[GitHub] [incubator-tvm] were closed pull request #4829: [Pact 0] Can you take a look at my glue?
were closed pull request #4829: [Pact 0] Can you take a look at my glue? URL: https://github.com/apache/incubator-tvm/pull/4829 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] were opened a new pull request #4829: [Pact 0] Can you take a look at my glue?
were opened a new pull request #4829: [Pact 0] Can you take a look at my glue? URL: https://github.com/apache/incubator-tvm/pull/4829 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] LiangHao151941 commented on issue #4828: [QNN][TFLite] TFLite rounding mode support
LiangHao151941 commented on issue #4828: [QNN][TFLite] TFLite rounding mode support URL: https://github.com/apache/incubator-tvm/pull/4828#issuecomment-582771717 Makes sense! I'll follow up on those. @FrozenGene This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] soiferj commented on a change in pull request #4825: [Frontend][ONNX] LSTM Support
soiferj commented on a change in pull request #4825: [Frontend][ONNX] LSTM Support URL: https://github.com/apache/incubator-tvm/pull/4825#discussion_r375662346 ## File path: python/tvm/relay/frontend/onnx.py ## @@ -32,6 +32,55 @@ __all__ = ['from_onnx'] +class onnx_input(): Review comment: I really like this design - very sleek. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] soiferj commented on a change in pull request #4825: [Frontend][ONNX] LSTM Support
soiferj commented on a change in pull request #4825: [Frontend][ONNX] LSTM
Support
URL: https://github.com/apache/incubator-tvm/pull/4825#discussion_r375661042
##
File path: python/tvm/relay/frontend/onnx.py
##
@@ -1190,6 +1250,145 @@ def expand_shape(in_shape, shape):
return _op.broadcast_to(inputs[0], shape=tuple(shape))
+class LSTM(OnnxOpConverter):
+""" Operator converter for LSTM.
+"""
+
+@classmethod
+def _activation_helper(cls, activation, alpha, beta):
+convert_map = _get_convert_map(1)
+attrs = {}
+if alpha is not None:
+attrs['alpha'] = alpha
+if beta is not None:
+attrs['beta'] = beta
+return lambda x: convert_map[activation.decode("utf-8")]([x], attrs,
{})
+
+@classmethod
+def _activation_needs_alpha(cls, activation):
+needs_alpha = [
+"Affine",
+"LeakyRelu",
+"ThresholdedRelu",
+"ScaledTanh",
+"HardSigmoid",
+"Elu",
+]
+return activation.decode("utf-8") in needs_alpha
+
+@classmethod
+def _activation_needs_beta(cls, activation):
+needs_beta = [
+"Affine",
+"ScaledTanh",
+"HardSigmoid",
+]
+return activation.decode("utf-8") in needs_beta
+
+@classmethod
+def _impl_v7(cls, inputs, attr, params):
+# Unpack inputs, note that if optional and not provided then value
will be None.
+X = inputs[0]
+W = inputs[1]
Review comment:
Is there any case when the weights won’t be constant? If they’re constant,
we can remove some operations from the graph and compute them here (like
squeeze).
By constant, I mean we can call `infer_value` on it.
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[GitHub] [incubator-tvm] soiferj commented on a change in pull request #4825: [Frontend][ONNX] LSTM Support
soiferj commented on a change in pull request #4825: [Frontend][ONNX] LSTM
Support
URL: https://github.com/apache/incubator-tvm/pull/4825#discussion_r375661224
##
File path: tests/python/frontend/onnx/test_forward.py
##
@@ -1962,6 +1962,126 @@ def test_pooling():
auto_pad='SAME_UPPER')
+def verify_lstm(seq_length,
+batch_size,
+input_size,
+hidden_size,
+use_bias=False,
+activations=None,
+alphas=None,
+betas=None):
+x_np = np.random.uniform(size=(seq_length, batch_size,
input_size)).astype('float32')
+w_np = np.random.uniform(size=(1, 4 * hidden_size,
input_size)).astype('float32')
+r_np = np.random.uniform(size=(1, 4 * hidden_size,
hidden_size)).astype('float32')
+input_names = ["X", "W", "R"]
+input_tensors = [
+helper.make_tensor_value_info("X", TensorProto.FLOAT,
list(x_np.shape)),
+helper.make_tensor_value_info("W", TensorProto.FLOAT,
list(w_np.shape)),
+helper.make_tensor_value_info("R", TensorProto.FLOAT, list(r_np.shape))
+]
+input_values = [x_np, w_np, r_np]
+if use_bias:
+b_np = np.random.uniform(size=(1, 8 * hidden_size)).astype('float32')
+input_names.append("B")
+input_tensors.append(
+helper.make_tensor_value_info("B", TensorProto.FLOAT, [1, 8 *
hidden_size]))
+input_values.append(b_np)
+
+Y_shape = [seq_length, 1, batch_size, hidden_size]
+Y_h_shape = [1, batch_size, hidden_size]
+Y_c_shape = [1, batch_size, hidden_size]
+
+if activations is None:
+lstm_node = helper.make_node(
+'LSTM', inputs=input_names, outputs=["Y", "Y_h", "Y_c"],
hidden_size=hidden_size)
+elif alphas is None:
+lstm_node = helper.make_node(
+'LSTM',
+inputs=input_names,
+outputs=["Y", "Y_h", "Y_c"],
+hidden_size=hidden_size,
+activations=activations)
+else:
+lstm_node = helper.make_node(
+'LSTM',
+inputs=input_names,
+outputs=["Y", "Y_h", "Y_c"],
+hidden_size=hidden_size,
+activations=activations,
+activation_alpha=alphas,
+activation_beta=betas)
+
+graph = helper.make_graph([lstm_node],
+ "lstm_test",
+ inputs=input_tensors,
+ outputs=[
+ helper.make_tensor_value_info("Y",
TensorProto.FLOAT,
+list(Y_shape)),
+ helper.make_tensor_value_info("Y_h",
TensorProto.FLOAT,
+
list(Y_h_shape)),
+ helper.make_tensor_value_info("Y_c",
TensorProto.FLOAT,
+
list(Y_c_shape))
+ ])
+
+model = helper.make_model(graph, producer_name='lstm_test')
+
+for target, ctx in ctx_list():
+onnx_out = get_onnxruntime_output(model, input_values, 'float32')
+tvm_out = get_tvm_output(
+model,
+input_values,
+target,
+ctx, [Y_shape, Y_h_shape, Y_c_shape],
+output_dtype=['float32', 'float32', 'float32'])
+for o_out, t_out in zip(onnx_out, tvm_out):
+tvm.testing.assert_allclose(o_out, t_out, rtol=5e-3, atol=5e-3)
+
+
+def test_lstm():
Review comment:
Can you also add a test where initial c and h states are set to something
other than 0?
