Author: mattip <[email protected]>
Branch: ufuncapi
Changeset: r74826:384b1e427559
Date: 2014-12-05 08:28 +0200
http://bitbucket.org/pypy/pypy/changeset/384b1e427559/
Log: simplify, test for not-implemented need to cast args
diff --git a/pypy/module/micronumpy/ufuncs.py b/pypy/module/micronumpy/ufuncs.py
--- a/pypy/module/micronumpy/ufuncs.py
+++ b/pypy/module/micronumpy/ufuncs.py
@@ -590,8 +590,10 @@
inargs0 = inargs[0]
assert isinstance(inargs0, W_NDimArray)
outarg_shapes = [inargs0.get_shape()] * self.nargs
- inargs, outargs = self.alloc_args(space, inargs, outargs,
+ inargs, outargs, need_to_cast = self.alloc_args(space, inargs,
outargs,
dtypes, outarg_shapes)
+ if any(need_to_cast):
+ raise oefmt(space.w_NotImplementedError, "casting not
supported yet")
if self.stack_inputs:
arglist = space.newlist(list(inargs + outargs))
space.call_args(func, Arguments.frompacked(space, arglist))
@@ -602,9 +604,11 @@
if len(outargs) < 2:
return outargs[0]
return space.newtuple(outargs)
- iter_shape, outarg_shapes, matched_dims = self.verify_args(space,
inargs, outargs)
- inargs, outargs = self.alloc_args(space, inargs, outargs, dtypes,
- outarg_shapes)
+ iter_shape, arg_shapes, matched_dims = self.verify_args(space, inargs,
outargs)
+ inargs, outargs, need_to_cast = self.alloc_args(space, inargs,
outargs, dtypes,
+ arg_shapes)
+ if any(need_to_cast):
+ raise oefmt(space.w_NotImplementedError, "casting not supported
yet")
w_flags = space.w_None # NOT 'external_loop', we do coalescing by
core_num_dims
w_op_flags = space.newtuple([space.wrap(r) for r in ['readonly'] *
len(inargs)] + \
[space.wrap(r) for r in ['readwrite'] *
len(outargs)])
@@ -769,79 +773,49 @@
dtypes[j] = _dtypes[i+j]
return i / self.nargs, dtypes
- def alloc_args(self, space, inargs, outargs, dtypes, outarg_shapes):
+ def alloc_args(self, space, inargs, outargs, dtypes, arg_shapes):
# Any None outarg are allocated, and inargs, outargs may need casting
inargs0 = inargs[0]
assert isinstance(inargs0, W_NDimArray)
order = inargs0.get_order()
+ need_to_cast = []
+ for i in range(self.nin):
+ curarg = inargs[i]
+ assert isinstance(curarg, W_NDimArray)
+ if len(arg_shapes[i]) != curarg.ndims():
+ # XXX reshape (after merge with default)
+ pass
+ need_to_cast.append(curarg.get_dtype() == dtypes[i])
for i in range(len(outargs)):
+ j = self.nin + i
if outargs[i] is None:
- j = self.nin + i
- outargs[i] = W_NDimArray.from_shape(space, outarg_shapes[i],
dtypes[j], order)
- for i in outargs:
- assert isinstance(i, W_NDimArray)
- return inargs, outargs
+ outargs[i] = W_NDimArray.from_shape(space, outarg_shapes[j],
dtypes[j], order)
+ elif len(arg_shapes[i]) != curarg.ndims():
+ # XXX reshape (after merge with default)
+ pass
+ need_to_cast.append(curarg.get_dtype() == dtypes[j])
+ return inargs, outargs, need_to_cast
def verify_args(self, space, inargs, outargs):
# Figure out the number of iteration dimensions, which
# is the broadcast result of all the input non-core
# dimensions
iter_shape = []
+ arg_shapes = []
max_matched_dims = 0
for i in self.core_dim_ixs:
if i > max_matched_dims:
max_matched_dims = i
matched_dims = [-1] * (1 + max_matched_dims)
- for i in range(self.nin):
- curarg = inargs[i]
- assert isinstance(curarg, W_NDimArray)
- dim_offset = self.core_offsets[i] # index into XXX_ixs
- num_dims = self.core_num_dims[i]
- # Make sure the last num_dims shape of curarg match the signature
- n = len(curarg.get_shape()) - num_dims
- if n < 0:
- raise oefmt(space.w_ValueError, "%s: Input operand %d does "
- "not have enough dimensions (has %d, gufunc with "
- "signature %s requires %d)", self.name, i,
- num_dims+n, self.signature, num_dims)
- dims_to_match = curarg.get_shape()[n:]
- dims_to_broadcast = curarg.get_shape()[:n]
- offset = len(dims_to_broadcast) - len(iter_shape)
- if offset >= 0:
- # Prepend extra dimensions to iter_shape
- iter_shape = dims_to_broadcast[:offset] + iter_shape
- offset = 0
- # Make sure iter_shape[offset:] matches dims_to_broadcast
- offset = abs(offset) # for translation
- for j in range(offset, len(iter_shape)):
- x = iter_shape[j + offset]
- y = dims_to_broadcast[j]
- if (x > y and x % y) or y %x:
- raise oefmt(space.w_ValueError, "%s: Operand %d has a "
- "mismatch in its broadcast dimension %d "
