[fx]patch nn.functional convolution (#1528)

colossalai/fx/tracer/meta_patch/patched_function/__init__.py

@@ -3,4 +3,5 @@ from .arithmetic import *
 from .embedding import *
 from .normalization import *
 from .python_ops import *
-from .torch_ops import *
+from .torch_ops import *
+from .convolution import *

colossalai/fx/tracer/meta_patch/patched_function/convolution.py

@@ -0,0 +1,178 @@
+import torch
+import collections
+from itertools import repeat
+from ..registry import meta_patched_function
+import math
+
+
+def _ntuple(n, name="parse"):
+
+    def parse(x):
+        if isinstance(x, collections.abc.Iterable):
+            return tuple(x)
+        return tuple(repeat(x, n))
+
+    parse.__name__ = name
+    return parse
+
+
+_single = _ntuple(1, "_single")
+_pair = _ntuple(2, "_pair")
+_triple = _ntuple(3, "_triple")
+
+
+def _extract_kwargs(kwargs):
+    if 'stride' in kwargs:
+        stride = kwargs['stride']
+    else:
+        stride = 1
+    # TODO: process str type padding
+    if 'padding' in kwargs:
+        padding = kwargs['padding']
+    else:
+        padding = 0
+    if 'dilation' in kwargs:
+        dilation = kwargs['dilation']
+    else:
+        dilation = 1
+    if 'output_padding' in kwargs:
+        output_padding = kwargs['output_padding']
+    else:
+        output_padding = 0
+
+    return stride, padding, dilation, output_padding
+
+
+@meta_patched_function.register(torch.nn.functional.conv1d)
+def torch_nn_functional_conv1d(input, weight, **kwargs):
+    stride, padding, dilation, _ = _extract_kwargs(kwargs)
+
+    stride = _single(stride)
+    padding = _single(padding)
+    dilation = _single(dilation)
+
+    kernel_size = weight.shape[2:]
+    l_in = input.shape[-1]
+    c_out = weight.shape[0]
+    l_out = math.floor((l_in + 2 * padding[0] - dilation[0] * (kernel_size[0] - 1) - 1) / stride[0] + 1)
+    result_shape = input.shape[:-2] + (
+        c_out,
+        l_out,
+    )
+    return torch.empty(result_shape, device='meta')
+
+
+@meta_patched_function.register(torch.nn.functional.conv2d)
+def torch_nn_functional_conv2d(input, weight, **kwargs):
+    stride, padding, dilation, _ = _extract_kwargs(kwargs)
+
+    stride = _pair(stride)
+    padding = _pair(padding)
+    dilation = _pair(dilation)
+
+    kernel_size = weight.shape[2:]
+    h_in, w_in = input.shape[-2:]
+    c_out = weight.shape[0]
+    h_out = math.floor((h_in + 2 * padding[0] - dilation[0] * (kernel_size[0] - 1) - 1) / stride[0] + 1)
+    w_out = math.floor((w_in + 2 * padding[1] - dilation[1] * (kernel_size[1] - 1) - 1) / stride[1] + 1)
+    result_shape = input.shape[:-3] + (
+        c_out,
+        h_out,
+        w_out,
+    )
+    return torch.empty(result_shape, device='meta')
+
+
+@meta_patched_function.register(torch.nn.functional.conv3d)
+def torch_nn_functional_conv3d(input, weight, **kwargs):
+    stride, padding, dilation, _ = _extract_kwargs(kwargs)
+
+    stride = _triple(stride)
+    padding = _triple(padding)
+    dilation = _triple(dilation)
+
+    kernel_size = weight.shape[2:]
+    d_in, h_in, w_in = input.shape[-3:]
+    c_out = weight.shape[0]
+    d_out = math.floor((d_in + 2 * padding[0] - dilation[0] * (kernel_size[0] - 1) - 1) / stride[0] + 1)
+    h_out = math.floor((h_in + 2 * padding[1] - dilation[1] * (kernel_size[1] - 1) - 1) / stride[1] + 1)
+    w_out = math.floor((w_in + 2 * padding[2] - dilation[2] * (kernel_size[2] - 1) - 1) / stride[2] + 1)
+    result_shape = input.shape[:-4] + (
+        c_out,
+        d_out,
+        h_out,
+        w_out,
+    )
+    return torch.empty(result_shape, device='meta')
+
+
+@meta_patched_function.register(torch.nn.functional.conv_transpose1d)
+def torch_nn_functional_convtranspose1d(input, weight, **kwargs):
+    stride, padding, dilation, output_padding = _extract_kwargs(kwargs)
+
+    stride = _single(stride)
+    padding = _single(padding)
+    dilation = _single(dilation)
+    output_padding = _single(output_padding)
+
+    kernel_size = weight.shape[2:]
+    l_in = input.shape[-1]
+    c_out = weight.shape[1]
+    l_out = math.floor((l_in - 1) * stride[0] - 2 * padding[0] + dilation[0] * (kernel_size[0] - 1) +
+                       output_padding[0] + 1)
+    result_shape = input.shape[:-2] + (
+        c_out,
+        l_out,
+    )
+    return torch.empty(result_shape, device='meta')
+
+
+@meta_patched_function.register(torch.nn.functional.conv_transpose2d)
+def torch_nn_functional_convtranspose2d(input, weight, **kwargs):
+    stride, padding, dilation, output_padding = _extract_kwargs(kwargs)
+
+    stride = _pair(stride)
+    padding = _pair(padding)
+    dilation = _pair(dilation)
+    output_padding = _pair(output_padding)
+
+    kernel_size = weight.shape[2:]
+    h_in, w_in = input.shape[-2:]
+    c_out = weight.shape[1]
+    h_out = math.floor((h_in - 1) * stride[0] - 2 * padding[0] + dilation[0] * (kernel_size[0] - 1) +
+                       output_padding[0] + 1)
+    w_out = math.floor((w_in - 1) * stride[1] - 2 * padding[1] + dilation[1] * (kernel_size[1] - 1) +
+                       output_padding[1] + 1)
+    result_shape = input.shape[:-3] + (
+        c_out,
+        h_out,
+        w_out,
+    )
+    return torch.empty(result_shape, device='meta')
+
+
+@meta_patched_function.register(torch.nn.functional.conv_transpose3d)
+def torch_nn_functional_convtranspose3d(input, weight, **kwargs):
+    stride, padding, dilation, output_padding = _extract_kwargs(kwargs)
+
+    stride = _triple(stride)
+    padding = _triple(padding)
+    dilation = _triple(dilation)
+    output_padding = _triple(output_padding)
+
+    kernel_size = weight.shape[2:]
+    d_in, h_in, w_in = input.shape[-3:]
+    c_out = weight.shape[1]
+    d_out = math.floor((d_in - 1) * stride[0] - 2 * padding[0] + dilation[0] * (kernel_size[0] - 1) +
+                       output_padding[0] + 1)
+    h_out = math.floor((h_in - 1) * stride[1] - 2 * padding[1] + dilation[1] * (kernel_size[1] - 1) +
+                       output_padding[1] + 1)
+    w_out = math.floor((w_in - 1) * stride[2] - 2 * padding[2] + dilation[2] * (kernel_size[2] - 1) +
+                       output_padding[2] + 1)
+    result_shape = input.shape[:-4] + (
+        c_out,
+        d_out,
+        h_out,
+        w_out,
+    )
+    return torch.empty(result_shape, device='meta')