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[GitHub] [incubator-tvm] soiferj commented on a change in pull request #4825: [Frontend][ONNX] LSTM Support
soiferj commented on a change in pull request #4825: [Frontend][ONNX] LSTM
Support
URL: https://github.com/apache/incubator-tvm/pull/4825#discussion_r375661042
##
File path: python/tvm/relay/frontend/onnx.py
##
@@ -1190,6 +1250,145 @@ def expand_shape(in_shape, shape):
return _op.broadcast_to(inputs[0], shape=tuple(shape))
+class LSTM(OnnxOpConverter):
+""" Operator converter for LSTM.
+"""
+
+@classmethod
+def _activation_helper(cls, activation, alpha, beta):
+convert_map = _get_convert_map(1)
+attrs = {}
+if alpha is not None:
+attrs['alpha'] = alpha
+if beta is not None:
+attrs['beta'] = beta
+return lambda x: convert_map[activation.decode("utf-8")]([x], attrs,
{})
+
+@classmethod
+def _activation_needs_alpha(cls, activation):
+needs_alpha = [
+"Affine",
+"LeakyRelu",
+"ThresholdedRelu",
+"ScaledTanh",
+"HardSigmoid",
+"Elu",
+]
+return activation.decode("utf-8") in needs_alpha
+
+@classmethod
+def _activation_needs_beta(cls, activation):
+needs_beta = [
+"Affine",
+"ScaledTanh",
+"HardSigmoid",
+]
+return activation.decode("utf-8") in needs_beta
+
+@classmethod
+def _impl_v7(cls, inputs, attr, params):
+# Unpack inputs, note that if optional and not provided then value
will be None.
+X = inputs[0]
+W = inputs[1]
Review comment:
Is there any case when the weights won’t be constant? If they’re constant,
we can remove some operations from the graph and compute them here (like
squeeze).
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[GitHub] [incubator-tvm] soiferj commented on a change in pull request #4825: [Frontend][ONNX] LSTM Support
soiferj commented on a change in pull request #4825: [Frontend][ONNX] LSTM
Support
URL: https://github.com/apache/incubator-tvm/pull/4825#discussion_r375661224
##
File path: tests/python/frontend/onnx/test_forward.py
##
@@ -1962,6 +1962,126 @@ def test_pooling():
auto_pad='SAME_UPPER')
+def verify_lstm(seq_length,
+batch_size,
+input_size,
+hidden_size,
+use_bias=False,
+activations=None,
+alphas=None,
+betas=None):
+x_np = np.random.uniform(size=(seq_length, batch_size,
input_size)).astype('float32')
+w_np = np.random.uniform(size=(1, 4 * hidden_size,
input_size)).astype('float32')
+r_np = np.random.uniform(size=(1, 4 * hidden_size,
hidden_size)).astype('float32')
+input_names = ["X", "W", "R"]
+input_tensors = [
+helper.make_tensor_value_info("X", TensorProto.FLOAT,
list(x_np.shape)),
+helper.make_tensor_value_info("W", TensorProto.FLOAT,
list(w_np.shape)),
+helper.make_tensor_value_info("R", TensorProto.FLOAT, list(r_np.shape))
+]
+input_values = [x_np, w_np, r_np]
+if use_bias:
+b_np = np.random.uniform(size=(1, 8 * hidden_size)).astype('float32')
+input_names.append("B")
+input_tensors.append(
+helper.make_tensor_value_info("B", TensorProto.FLOAT, [1, 8 *
hidden_size]))
+input_values.append(b_np)
+
+Y_shape = [seq_length, 1, batch_size, hidden_size]
+Y_h_shape = [1, batch_size, hidden_size]
+Y_c_shape = [1, batch_size, hidden_size]
+
+if activations is None:
+lstm_node = helper.make_node(
+'LSTM', inputs=input_names, outputs=["Y", "Y_h", "Y_c"],
hidden_size=hidden_size)
+elif alphas is None:
+lstm_node = helper.make_node(
+'LSTM',
+inputs=input_names,
+outputs=["Y", "Y_h", "Y_c"],
+hidden_size=hidden_size,
+activations=activations)
+else:
+lstm_node = helper.make_node(
+'LSTM',
+inputs=input_names,
+outputs=["Y", "Y_h", "Y_c"],
+hidden_size=hidden_size,
+activations=activations,
+activation_alpha=alphas,
+activation_beta=betas)
+
+graph = helper.make_graph([lstm_node],
+ "lstm_test",
+ inputs=input_tensors,
+ outputs=[
+ helper.make_tensor_value_info("Y",
TensorProto.FLOAT,
+list(Y_shape)),
+ helper.make_tensor_value_info("Y_h",
TensorProto.FLOAT,
+
list(Y_h_shape)),
+ helper.make_tensor_value_info("Y_c",
TensorProto.FLOAT,
+
list(Y_c_shape))
+ ])
+
+model = helper.make_model(graph, producer_name='lstm_test')
+
+for target, ctx in ctx_list():
+onnx_out = get_onnxruntime_output(model, input_values, 'float32')
+tvm_out = get_tvm_output(
+model,
+input_values,
+target,
+ctx, [Y_shape, Y_h_shape, Y_c_shape],
+output_dtype=['float32', 'float32', 'float32'])
+for o_out, t_out in zip(onnx_out, tvm_out):
+tvm.testing.assert_allclose(o_out, t_out, rtol=5e-3, atol=5e-3)
+
+
+def test_lstm():
Review comment:
Can you also add a test where initial c and h states are set?