- "(size %d is different from %d)",
- self.name, i, j, x, y)
- iter_shape[offset + j] = max(x, y)
- # Find or verify signature ixs
- for j in range(num_dims):
- core_dim_index = self.core_dim_ixs[dim_offset + j]
- if core_dim_index > len(dims_to_match):
- raise oefmt(space.w_ValueError, "%s: Operand %d has a "
- "mismatch in its core dimension %d, with gufunc "
- "signature %s (index is larger than input shape)",
- self.name, i, j, self.signature, core_dim_index)
- if matched_dims[core_dim_index] < 0:
- matched_dims[core_dim_index] = dims_to_match[j]
- elif matched_dims[core_dim_index] != dims_to_match[j]:
- raise oefmt(space.w_ValueError, "%s: Operand %d has a "
- "mismatch in its core dimension %d, with gufunc "
- "signature %s (expected %d, got %d)",
- self.name, i, j,
- self.signature, matched_dims[core_dim_index],
- dims_to_match[j])
- outarg_shapes = []
- for i in range(len(outargs)):
- curarg = outargs[i]
+ for i in range(len(inargs) + len(outargs)):
+ if i < len(inargs):
+ _i = i
+ name = 'Input'
+ curarg = inargs[i]
+ else:
+ _i = i + self.nin
+ name = 'Output'
+ curarg = outargs[_i]
dim_offset = self.core_offsets[i]
num_dims = self.core_num_dims[i]
if not isinstance(curarg, W_NDimArray):
@@ -849,18 +823,18 @@
for j in range(num_dims):
core_dim_index = self.core_dim_ixs[dim_offset + j]
if matched_dims[core_dim_index] < 0:
- raise oefmt(space.w_ValueError, "%s: Output operand %d
"
+ raise oefmt(space.w_ValueError, "%s: %s operand %d "
"is empty but unique core dimension %d in
signature "
"%s of gufunc was not specified",
- self.name, i, core_dim_index, self.signature)
+ self.name, name, _i, core_dim_index,
self.signature)
arg_shape.append(matched_dims[core_dim_index])
- outarg_shapes.append(arg_shape)
+ arg_shapes.append(arg_shape)
continue
n = len(curarg.get_shape()) - num_dims
if n < 0:
- raise oefmt(space.w_ValueError, "%s: Output operand %d does "
+ raise oefmt(space.w_ValueError, "%s: %s operand %d does "
"not have enough dimensions (has %d, gufunc with "
- "signature %s requires %d)", self.name, i,
+ "signature %s requires %d)", self.name, name, _i,
num_dims+n, self.signature, num_dims)
dims_to_match = curarg.get_shape()[n:]
dims_to_broadcast = curarg.get_shape()[:n]
@@ -876,37 +850,37 @@
x = iter_shape[j + offset]
y = dims_to_broadcast[j]
if (x > y and x % y) or y %x:
- raise oefmt(space.w_ValueError, "%s: Operand %d has a "
+ raise oefmt(space.w_ValueError, "%s: %s operand %d has a "
"mismatch in its broadcast dimension %d "
"(size %d is different from %d)",
- self.name, i, j, x, y)
+ self.name, name, _i, j, x, y)
iter_shape[offset + j] = max(x, y)
# Find or verify signature ixs
for j in range(num_dims):
core_dim_index = self.core_dim_ixs[dim_offset + j]
if core_dim_index > len(dims_to_match):
- raise oefmt(space.w_ValueError, "%s: Operand %d has a "
+ raise oefmt(space.w_ValueError, "%s: %s operand %d has a "
"mismatch in its core dimension %d, with gufunc "
"signature %s (index is larger than input shape)",
- self.name, i, j, self.signature, core_dim_index)
+ self.name, name, _i, j, self.signature,
core_dim_index)
if matched_dims[core_dim_index] < 0:
matched_dims[core_dim_index] =
dims_to_match[core_dim_index]
elif matched_dims[core_dim_index] !=
dims_to_match[core_dim_index]:
- raise oefmt(space.w_ValueError, "%s: Operand %d has a "
+ raise oefmt(space.w_ValueError, "%s: %s operand %d has a "
"mismatch in its core dimension %d, with gufunc "
"signature %s (expected %d, got %d)",
- self.name, i, core_dim_index + num_dims,
+ self.name, name, _i, core_dim_index + num_dims,
self.signature, matched_dims[core_dim_index],
dims_to_match[core_dim_index])
- outarg_shapes.append(iter_shape + dims_to_match)
+ arg_shapes.append(iter_shape + dims_to_match)
# TODO once we support obejct dtypes,
- # FAIL with NotImplemented if the other object has
+ # FAIL with NotImplementedError if the other object has
# the __r<op>__ method and has a higher priority than
# the current op (signalling it can handle ndarray's).
# TODO parse and handle subok
# TODO handle flags, op_flags
- return iter_shape, outarg_shapes, matched_dims
+ return iter_shape, arg_shapes, matched_dims
W_Ufunc.typedef = TypeDef("numpy.ufunc",
__call__ = interp2app(W_Ufunc.descr_call),
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