refactor(imperative): move typecvt code of sereval ops to c++

GitOrigin-RevId: 4ffaa376c1f8edaaff7a9c9fb14cbe3ddd186515

imperative/python/megengine/functional/nn.py

@@ -320,12 +320,6 @@ def conv3d(
     stride = _triple_nonzero(stride)
     dilate = _triple_nonzero(dilation)
 
-    dtype = dtype_promotion(inp, weight)
-    if inp.dtype != dtype:
-        inp = inp.astype(dtype)
-    if weight.dtype != dtype:
-        weight = weight.astype(dtype)
-
     sparse_type = "dense" if groups == 1 else "group"
     op = builtin.Convolution3D(
         pad_d=pad[D],
@@ -389,15 +383,6 @@ def conv_transpose2d(
         conv_mode.lower() == "cross_correlation"
         or conv_mode.name == "CROSS_CORRELATION"
     )
-    if amp._enabled:
-        compute_mode = "float32"
-        inp, weight, bias = cast_tensors(inp, weight, bias)
-    else:
-        dtype = dtype_promotion(inp, weight)
-        if inp.dtype != dtype:
-            inp = inp.astype(dtype)
-        if weight.dtype != dtype:
-            weight = weight.astype(dtype)
 
     stride_h, stride_w = expand_hw(stride)
     pad_h, pad_w = expand_hw(padding)
@@ -418,6 +403,8 @@ def conv_transpose2d(
     )
     (output,) = apply(op, weight, inp)
     if bias is not None:
+        if amp._enabled:
+            bias = cast_tensors(bias)
         output += bias
     return output
 
@@ -591,12 +578,6 @@ def conv_transpose3d(
     stride = _triple_nonzero(stride)
     dilate = _triple_nonzero(dilation)
 
-    dtype = dtype_promotion(inp, weight)
-    if inp.dtype != dtype:
-        inp = inp.astype(dtype)
-    if weight.dtype != dtype:
-        weight = weight.astype(dtype)
-
     sparse_type = "dense" if groups == 1 else "group"
     op = builtin.Convolution3DBackwardData(
         pad_d=pad[D],

imperative/src/impl/transformations/dtype_promote.cpp

@@ -183,6 +183,38 @@ ValueRefList convolution_rule(const OpDef& op, Span<ValueRef> inputs) {
     return imperative::apply(op, converted);
 }
 
+// differ from Convolution, ConvolutionBackwardData is used in both
+// functional.conv_transpose2d and quantize.conv_transpose2d
+ValueRefList convolution_backward_rule(const OpDef& op, Span<ValueRef> inputs) {
+    auto&& conv_op = const_cast<ConvolutionBackwardData&>(
+            op.cast_final_safe<ConvolutionBackwardData>());
+    SmallVector<DType> dtypes = get_value_dtypes(inputs);
+
+    if (is_quantized_dtype(dtypes[0]) && is_quantized_dtype(dtypes[1])) {
+        return imperative::apply(op, inputs);
+    }
+
+    mgb::DType target_dtype;
+    if (DTypePromoteCfg::amp_dtype_autocast_enabled) {
+        conv_op.compute_mode = ConvolutionBackwardData::ComputeMode::FLOAT32;
+        target_dtype = DTypePromoteCfg::amp_low_prec_dtype;
+    } else {
+        target_dtype = get_promoted_dtype(dtypes);
+    }
+
+    ValueRefList converted(inputs.size());
+    for (size_t i = 0; i < inputs.size(); ++i) {
+        if (dtypes[i] != target_dtype) {
+            converted[i] = imperative::apply(
+                    ApplyOp(*TypeCvt::make(target_dtype)), inputs[i])[0];
+        } else {
+            converted[i] = inputs[i];
+        }
+    }
+
+    return imperative::apply(op, converted);
+}
+
 ValueRefList batch_norm_rule(const OpDef& op, Span<ValueRef> inputs) {
     if (DTypePromoteCfg::amp_dtype_autocast_enabled) {
         mgb_assert(inputs.size() > 0);
@@ -208,12 +240,32 @@ ValueRefList batch_norm_rule(const OpDef& op, Span<ValueRef> inputs) {
     return imperative::apply(op, inputs);
 }
 
+ValueRefList convolution3d_rule(const OpDef& op, Span<ValueRef> inputs) {
+    SmallVector<DType> dtypes = get_value_dtypes(inputs);
+    mgb::DType target_dtype = get_promoted_dtype(dtypes);
+
+    ValueRefList converted(inputs.size());
+    for (size_t i = 0; i < inputs.size(); ++i) {
+        if (dtypes[i] != target_dtype) {
+            converted[i] = imperative::apply(
+                    ApplyOp(*TypeCvt::make(target_dtype)), inputs[i])[0];
+        } else {
+            converted[i] = inputs[i];
+        }
+    }
+
+    return imperative::apply(op, converted);
+}
+
 struct DTypePromoteRuleRegistry {
     DTypePromoteRuleRegistry() {
         register_dtype_promote_rule<Elemwise>(elemwise_rule);
         register_dtype_promote_rule<Reduce>(reduce_rule);
         register_dtype_promote_rule<Convolution>(convolution_rule);
+        register_dtype_promote_rule<ConvolutionBackwardData>(convolution_backward_rule);
         register_dtype_promote_rule<BatchNorm>(batch_norm_rule);
+        register_dtype_promote_rule<Convolution3D>(convolution3d_rule);
+        register_dtype_promote_rule<Convolution3DBackwardData>(convolution3d_rule);
     }
 } register_helper;