feat: support additional Tensor accuracy APIs

Author: cangtianhuang

tester/base.py

@@ -406,6 +406,12 @@ def get_arg(api_config, arg_pos, arg_name, default=None):
 # differs materially from any public API.
 no_signature_api_mappings.update(
     {
+        # copy_ 是内建方法，无法通过 inspect 获取签名。
+        "paddle.Tensor.copy_": {
+            "self": lambda cfg: get_arg(cfg, 0, "self"),
+            "other": lambda cfg: get_arg(cfg, 1, "other"),
+            "blocking": lambda cfg: get_arg(cfg, 2, "blocking", True),
+        },
         # adamw_(param, grad, lr, moment1, moment2, moment2_max,
         #        beta1_pow, beta2_pow, master_param, skip_update,
         #        beta1, beta2, epsilon, lr_ratio, coeff, with_decay,

tester/base_config.yaml

@@ -173,6 +173,7 @@ handle_axes_api:
 
 forward_only_apis:
   - _run_custom_op
+  - copy_
   - moe_permute
   - moe_unpermute
   - fp8_quant_blockwise

tester/paddle_to_torch/mapping.json

@@ -5049,5 +5049,48 @@
     },
     "paddle._C_ops._run_custom_op": {
         "Rule": "CopsRunCustomOpRule"
+    },
+    "paddle.Tensor.add_": {
+        "Rule": "CopsAdd_Rule"
+    },
+    "paddle.Tensor.contiguous": {
+        "torch_api": "torch.Tensor.contiguous"
+    },
+    "paddle.Tensor.copy_": {
+        "torch_api": "torch.Tensor.copy_",
+        "torch_args": [],
+        "torch_kwargs": {
+            "other": "other",
+            "non_blocking": "not blocking"
+        }
+    },
+    "paddle.Tensor.flatten_": {
+        "Rule": "CopsFlatten_Rule"
+    },
+    "paddle.Tensor.multiply_": {
+        "Rule": "CopsMultiply_Rule"
+    },
+    "paddle.Tensor.put_along_axis_": {
+        "Rule": "CopsPutAlongAxis_Rule"
+    },
+    "paddle.Tensor.scale_": {
+        "Rule": "CopsScale_Rule"
+    },
+    "paddle.Tensor.subtract_": {
+        "Rule": "CopsSubtract_Rule"
+    },
+    "paddle.Tensor.to": {
+        "Rule": "TensorToRule"
+    },
+    "paddle.randint": {
+        "torch_api": "torch.randint",
+        "torch_args": [
+            "low",
+            "high",
+            "tuple(shape) if isinstance(shape, list) else shape"
+        ],
+        "torch_kwargs": {
+            "dtype": "locals().get(\"dtype\") if locals().get(\"dtype\") is not None else torch.int64"
+        }
     }
 }

tester/paddle_to_torch/rules.py

@@ -7400,8 +7400,9 @@ def apply(self, paddle_api: str) -> ConvertResult:
         core = """
 x = locals().get("x")
 y = locals().get("y")
+alpha = locals().get("alpha", 1)
 with torch.no_grad():
-    x.sub_(y)
+    x.sub_(y.to(x.dtype), alpha=alpha)
 result = x
 """
         code = Code(core=core.splitlines())
@@ -7421,8 +7422,9 @@ def apply(self, paddle_api: str) -> ConvertResult:
         core = """
 x = locals().get("x")
 y = locals().get("y")
+alpha = locals().get("alpha", 1)
 with torch.no_grad():
-    x.add_(y.to(x.dtype))
+    x.add_(y.to(x.dtype), alpha=alpha)
 result = x
 """
         code = Code(core=core.splitlines())
@@ -7513,7 +7515,7 @@ class CopsPutAlongAxis_Rule(BaseRule):
 
     def apply(self, paddle_api: str) -> ConvertResult:
         core = """
-arr          = locals().get("arr")
+arr          = locals().get("arr", locals().get("x"))
 indices      = locals().get("indices")
 values       = locals().get("values")
 axis         = locals().get("axis")
@@ -7606,6 +7608,41 @@ def apply(self, paddle_api: str) -> ConvertResult:
         return ConvertResult.success(paddle_api, code, is_torch_corresponding=False)
 
 
+class TensorToRule(BaseRule):
+    """Translate Paddle Tensor.to device and dtype values for Torch."""
+
+    def apply(self, paddle_api: str) -> ConvertResult:
+        core = """
+tensor = locals().get("self")
+to_args = list(locals().get("args", ()))
+to_kwargs = dict(locals().get("kwargs", {}))
+to_kwargs.pop("self", None)
+to_kwargs.pop("args", None)
+dtype_names = {
+    "bool", "float16", "float32", "float64", "bfloat16",
+    "int8", "int16", "int32", "int64", "uint8", "complex64", "complex128",
+}
+
+def _translate_to_value(value, dtype_names=dtype_names):
+    if isinstance(value, str) and value.startswith("gpu"):
+        return "cuda" + value[3:]
+    dtype_name = str(value).split(".")[-1]
+    if dtype_name in dtype_names:
+        return getattr(torch, dtype_name)
+    return value
+
+if to_args:
+    to_args[0] = _translate_to_value(to_args[0])
+if "device" in to_kwargs:
+    to_kwargs["device"] = _translate_to_value(to_kwargs["device"])
+if "dtype" in to_kwargs:
+    to_kwargs["dtype"] = _translate_to_value(to_kwargs["dtype"])
+result = tensor.to(*to_args, **to_kwargs) if to_args or to_kwargs else tensor
+"""
+        code = Code(core=core.splitlines())
+        return ConvertResult.success(paddle_api, code)
+
+
 class CopsTransposeRule(BaseRule):
     """paddle._C_ops.transpose(x, perm) → torch.permute(x, dims)