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[GitHub] [incubator-tvm] FrozenGene commented on issue #4828: tflite rounding mode support
FrozenGene commented on issue #4828: tflite rounding mode support URL: https://github.com/apache/incubator-tvm/pull/4828#issuecomment-582757548 Thanks @LiangHao151941 ! I haven't reviewed the code, however, I have some high level comments: 1. if we have TFLITE rounding support, we should make TFLite frontend using TFLITE rounding and we should get the bit exact result as TFLite. 2. you should also modify the `test_forward.py` for tflite, like test_qnn* related test cases, we shouldn't need `atol=1` any more ideally. 3. you could add `q_conv2d` unit testing case, we could get the same result compared with TFLite. we lack of this unit testing. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] FrozenGene commented on issue #4824: Tflite frontend needs to use zero point of input tensor while lowering qnn.conv2d for padding
FrozenGene commented on issue #4824: Tflite frontend needs to use zero point of input tensor while lowering qnn.conv2d for padding URL: https://github.com/apache/incubator-tvm/issues/4824#issuecomment-58275 Nice suggestion! We won't do similar things before. For some critical patches, we should port it back to our previous releases (like 0.6 mentioned here). I come up with two suggestions: 1. make one new release named as `0.6 SP1 / SP2 / SP3 /...`, SP means service pack, the inspiration comes from Microsoft's release concept. Like Visual Studio SP1 / SP2 / SP3. 2. make one new release named as `0.6.1 / 0.6.2 / 0.6.3 / ...`. .1 / . 2 / .3 is the patch version. This inspiration comes from LLVM's release concept. Like LLVM 3.7.0 / 3.7.1 / 3.7.2 /. cc @tqchen @zhiics @yzhliu @icemelon9 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] LiangHao151941 opened a new pull request #4828: tflite rounding mode support
LiangHao151941 opened a new pull request #4828: tflite rounding mode support URL: https://github.com/apache/incubator-tvm/pull/4828 Thanks for contributing to TVM! Please refer to guideline https://docs.tvm.ai/contribute/ for useful information and tips. After the pull request is submitted, please request code reviews from [Reviewers](https://github.com/apache/incubator-tvm/blob/master/CONTRIBUTORS.md#reviewers) by @ them in the pull request thread. Add tflite rounding support with corresponding test cases. The tflite rounding mode golden results are generated with a testbench using MultiplyByQuantizedMultiplier function here: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/kernels/internal/common.h#L148 @FrozenGene @anijain2305 Might help fix the problem described here: https://discuss.tvm.ai/t/supporting-bit-exact-tflite-qnn-inference/5528 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] masahi commented on issue #4825: [Frontend][ONNX] LSTM Support
masahi commented on issue #4825: [Frontend][ONNX] LSTM Support URL: https://github.com/apache/incubator-tvm/pull/4825#issuecomment-582737938 looks great! Will wait for @soiferj's review. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] jwfromm commented on a change in pull request #4644: [WIP] Relay op strategy
jwfromm commented on a change in pull request #4644: [WIP] Relay op strategy
URL: https://github.com/apache/incubator-tvm/pull/4644#discussion_r374966045
##
File path: python/tvm/relay/op/strategy/x86.py
##
@@ -0,0 +1,277 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Definition of x86 operator strategy."""
+# pylint:
disable=invalid-name,unused-argument,wildcard-import,unused-wildcard-import
+from __future__ import absolute_import
+
+import logging
+
+import topi
+from .generic import *
+from .. import op as _op
+from schedule import SpecializedCondition
+
+logger = logging.getLogger('strategy')
+
+@schedule_injective.register("cpu")
+def schedule_injective_cpu(attrs, outs, target):
+"""schedule injective ops for x86"""
+with target:
+return topi.x86.schedule_injective(outs)
+
+@schedule_reduce.register("cpu")
+def schedule_reduce_cpu(attrs, outs, target):
+"""schedule reduction ops for x86"""
+with target:
+return topi.x86.schedule_reduce(outs)
+
+@schedule_concatenate.register("cpu")
+def schedule_concatenate_cpu(attrs, outs, target):
+"""schedule concatenate op for x86"""
+with target:
+return topi.x86.schedule_concatenate(outs)
+
+@schedule_pool.register("cpu")
+def schedule_pool_cpu(attrs, outs, target):
+"""schedule pooling ops for x86"""
+with target:
+return topi.x86.schedule_pool(outs, attrs.layout)
+
+@schedule_adaptive_pool.register("cpu")
+def schedule_adaptive_pool_cpu(attrs, outs, target):
+"""schedule adaptive pooling ops for x86"""
+with target:
+return topi.x86.schedule_adaptive_pool(outs)
+
+@schedule_softmax.register("cpu")
+def schedule_softmax_cpu(attrs, outs, target):
+"""schedule softmax for x86"""
+with target:
+return topi.x86.schedule_softmax(outs)
+
+@conv2d_strategy.register("cpu")
+def conv2d_strategy_cpu(attrs, inputs, out_type, target):
+"""conv2d x86 strategy"""
+strategy = _op.OpStrategy()
+data, kernel = inputs
+dilation_h, dilation_w = get_const_tuple(attrs.dilation)
+groups = attrs.groups
+layout = attrs.data_layout
+kernel_layout = attrs.kernel_layout
+if dilation_h < 1 or dilation_w < 1:
+raise ValueError("dilation should be positive value")
+
+if groups == 1:
+if layout == "NCHW":
+assert kernel_layout == "OIHW"
+if topi.x86.is_int8_hw_support(data.dtype, kernel.dtype):
+strategy.add_implement(
+wrap_compute_conv2d(topi.x86.conv2d_nchw_int8),
+wrap_topi_schedule(topi.x86.schedule_conv2d_nchw_int8))
+else:
+strategy.add_implement(
+wrap_compute_conv2d(topi.x86.conv2d_nchw),
+wrap_topi_schedule(topi.x86.schedule_conv2d_nchw))
+elif layout == "NHWC":
+assert kernel_layout == "HWIO"
+logger.warning("For x86 target, NCHW layout is recommended for
conv2d.")
