def _generate_snippet_template(**kwargs):
"""Generate snippet template."""
template, exchange_msg, outputs_list, outputs_mapping = ONNXToMindSporeMapper._generate_snippet_template(
**kwargs)
op = kwargs.get("operation")
args = kwargs.get("converted_params", dict())
weights = kwargs.get("weights")
params = kwargs["raw_params"]
ipt_shape = params["input_shape"]
starts = SliceMapper._find_val_by_index(0, weights)
ends = SliceMapper._find_val_by_index(1, weights)
axes = SliceMapper._find_val_by_index(2, weights, np.array(list(range(len(ipt_shape)))))
steps = SliceMapper._find_val_by_index(3, weights, np.array([1 for _ in range(len(ipt_shape))]))
if not op:
raise ValueError("Can not get MindSpore operation name.")
if not weights:
raise ValueError("Can not get required params from slice.")
if axes.shape != (1,):
ordered_begin = sorted(zip(starts.tolist(), axes.tolist()), key=lambda x: x[1], reverse=False)
ordered_end = sorted(zip(ends.tolist(), axes.tolist()), key=lambda x: x[1], reverse=False)
ordered_strides = sorted(zip(steps.tolist(), axes.tolist()), key=lambda x: x[1], reverse=False)
begin = [i[0] for i in ordered_begin]
end = [min(i[0], ipt_shape[i[1]]) for i in ordered_end]
strides = [i[0] for i in ordered_strides]
else:
axis = axes.tolist()[0]
begin = [0 for _ in range(len(ipt_shape))]
end = list(ipt_shape)
strides = [1 for _ in range(len(ipt_shape))]
begin[axis] = starts.tolist()[0]
end[axis] = min(ends.tolist()[0], end[axis])
strides[axis] = steps.tolist()[0]
args['begin'] = tuple(begin)
args['end'] = tuple(end)
args['strides'] = tuple(strides)
variable_slot = "var_0"
init_template = f"self.{{{variable_slot}}} = {op}()"
init_begin = f"self.{{{variable_slot}}}_begin = {{begin}}"
init_end = f"self.{{{variable_slot}}}_end = {{end}}"
init_strides = f"self.{{{variable_slot}}}_strides = {{strides}}"
construct_template = f"opt_{{{variable_slot}}} = self.{{{variable_slot}}}" \
f"({{{ExchangeMessageKeywords.VariableScope.value.INPUTS.value}}}, " \
f"self.{{{variable_slot}}}_begin, self.{{{variable_slot}}}_end, " \
f"self.{{{variable_slot}}}_strides)"
template = reset_init_or_construct(template, variable_slot,
[init_template, init_begin, init_end, init_strides],
TemplateKeywords.INIT.value)
template = reset_init_or_construct(template, variable_slot, [construct_template],
TemplateKeywords.CONSTRUCT.value)
return template, exchange_msg, outputs_list, outputs_mapping
def _generate_snippet_template_with_weights(weights, args, template, op,
trainable_params):
"""Generate template when weights exist."""
tensor = MulMapper._find_val_by_index(0, weights)
w_shape = tensor.shape
w_location = MulMapper._find_location_by_index(0, weights)
variable_slot = "var_0"
inputs_in_construct = [
f"{{{ExchangeMessageKeywords.VariableScope.value.INPUTS.value}}}"
]
if w_location != -1:
inputs_in_construct.insert(w_location,
f"self.{{{variable_slot}}}_w")
if w_shape:
# Note: adding weight shape to args is now deprecated due to conflict of partial weights share processing.
variable_slot_param_name = f"{variable_slot}/w"
init_tensor = f"self.{{{variable_slot}}}_w = {{{variable_slot_param_name}}}"
else:
args["w_value"] = tensor.tolist()
init_tensor = f"self.{{{variable_slot}}}_w = {{w_value}}"
construct_template = f"opt_{{{variable_slot}}} = {' * '.join(inputs_in_construct)}"
template = reset_init_or_construct(template, variable_slot,
[init_tensor],
TemplateKeywords.INIT.value)
template = reset_init_or_construct(template, variable_slot,
[construct_template],
TemplateKeywords.CONSTRUCT.value)
exchange_msg = {
variable_slot: {
ExchangeMessageKeywords.VariableScope.value.OPERATION.value:
op,
ExchangeMessageKeywords.VariableScope.value.VARIABLE_NAME.value:
None,
ExchangeMessageKeywords.VariableScope.value.OUTPUT_TYPE.value:
ExchangeMessageKeywords.VariableScope.value.TSR_TYPE.value,
ExchangeMessageKeywords.VariableScope.value.INPUTS.value: [],
ExchangeMessageKeywords.VariableScope.value.ARGS.value:
args,
ExchangeMessageKeywords.VariableScope.value.WEIGHTS.value:
weights,
ExchangeMessageKeywords.VariableScope.value.TRAINABLE_PARAMS.value:
trainable_params
}
}
if w_shape:
exchange_msg[variable_slot][ExchangeMessageKeywords.VariableScope.
