sqr = op.Mul(input_sub_mean, input_sub_mean)
+    var = op.ReduceMean(sqr, axes, keepdims=False)
+    rstd = op.Div(1.0, op.Sqrt(var + eps))
+    # Get mean again with size = [1, C]
+    mean = op.ReduceMean(input, axes, keepdims=False)
+    # NOTE: Fixed to be FLOAT dtype
+    running_mean = op.Cast(running_mean, to=FLOAT.dtype)
+    running_var = op.Cast(running_var, to=FLOAT.dtype)
+    return norm, mean, rstd, running_mean, running_var
+
+
+@torch_op("aten::_native_batch_norm_legit_functional", private=True)
+def _aten__native_batch_norm_inference_functional_onnx(
+    input: TFloat,
+    weight: TFloat,
+    bias: TFloat,
+    running_mean: TFloat,
+    running_var: TFloat,
+    training: bool,
+    momentum: float,
+    eps: float,
+) -> Tuple[TFloat, TFloat, TFloat, TFloat, TFloat]:
+    # Assert(training is False)
+    norm = op.BatchNormalization(
+        input,
+        weight,
+        bias,
+        running_mean,
+        running_var,
+        epsilon=eps,
+        momentum=momentum,
+        training_mode=training,
+    )
+    # NOTE: mean and var are ommited in inference mode
+    # Cannot return 2 dup output, so have to do twice with different variable name
+    empty_mean = op.CastLike(op.Shape(input, start=0, end=0), norm)
+    empty_var = op.CastLike(op.Shape(input, start=0, end=0), norm)
+    return norm, empty_mean, empty_var, running_mean, running_var
+
+
 def aten_native_batch_norm_backward(
     grad_out: TensorType,
     input: TensorType,

onnxscript/tests/function_libs/torch_lib/extra_opinfo.py

@@ -1289,6 +1289,52 @@ def sample_inputs_scaled_dot_product_flash_attention(
     yield from samples
 
 
+# NOTE: In `_native_batch_norm_legit` tests, it generates two kinds of args:
+# 1. (input, weight, bias, running_mean, running_var, training, momentum, eps)
+# 2. (input, weight, bias, training, momentum, eps)
+# which requires two function signatures to take the inputs, that's why we have
+# two sample_inputs functions here instead.
+def sample_inputs__native_batch_norm_legit(op_info, device, dtype, requires_grad, **kwargs):
+    samples = common_methods_invocations.sample_inputs_batch_norm(
+        op_info, device, dtype, requires_grad, **kwargs
+    )
+    for sample in samples:
+        # torch.native_batch_norm does not support 0 numel tensors
+        # IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)
+        if sample.input.numel() == 0:
+            continue
+        args = sample.args
+        training = sample.kwargs.get("training", True)
+        momentum = sample.kwargs.get("momentum", 0.5)
+        eps = sample.kwargs.get("eps", 1e-5)
+        if args[0] is not None and args[1] is not None:
+            yield opinfo_core.SampleInput(
+                sample.input,
+                args=(args[2], args[3], args[0], args[1], training, momentum, eps),
+            )
+
+
+def sample_inputs__native_batch_norm_legit_no_stats(
+    op_info, device, dtype, requires_grad, **kwargs
+):
+    samples = common_methods_invocations.sample_inputs_batch_norm(
+        op_info, device, dtype, requires_grad, **kwargs
+    )
+    for sample in samples:
+        # torch.native_batch_norm does not support 0 numel tensors
+        # IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)
+        if sample.input.numel() == 0:
+            continue
+        args = sample.args
+        training = sample.kwargs.get("training", True)
+        momentum = sample.kwargs.get("momentum", 0.5)
+        eps = sample.kwargs.get("eps", 1e-5)
+        if args[0] is not None and args[1] is None:
+            yield opinfo_core.SampleInput(
+                sample.input, args=(args[2], args[3], training, momentum, eps)
+            )
+
+
 # NOTE: How to create an OpInfo:
 # 1. Create a function that generates sample inputs for the op.
 #    This function should yield SampleInputs.
@@ -1633,4 +1679,34 @@ def sample_inputs_scaled_dot_product_flash_attention(
         supports_fwgrad_bwgrad=True,
         check_batched_forward_grad=False,
     ),
+    opinfo_core.OpInfo(
+        "ops.aten._native_batch_norm_legit",
+        aten_name="_native_batch_norm_legit",
+        dtypes=common_dtype.floating_types_and(torch.bfloat16),
+        dtypesIfCUDA=common_dtype.floating_types_and(torch.float16, torch.bfloat16),
+        supports_forward_ad=True,
+        supports_fwgrad_bwgrad=True,
+        assert_jit_shape_analysis=True,
+        sample_inputs_func=sample_inputs__native_batch_norm_legit,
+    ),
+    opinfo_core.OpInfo(
+        "ops.aten._native_batch_norm_legit_functional",
+        aten_name="_native_batch_norm_legit_functional",
+        dtypes=common_dtype.floating_types_and(torch.bfloat16),
+        dtypesIfCUDA=common_dtype.floating_types_and(torch.float16, torch.bfloat16),
+        supports_forward_ad=True,
+        supports_fwgrad_bwgrad=True,
+        assert_jit_shape_analysis=True,
+        sample_inputs_func=sample_inputs__native_batch_norm_legit,
+    ),
+    opinfo_core.OpInfo(
+        "ops.aten._native_batch_norm_legit.no_stats",
+        aten_name="_native_batch_norm_legit.no_stats",
+        dtypes=common_dtype.floating_types_and(torch.bfloat16),
+        dtypesIfCUDA=common_dtype.floating_types_and(torch.float16, torch.bfloat16),
+        supports_forward_ad=True,
+        supports_fwgrad_bwgrad=True,
+        assert_jit_shape_analysis=True,
+        sample_inputs_func=sample_inputs__native_batch_norm_legit_no_stats,
+    ),
 ]

onnxscript/tests/function_libs/torch_lib/ops_test.py

@@ -184,7 +184,6 @@ def run_test_output_match(
 
     # Obtain the tolerance for the op
     rtol, atol = torchlib_op_info.get_tolerance(dtype)
-
     for i, cpu_sample in enumerate(samples):
         inputs = (cpu_sample.input, *cpu_sample.args)
         # Provide the repr to subtest because tensors are not serializable in parallel test runs

onnxscript/tests/function_libs/torch_lib/ops_test_data.py

@@ -1680,6 +1680,20 @@ def _where_input_wrangler(
         reason="fixme: 'shape' do not match: torch.Size([2, 3, 4, 3]) != torch.Size([2, 3, 4, 2]). https://github.com/microsoft/onnxscript/issues/975",
     ),
     TorchLibOpInfo("native_batch_norm", core_ops.aten_native_batch_norm, trace_only=True),
+    TorchLibOpInfo(
+        "ops.aten._native_batch_norm_legit", core_ops.aten_native_batch_norm, trace_only=True
+    ),
+    TorchLibOpInfo(
+        "ops.aten._native_batch_norm_legit.no_stats",
+        core_ops.aten__native_batch_norm_no_stats,
+        trace_only=True,
+    ),
+    TorchLibOpInfo(
+        "ops.aten._native_batch_norm_legit_functional",
+        core_ops.aten__native_batch_norm_legit_functional,
+        trace_only=True,
+        compare_shape_only_for_output=(3, 4),
+    ),
     TorchLibOpInfo(
         "ops.aten.native_group_norm",
         core_ops.aten_native_group_norm,