+strategy.add_implement(
+wrap_compute_conv2d(topi.nn.conv2d_nhwc),
+wrap_topi_schedule(topi.x86.schedule_conv2d_nhwc))
+elif layout == "HWCN":
+assert kernel_layout == "HWIO"
+logger.warning("For x86 target, NCHW layout is recommended for
conv2d.")
+strategy.add_implement(
+wrap_compute_conv2d(topi.nn.conv2d_hwcn),
+wrap_topi_schedule(topi.generic.schedule_conv2d_hwcn))
+else:
+raise RuntimeError("Unsupported conv2d layout {} for
cpu".format(layout))
+elif is_depthwise_conv2d(data.shape, layout, kernel.shape, kernel_layout,
groups):
+if layout == "NCHW":
+assert kernel_layout == "OIHW"
+channel_multiplier = get_const_tuple(inputs[1].shape)[1]
+if channel_multiplier == 1:
+strategy.add_implement(
+wrap_compute_conv2d(topi.x86.depthwise_conv2d_nchw),
+
wrap_topi_schedule(topi.x86.schedule_depthwise_conv2d_nchw))
+else:
+logger.warning("For x86 target, depthwise_conv2d with channel "
+ "multiplier greater than 1 is not optim
[GitHub] [incubator-tvm] jwfromm commented on a change in pull request #4644: [WIP] Relay op strategy
jwfromm commented on a change in pull request #4644: [WIP] Relay op strategy
URL: https://github.com/apache/incubator-tvm/pull/4644#discussion_r374870865
##
File path: python/tvm/relay/op/strategy/hls.py
##
@@ -0,0 +1,151 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Definition of HLS operator strategy."""
+# pylint:
disable=invalid-name,unused-argument,wildcard-import,unused-wildcard-import
+from __future__ import absolute_import
+
+import topi
+from .generic import *
+from .. import op as _op
+
+@schedule_injective.register("hls")
+def schedule_injective_hls(attrs, outs, target):
+"""schedule injective ops for hls"""
+with target:
+return topi.hls.schedule_injective(outs)
+
+@schedule_reduce.register("hls")
+def schedule_reduce_hls(attrs, outs, target):
+"""schedule reduction ops for hls"""
+with target:
+return topi.hls.schedule_reduce(outs)
+
+@schedule_concatenate.register("hls")
+def schedule_concatenate_hls(attrs, outs, target):
+"""schedule concatenate for hls"""
+with target:
+return topi.hls.schedule_injective(outs)
+
+@schedule_pool.register("hls")
+def schedule_pool_hls(attrs, outs, target):
+"""schedule pooling ops for hls"""
+with target:
+return topi.hls.schedule_pool(outs, attrs.layout)
+
+@schedule_adaptive_pool.register("hls")
+def schedule_adaptive_pool_hls(attrs, outs, target):
+"""schedule adaptive pooling ops for hls"""
+with target:
+return topi.hls.schedule_adaptive_pool(outs)
+
+@schedule_softmax.register("hls")
+def schedule_softmax_hls(attrs, outs, target):
+"""schedule softmax for hls"""
+with target:
+return topi.hls.schedule_softmax(outs)
+
+@override_native_generic_func("conv2d_strategy")
+def conv2d_strategy_hls(attrs, inputs, out_type, target):
+"""conv2d hls strategy"""
+strategy = _op.OpStrategy()
+data, kernel = inputs
+dilation = get_const_tuple(attrs.dilation)
+groups = attrs.groups
+layout = attrs.data_layout
+kernel_layout = attrs.kernel_layout
+(dilation_h, dilation_w) = dilation
+if dilation_h < 1 or dilation_w < 1:
+raise ValueError("dilation should be positive value")
+
+if groups == 1:
+if layout == "NCHW":
+assert kernel_layout == "OIHW"
+strategy.add_implement(
+wrap_compute_conv2d(topi.nn.conv2d_nchw),
+wrap_topi_schedule(topi.hls.schedule_conv2d_nchw))
+elif layout == "NHWC":
+assert kernel_layout == "HWIO"
+strategy.add_implement(
+wrap_compute_conv2d(topi.nn.conv2d_nhwc),
+wrap_topi_schedule(topi.hls.schedule_conv2d_nhwc))
+else:
+raise RuntimeError("Unsupported conv2d layout {}".format(layout))
+elif is_depthwise_conv2d(data.shape, layout, kernel.shape, kernel_layout,
groups):
+if layout == "NCHW":
+assert kernel_layout == "OIHW"
+strategy.add_implement(
+wrap_compute_conv2d(topi.nn.depthwise_conv2d_nchw),
+wrap_topi_schedule(topi.hls.schedule_depthwise_conv2d_nchw))
+elif layout == "NHWC":
+assert kernel_layout == "HWOI"
+strategy.add_implement(
+wrap_compute_conv2d(topi.nn.depthwise_conv2d_nhwc),
+wrap_topi_schedule(topi.hls.schedule_depthwise_conv2d_nhwc))
+else:
+raise RuntimeError("Unsupported depthwise_conv2d layout
{}".format(layout))
+else: # group_conv2d
+raise RuntimeError("group_conv2d is not supported for hls")
+return strategy
+
+@override_native_generic_func("conv2d_NCHWc_strategy")
+def conv2d_NCHWc_strategy_hls(attrs, inputs, out_type, target):
+"""conv2d_NCHWc hls strategy"""
+strategy = _op.OpStrategy()
+strategy.add_implement(
+wrap_compute_conv2d(topi.nn.conv2d_NCHWc, True, True),
+wrap_topi_schedule(topi.hls.schedule_conv2d_NCHWc))
+return strategy
+
+@conv2d_transpose_strategy.register("hls")
+def conv2d_transpose_strategy_hls(attrs, inputs, out_type, target):
+"""conv2d_transpose hls strategy"""
+layout = attrs.data_layout
+dilation = get_const_tuple(attrs.dilatio
[GitHub] [incubator-tvm] zhiics commented on issue #4564: [Doc] Introduction to module serialization
zhiics commented on issue #4564: [Doc] Introduction to module serialization URL: https://github.com/apache/incubator-tvm/pull/4564#issuecomment-582726010 LGTM now. I will wait till tomorrow to see if there are any more comments. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] wyc-ruiker commented on a change in pull request #4822: [Frontend][TFLite] Add MIRROR_PAD operator
wyc-ruiker commented on a change in pull request #4822: [Frontend][TFLite] Add
MIRROR_PAD operator
URL: https://github.com/apache/incubator-tvm/pull/4822#discussion_r375629498
##
File path: python/tvm/relay/frontend/tflite.py
##
@@ -1422,10 +1423,48 @@ def convert_pad(self, op):
# convert list of lists to tuple of tuples
paddings = tuple(tuple(l) for l in pad_list)
-# Use default pad_value 0 because TFLite does not support
constant_values parameter
+# Use default pad_value 0 because TFLite PAD does not support
constant_values parameter
out = _op.nn.pad(in_expr, paddings)
return out
+def convert_mirror_pad(self, op):
+"""Convert TFLite MIRROR_PAD"""
+try:
+from tflite.Operator import Operator
+from tflite.BuiltinOptions import BuiltinOptions
+from tflite.MirrorPadOptions import MirrorPadOptions
+except ImportError:
+raise ImportError("The tflite package must be installed")
+
+# the quantized form MirrorPad is not yet implemented in TFLite.