value.PARAMETERS_DECLARED.value] = {
"w": ""
}
outputs_list = [f"opt_{{{variable_slot}}}"]
outputs_mapping = ((0, 0), )
return template, exchange_msg, outputs_list, outputs_mapping
def _generate_snippet_template_with_weights(weights, args, template, op,
trainable_params):
"""Generate template when weights exist."""
tensor = DivMapper._find_val_by_index(0, weights)
w_shape = tensor.shape
w_dtype = tensor.dtype
w_location = DivMapper._find_location_by_index(0, weights)
variable_slot = "var_0"
inputs_in_construct = [
f"{{{ExchangeMessageKeywords.VariableScope.value.INPUTS.value}}}"
]
if w_location != -1:
inputs_in_construct.insert(w_location,
f"self.{{{variable_slot}}}_w")
if w_shape:
args["w_shape"] = w_shape
args["w_dtype"] = w_dtype
init_tensor = f"self.{{{variable_slot}}}_w = " \
f"Parameter(Tensor(np.random.uniform(0, 1, {{w_shape}}).astype(np.{{w_dtype}})), " \
f"name=None)"
else:
args["w_value"] = tensor.tolist()
init_tensor = f"self.{{{variable_slot}}}_w = {{w_value}}"
construct_template = f"opt_{{{variable_slot}}} = {' / '.join(inputs_in_construct)}"
template = reset_init_or_construct(template, variable_slot,
[init_tensor],
TemplateKeywords.INIT.value)
template = reset_init_or_construct(template, variable_slot,
[construct_template],
TemplateKeywords.CONSTRUCT.value)
exchange_msg = {
variable_slot: {
ExchangeMessageKeywords.VariableScope.value.OPERATION.value:
op,
ExchangeMessageKeywords.VariableScope.value.VARIABLE_NAME.value:
None,
ExchangeMessageKeywords.VariableScope.value.OUTPUT_TYPE.value:
ExchangeMessageKeywords.VariableScope.value.TSR_TYPE.value,
ExchangeMessageKeywords.VariableScope.value.INPUTS.value: [],
ExchangeMessageKeywords.VariableScope.value.ARGS.value:
args,
ExchangeMessageKeywords.VariableScope.value.WEIGHTS.value:
weights,
ExchangeMessageKeywords.VariableScope.value.TRAINABLE_PARAMS.value:
trainable_params
}
}
outputs_list = [f"opt_{{{variable_slot}}}"]
outputs_mapping = ((0, 0), )
return template, exchange_msg, outputs_list, outputs_mapping
def _generate_snippet_template(**kwargs):
template, exchange_msg, outputs_list, outputs_mapping = ONNXToMindSporeMapper._generate_snippet_template(
**kwargs)
op = kwargs.get("operation")
args = kwargs.get("converted_params")
weights = kwargs.get("weights")
trainable_params = kwargs.get("trainable_params", dict())
if not weights:
return template, exchange_msg, outputs_list, outputs_mapping
tensor = PowMapper._find_val_by_index(0, weights)
input_shape = tensor.shape
input_dtype = tensor.dtype
input_location = PowMapper._find_location_by_index(0, weights)
variable_slot = "var_0"
init_template = f"self.{{{variable_slot}}} = {op}()"
inputs_in_construct = [f"{{{ExchangeMessageKeywords.VariableScope.value.INPUTS.value}}}"]
if input_location != -1:
inputs_in_construct.insert(input_location, f"self.{{{variable_slot}}}_input_weight")
if input_shape:
args["input_shape"] = input_shape
args["input_dtype"] = input_dtype
init_tensor = f"self.{{{variable_slot}}}_input_weight = " \
f"Parameter(Tensor(np.random.uniform(0, 1, {{input_shape}}).astype(np.{{input_dtype}})), " \
f"name=None)"
else:
args["input_value"] = tensor.tolist()
init_tensor = f"self.{{{variable_slot}}}_input_weight = {{input_value}}"
construct_template = f"opt_{{{variable_slot}}} = self.{{{variable_slot}}}" \
f"({', '.join(inputs_in_construct)})"
template = reset_init_or_construct(template, variable_slot, [init_template, init_tensor],
TemplateKeywords.INIT.value)
template = reset_init_or_construct(template, variable_slot, [construct_template],
TemplateKeywords.CONSTRUCT.value)