+if self.is_quantized(op):
+raise tvm.error.OpNotImplemented(
+'TFlite quantized MIRROR_PAD operator is not supported yet.')
Review comment:
I think this exception will not be triggered when quantized MIRROR_PAD is
not implemented by TFLite. If the exception is triggered, it certainly means
the TVM stack doesn't support quantized MIRROR_PAD.
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[GitHub] [incubator-tvm] masahi commented on a change in pull request #4826: [CI][DOCKER] Update ci-gpu torch1.4 and onnx1.6
masahi commented on a change in pull request #4826: [CI][DOCKER] Update ci-gpu torch1.4 and onnx1.6 URL: https://github.com/apache/incubator-tvm/pull/4826#discussion_r375627008 ## File path: docker/install/ubuntu_install_onnx.sh ## @@ -21,7 +21,7 @@ set -u set -o pipefail # fix to certain version for now -pip3 install onnx==1.5.0 +pip3 install onnx==1.6.0 pip3 install onnxruntime==1.0.0 # torch depends on a number of other packages, but unhelpfully, does Review comment: ok This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on issue #4543: [FRONTEND][TFLITE] Add support for TFLite_Detection_PostProcess
tqchen commented on issue #4543: [FRONTEND][TFLITE] Add support for TFLite_Detection_PostProcess URL: https://github.com/apache/incubator-tvm/pull/4543#issuecomment-582721889 @mbarrett97 please rebase, @FrozenGene please followup :) This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on issue #4827: [CI][DOCKER] Update ci-gpu to v0.60
tqchen commented on issue #4827: [CI][DOCKER] Update ci-gpu to v0.60 URL: https://github.com/apache/incubator-tvm/pull/4827#issuecomment-582721675 ``` docker/bash.sh tvmai/ci-gpu:v0.60 >>> import torch >>> import torchvision >>> torch.__version__ '1.4.0' >>> torchvision.__version__ '0.5.0' >>> ``` This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on issue #4826: [CI][DOCKER] Update ci-gpu torch1.4 and onnx1.6
tqchen commented on issue #4826: [CI][DOCKER] Update ci-gpu torch1.4 and onnx1.6 URL: https://github.com/apache/incubator-tvm/pull/4826#issuecomment-582721513 @masahi please take another look This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on a change in pull request #4826: [CI][DOCKER] Update ci-gpu torch1.4 and onnx1.6
tqchen commented on a change in pull request #4826: [CI][DOCKER] Update ci-gpu torch1.4 and onnx1.6 URL: https://github.com/apache/incubator-tvm/pull/4826#discussion_r375626423 ## File path: docker/install/ubuntu_install_onnx.sh ## @@ -21,7 +21,7 @@ set -u set -o pipefail # fix to certain version for now -pip3 install onnx==1.5.0 +pip3 install onnx==1.6.0 pip3 install onnxruntime==1.0.0 # torch depends on a number of other packages, but unhelpfully, does Review comment: nice catch, the binary image is correct, somehow i missed the patch. ``` Type "help", "copyright", "credits" or "license" for more information. >>> import torch i>>> import torchvision >>> torch.__version__ '1.4.0' >>> torchvision.__version__ '0.5.0' >>> ``` This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] masahi commented on a change in pull request #4826: [CI][DOCKER] Update ci-gpu torch1.4 and onnx1.6
masahi commented on a change in pull request #4826: [CI][DOCKER] Update ci-gpu torch1.4 and onnx1.6 URL: https://github.com/apache/incubator-tvm/pull/4826#discussion_r375625372 ## File path: docker/install/ubuntu_install_onnx.sh ## @@ -21,7 +21,7 @@ set -u set -o pipefail # fix to certain version for now -pip3 install onnx==1.5.0 +pip3 install onnx==1.6.0 pip3 install onnxruntime==1.0.0 # torch depends on a number of other packages, but unhelpfully, does Review comment: have you updated torch? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on issue #4696: [Relay][Frontend][TFlite] Add support for quantized LOGISTIC
tqchen commented on issue #4696: [Relay][Frontend][TFlite] Add support for quantized LOGISTIC URL: https://github.com/apache/incubator-tvm/pull/4696#issuecomment-582719957 @inadob please rebase, @FrozenGene please https://docs.tvm.ai/contribute/code_review.html#approve-and-request-changes-explicitly This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on issue #4564: [Doc] Introduction to module serialization
tqchen commented on issue #4564: [Doc] Introduction to module serialization URL: https://github.com/apache/incubator-tvm/pull/4564#issuecomment-582719710 ping @zhiics @yzhliu @FrozenGene @yangjunpro please followup on this thread :) This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen closed pull request #4270: [Codgen] Thread variable use before define
tqchen closed pull request #4270: [Codgen] Thread variable use before define URL: https://github.com/apache/incubator-tvm/pull/4270 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on issue #3943: Qnn quantize with min max using Mxnet flavor to support Mxnet prequantized models.