exchange_msg = {
variable_slot: {
ExchangeMessageKeywords.VariableScope.value.OPERATION.value: op,
ExchangeMessageKeywords.VariableScope.value.VARIABLE_NAME.value: None,
ExchangeMessageKeywords.VariableScope.value.OUTPUT_TYPE.value:
ExchangeMessageKeywords.VariableScope.value.TSR_TYPE.value,
ExchangeMessageKeywords.VariableScope.value.INPUTS.value: [],
ExchangeMessageKeywords.VariableScope.value.ARGS.value: args,
ExchangeMessageKeywords.VariableScope.value.WEIGHTS.value: weights,
ExchangeMessageKeywords.VariableScope.value.TRAINABLE_PARAMS.value: trainable_params
}
}
outputs_list = [f"opt_{{{variable_slot}}}"]
outputs_mapping = ((0, 0),)
return template, exchange_msg, outputs_list, outputs_mapping
def _generate_snippet_template(**kwargs):
template, exchange_msg, outputs_list, outputs_mapping = ONNXToMindSporeMapper._generate_snippet_template(
**kwargs)
raw_params = kwargs.get('raw_params', dict())
trans_a = raw_params.get('transA', 0)
if not trans_a:
return template, exchange_msg, outputs_list, outputs_mapping
op = kwargs.get("operation")
args = kwargs.get("converted_params")
weights = kwargs.get("weights")
trainable_params = kwargs.get('trainable_params', dict())
if not op:
raise ValueError("Can not get MindSpore operation name.")
input_rank = len(raw_params['input_shape'])
input_perm = [i for i in range(input_rank)]
input_perm[-2], input_perm[-1] = input_perm[-1], input_perm[-2]
variable_slot = "var_0"
init_input_perm = f"self.{{{variable_slot}}}_input_perm = {{input_perm}}"
init_template = f"self.{{{variable_slot}}} = {op}({', '.join(['%s={%s}' % (p, p) for p in args])})"
inputs_in_construct = [f"{{{ExchangeMessageKeywords.VariableScope.value.INPUTS.value}}}",
f"self.{{{variable_slot}}}_input_perm"]
args['input_perm'] = tuple(input_perm)
construct_transpose = f"opt_{{{variable_slot}}}_transpose = P.Transpose()({', '.join(inputs_in_construct)})"
construct_template = f"opt_{{{variable_slot}}} = self.{{{variable_slot}}}(opt_{{{variable_slot}}}_transpose)"
template = reset_init_or_construct(template, variable_slot, [init_input_perm, init_template],
TemplateKeywords.INIT.value)
template = reset_init_or_construct(template, variable_slot, [construct_transpose, construct_template],
TemplateKeywords.CONSTRUCT.value)
exchange_msg = {
variable_slot: {
ExchangeMessageKeywords.VariableScope.value.OPERATION.value: op,
ExchangeMessageKeywords.VariableScope.value.VARIABLE_NAME.value: None,
ExchangeMessageKeywords.VariableScope.value.OUTPUT_TYPE.value:
ExchangeMessageKeywords.VariableScope.value.TSR_TYPE.value,
ExchangeMessageKeywords.VariableScope.value.INPUTS.value: [],
ExchangeMessageKeywords.VariableScope.value.ARGS.value: args,
ExchangeMessageKeywords.VariableScope.value.WEIGHTS.value: weights,
ExchangeMessageKeywords.VariableScope.value.TRAINABLE_PARAMS.value: trainable_params
}
}
outputs_list = [f"opt_{{{variable_slot}}}"]
outputs_mapping = ((0, 0),)
return template, exchange_msg, outputs_list, outputs_mapping
def _generate_snippet_template(**kwargs):
template, exchange_msg, outputs_list, outputs_mapping = ONNXToMindSporeMapper._generate_snippet_template(
**kwargs)
raw_params = kwargs.get("raw_params")
perm = raw_params["perm"]
variable_slot = "var_0"
construct_template = f"opt_{{{variable_slot}}} = self.{{{variable_slot}}}" \
f"({{{ExchangeMessageKeywords.VariableScope.value.INPUTS.value}}}, {tuple(perm)})"
template = reset_init_or_construct(template, variable_slot,
[construct_template],
TemplateKeywords.CONSTRUCT.value)
return template, exchange_msg, outputs_list, outputs_mapping