tqchen commented on issue #3943: Qnn quantize with min max using Mxnet flavor to support Mxnet prequantized models. URL: https://github.com/apache/incubator-tvm/pull/3943#issuecomment-582719478 closue due to inactive status This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen closed pull request #3943: Qnn quantize with min max using Mxnet flavor to support Mxnet prequantized models.
tqchen closed pull request #3943: Qnn quantize with min max using Mxnet flavor to support Mxnet prequantized models. URL: https://github.com/apache/incubator-tvm/pull/3943 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen closed pull request #4370: [WIP] Relay visualization: exporter + visualizer
tqchen closed pull request #4370: [WIP] Relay visualization: exporter + visualizer URL: https://github.com/apache/incubator-tvm/pull/4370 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on issue #4370: [WIP] Relay visualization: exporter + visualizer
tqchen commented on issue #4370: [WIP] Relay visualization: exporter + visualizer URL: https://github.com/apache/incubator-tvm/pull/4370#issuecomment-582719302 close due to inactive status, @hcho3 feel free to reopen :) This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen closed pull request #4456: [Relay][Legalize] Legalize conv2d cuda for NHWC
tqchen closed pull request #4456: [Relay][Legalize] Legalize conv2d cuda for NHWC URL: https://github.com/apache/incubator-tvm/pull/4456 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on issue #3934: [WIP][Runtime] MISRA-C compliant TVM runtime
tqchen commented on issue #3934: [WIP][Runtime] MISRA-C compliant TVM runtime URL: https://github.com/apache/incubator-tvm/pull/3934#issuecomment-582719122 ping @liangfu please see if you are interested in continue pushing this thread This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen closed pull request #4475: onnx frontend support layout choice depend on hardware target support…
tqchen closed pull request #4475: onnx frontend support layout choice depend on hardware target support… URL: https://github.com/apache/incubator-tvm/pull/4475 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on issue #4475: onnx frontend support layout choice depend on hardware target support…
tqchen commented on issue #4475: onnx frontend support layout choice depend on hardware target support… URL: https://github.com/apache/incubator-tvm/pull/4475#issuecomment-582719028 Close this for now, as it is likely superceded by the layout transform by @anijain2305 Thanks @Beya2019 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on issue #4750: Fix onnx import bugs
tqchen commented on issue #4750: Fix onnx import bugs URL: https://github.com/apache/incubator-tvm/pull/4750#issuecomment-582718831 ping @kice can you please add a few testcases? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on issue #4815: [TOPI][Relay] Add bitwise ops
tqchen commented on issue #4815: [TOPI][Relay] Add bitwise ops URL: https://github.com/apache/incubator-tvm/pull/4815#issuecomment-582718578 Thanks @jroesch @jwfromm @abergeron ! This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated (19d0d15 -> 2bd2f99)
This is an automated email from the ASF dual-hosted git repository. tqchen pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 19d0d15 [CONTRIB][CC] Enhance cc.cross_compiler (#4817) add 2bd2f99 [TOPI][Relay] Add bitwise ops (#4815) No new revisions were added by this update. Summary of changes: python/tvm/relay/op/_tensor.py | 7 +++ python/tvm/relay/op/tensor.py| 70 ++ src/relay/op/tensor/binary.cc| 18 src/relay/op/tensor/unary.cc | 13 +- topi/include/topi/broadcast.h| 42 ++ topi/include/topi/elemwise.h | 17 topi/python/topi/broadcast.py| 73 topi/src/topi.cc | 8 topi/tests/python/test_topi_broadcast.py | 72 +++ 9 files changed, 319 insertions(+), 1 deletion(-)
[GitHub] [incubator-tvm] tqchen merged pull request #4815: [TOPI][Relay] Add bitwise ops
tqchen merged pull request #4815: [TOPI][Relay] Add bitwise ops URL: https://github.com/apache/incubator-tvm/pull/4815 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on issue #4756: [Docker] update onnx to 1.6 and torch to 1.4
tqchen commented on issue #4756: [Docker] update onnx to 1.6 and torch to 1.4 URL: https://github.com/apache/incubator-tvm/pull/4756#issuecomment-582718289 updates - https://github.com/apache/incubator-tvm/pull/4827 - https://github.com/apache/incubator-tvm/pull/4826 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen opened a new pull request #4827: [CI][DOCKER] Update ci-gpu to v0.60
tqchen opened a new pull request #4827: [CI][DOCKER] Update ci-gpu to v0.60 URL: https://github.com/apache/incubator-tvm/pull/4827 cc @masahi This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen opened a new pull request #4826: [CI][DOCKER] Update ci-gpu torch1.4 and onnx1.6
tqchen opened a new pull request #4826: [CI][DOCKER] Update ci-gpu torch1.4 and onnx1.6 URL: https://github.com/apache/incubator-tvm/pull/4826 cc @masahi This is a docker image verified with testcases pass This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] FrozenGene commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser
FrozenGene commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#discussion_r375619314 ## File path: python/tvm/relay/frontend/pytorch.py ## @@ -0,0 +1,1023 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# pylint: disable=import-self, too-many-lines, len-as-condition, no-else-return, unused-variable, too-many-nested-blocks +# pylint: disable=consider-iterating-dictionary, invalid-name, unused-argument, unused-variable, broad-except +"""PT: PyTorch frontend.""" +import numpy as np + +import tvm + +from .. import analysis as _analysis +from .. import expr as _expr +from .. import module as _module +from .. import op as _op +from .common import get_relay_op +from .common import infer_shape as _infer_shape + +__all__ = ["from_pytorch"] + +# operator implementation +def _elemwise(name): +def _impl(inputs, input_types): +# TODO: Figure out a better way to get typing to work for tensor + scalar +type0 = input_types[0] +if isinstance(inputs[1], _expr.Expr): +type0 = input_types[1] + +type1 = input_types[1] +if isinstance(inputs[0], _expr.Expr): +type1 = input_types[0] + +data0 = _convert_elemwise_input(inputs[0], type0) +data1 = _convert_elemwise_input(inputs[1], type1) + +return get_relay_op(name)(data0, data1) +return _impl + +def _unsqueeze(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +return _op.transform.expand_dims(data, int(axis), 1) +return _impl + +def _concatenate(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +if isinstance(data, _expr.Expr): +data = [data] + +return _op.tensor.concatenate(data, int(axis)) +return _impl + +def _slice(): +def _impl(inputs, input_types): +data = inputs[0] +strides = [] + +if isinstance(data, _expr.Expr): +inferred_shape = _infer_shape(data) +end = [] +for infer in inferred_shape: +end.append(int(infer)) +if isinstance(data, _expr.Var): +end = inferred_shape +end = list(end) +else: +end = data.shape + +begin = [0]*len(end) +dim = int(inputs[1]) +begin[dim] = int(inputs[2]) + +if isinstance(inputs[3], str) and inputs[3].isdigit(): +end[dim] = min(end[dim], int(inputs[3])) +else: +end[dim] = inputs[3] + +strides.append(int(inputs[4])) +return _op.transform.strided_slice(data, begin, end, strides) +return _impl + +def _select(): +def _impl(inputs, input_types): +data = inputs[0] +dim = int(inputs[1]) +index = int(inputs[2]) + +return _op.transform.take(data, _expr.const(index, dtype="int32"), axis=dim) +return _impl + +def _ones(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Expr): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(1), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _zeros(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Expr): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(0), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _relu(): +def _impl(inputs, input_types): +data = inputs[0] +return _op.nn.relu(data) +return _impl + +def _adaptive_avg_2d(): +def _impl(inputs, input_types): +data = inputs[0] +output_size = _infer_shape(inputs[1]) + +return _op.contrib.contrib.adaptive_avg_pool2d( +data, +output_size=output_size) +return _impl + +def _adaptive_max_2d(): +def _impl(inputs, input_types): +data = inputs[0] +output_size = _infer_shape(inputs[1]) + +return _op.contrib.contrib.adaptive_max_pool2d( +data,
[GitHub] [incubator-tvm] FrozenGene commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser
FrozenGene commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#discussion_r375617997 ## File path: python/tvm/relay/frontend/pytorch.py ## @@ -0,0 +1,1023 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# pylint: disable=import-self, too-many-lines, len-as-condition, no-else-return, unused-variable, too-many-nested-blocks +# pylint: disable=consider-iterating-dictionary, invalid-name, unused-argument, unused-variable, broad-except +"""PT: PyTorch frontend.""" +import numpy as np + +import tvm + +from .. import analysis as _analysis +from .. import expr as _expr +from .. import module as _module +from .. import op as _op +from .common import get_relay_op +from .common import infer_shape as _infer_shape + +__all__ = ["from_pytorch"] + +# operator implementation +def _elemwise(name): +def _impl(inputs, input_types): +# TODO: Figure out a better way to get typing to work for tensor + scalar +type0 = input_types[0] +if isinstance(inputs[1], _expr.Expr): +type0 = input_types[1] + +type1 = input_types[1] +if isinstance(inputs[0], _expr.Expr): +type1 = input_types[0] + +data0 = _convert_elemwise_input(inputs[0], type0) +data1 = _convert_elemwise_input(inputs[1], type1) + +return get_relay_op(name)(data0, data1) +return _impl + +def _unsqueeze(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +return _op.transform.expand_dims(data, int(axis), 1) +return _impl + +def _concatenate(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +if isinstance(data, _expr.Expr): +data = [data] + +return _op.tensor.concatenate(data, int(axis)) +return _impl + +def _slice(): +def _impl(inputs, input_types): +data = inputs[0] +strides = [] + +if isinstance(data, _expr.Expr): +inferred_shape = _infer_shape(data) +end = [] +for infer in inferred_shape: +end.append(int(infer)) +if isinstance(data, _expr.Var): +end = inferred_shape +end = list(end) +else: +end = data.shape + +begin = [0]*len(end) +dim = int(inputs[1]) +begin[dim] = int(inputs[2]) + +if isinstance(inputs[3], str) and inputs[3].isdigit(): +end[dim] = min(end[dim], int(inputs[3])) +else: +end[dim] = inputs[3] + +strides.append(int(inputs[4])) +return _op.transform.strided_slice(data, begin, end, strides) +return _impl + +def _select(): +def _impl(inputs, input_types): +data = inputs[0] +dim = int(inputs[1]) +index = int(inputs[2]) + +return _op.transform.take(data, _expr.const(index, dtype="int32"), axis=dim) +return _impl + +def _ones(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Expr): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(1), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _zeros(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Expr): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(0), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _relu(): +def _impl(inputs, input_types): +data = inputs[0] +return _op.nn.relu(data) +return _impl + +def _adaptive_avg_2d(): +def _impl(inputs, input_types): +data = inputs[0] +output_size = _infer_shape(inputs[1]) + +return _op.contrib.contrib.adaptive_avg_pool2d( +data, +output_size=output_size) +return _impl + +def _adaptive_max_2d(): +def _impl(inputs, input_types): +data = inputs[0] +output_size = _infer_shape(inputs[1]) + +return _op.contrib.contrib.adaptive_max_pool2d( +data,
[GitHub] [incubator-tvm] FrozenGene commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser
FrozenGene commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#discussion_r375618821 ## File path: python/tvm/relay/frontend/pytorch.py ## @@ -0,0 +1,1023 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# pylint: disable=import-self, too-many-lines, len-as-condition, no-else-return, unused-variable, too-many-nested-blocks +# pylint: disable=consider-iterating-dictionary, invalid-name, unused-argument, unused-variable, broad-except +"""PT: PyTorch frontend.""" +import numpy as np + +import tvm + +from .. import analysis as _analysis +from .. import expr as _expr +from .. import module as _module +from .. import op as _op +from .common import get_relay_op +from .common import infer_shape as _infer_shape + +__all__ = ["from_pytorch"] + +# operator implementation +def _elemwise(name): +def _impl(inputs, input_types): +# TODO: Figure out a better way to get typing to work for tensor + scalar +type0 = input_types[0] +if isinstance(inputs[1], _expr.Expr): +type0 = input_types[1] + +type1 = input_types[1] +if isinstance(inputs[0], _expr.Expr): +type1 = input_types[0] + +data0 = _convert_elemwise_input(inputs[0], type0) +data1 = _convert_elemwise_input(inputs[1], type1) + +return get_relay_op(name)(data0, data1) +return _impl + +def _unsqueeze(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +return _op.transform.expand_dims(data, int(axis), 1) +return _impl + +def _concatenate(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +if isinstance(data, _expr.Expr): +data = [data] + +return _op.tensor.concatenate(data, int(axis)) +return _impl + +def _slice(): +def _impl(inputs, input_types): +data = inputs[0] +strides = [] + +if isinstance(data, _expr.Expr): +inferred_shape = _infer_shape(data) +end = [] +for infer in inferred_shape: +end.append(int(infer)) +if isinstance(data, _expr.Var): +end = inferred_shape +end = list(end) +else: +end = data.shape + +begin = [0]*len(end) +dim = int(inputs[1]) +begin[dim] = int(inputs[2]) + +if isinstance(inputs[3], str) and inputs[3].isdigit(): +end[dim] = min(end[dim], int(inputs[3])) +else: +end[dim] = inputs[3] + +strides.append(int(inputs[4])) +return _op.transform.strided_slice(data, begin, end, strides) +return _impl + +def _select(): +def _impl(inputs, input_types): +data = inputs[0] +dim = int(inputs[1]) +index = int(inputs[2]) + +return _op.transform.take(data, _expr.const(index, dtype="int32"), axis=dim) +return _impl + +def _ones(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Expr): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(1), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _zeros(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Expr): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(0), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _relu(): +def _impl(inputs, input_types): +data = inputs[0] +return _op.nn.relu(data) +return _impl + +def _adaptive_avg_2d(): +def _impl(inputs, input_types): +data = inputs[0] +output_size = _infer_shape(inputs[1]) + +return _op.contrib.contrib.adaptive_avg_pool2d( +data, +output_size=output_size) +return _impl + +def _adaptive_max_2d(): +def _impl(inputs, input_types): +data = inputs[0] +output_size = _infer_shape(inputs[1]) + +return _op.contrib.contrib.adaptive_max_pool2d( +data,
[GitHub] [incubator-tvm] soiferj commented on issue #4825: [Frontend][ONNX] LSTM Support
soiferj commented on issue #4825: [Frontend][ONNX] LSTM Support URL: https://github.com/apache/incubator-tvm/pull/4825#issuecomment-582713458 Awesome!! I was looking at implementing this myself yesterday. I’ll take a look as soon as possible. Thanks for sending the PR! This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] FrozenGene merged pull request #4817: [CONTRIB][CC] Enhance cc.cross_compiler
FrozenGene merged pull request #4817: [CONTRIB][CC] Enhance cc.cross_compiler URL: https://github.com/apache/incubator-tvm/pull/4817 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated (5ea4f0d -> 19d0d15)
This is an automated email from the ASF dual-hosted git repository. zhaowu pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 5ea4f0d [Relay] Conv2D padding representation (#4787) add 19d0d15 [CONTRIB][CC] Enhance cc.cross_compiler (#4817) No new revisions were added by this update. Summary of changes: python/tvm/contrib/cc.py | 69 +++ tests/python/unittest/test_module_load.py | 7 ++-- 2 files changed, 46 insertions(+), 30 deletions(-)
[GitHub] [incubator-tvm] FrozenGene commented on issue #4817: [CONTRIB][CC] Enhance cc.cross_compiler
FrozenGene commented on issue #4817: [CONTRIB][CC] Enhance cc.cross_compiler URL: https://github.com/apache/incubator-tvm/pull/4817#issuecomment-582711329 Thanks @tqchen @jroesch It is Merged. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] jwfromm opened a new pull request #4825: [Frontend][ONNX] LSTM Support
jwfromm opened a new pull request #4825: [Frontend][ONNX] LSTM Support URL: https://github.com/apache/incubator-tvm/pull/4825 This PR adds LSTM support to the relay Onnx frontend. Besides adding the LSTM parser itself, we encountered an issue where for some Onnx operations (like LSTMs) arguments are optional. The current method for passing arguments to converters is just to pack them into a list however as some arguments are optional the position of each input is inconsistent. Instead, we should be using a dictionary mapping input names to their value. However, changing all inputs to a dictionary would require changing all the current operators and present problems with direct Onnx to relay conversions. Our workaround here is to add the `onnx_input` class that can be accessed as a list as we previously did or with input name dictionary style lookup. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] jwfromm commented on issue #4825: [Frontend][ONNX] LSTM Support
jwfromm commented on issue #4825: [Frontend][ONNX] LSTM Support URL: https://github.com/apache/incubator-tvm/pull/4825#issuecomment-582692382 @masahi, @soiferj, @mbrookhart can you take a look at this PR? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] zhiics commented on a change in pull request #4771: [Relay] Added Merge Composite pass
zhiics commented on a change in pull request #4771: [Relay] Added Merge
Composite pass
URL: https://github.com/apache/incubator-tvm/pull/4771#discussion_r375526504
##
File path: src/relay/pass/merge_composite.cc
##
@@ -0,0 +1,205 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file src/relay/pass/merge_composite.cc
+ * \brief Merges expressions matching patterns into functions marked
+ * as 'composite'. This is primarily intended to be used alongside the
+ * external codegen infrastructure to support the case where multiple
+ * Relay operators map to a single external operator.
+ */
+
+#include
+#include
+#include
+#include
+#include
+
+namespace tvm {
+namespace relay {
+namespace merge_composite {
+
+
+class MergeCompositeWrapper : public ExprMutator {
+ public:
+ explicit MergeCompositeWrapper(const std::string& pattern_name, const Expr&
pattern)
+: pattern_name_(pattern_name), pattern_(pattern) {}
+
+ Expr ExtractPattern(const Var& pattern, const Expr& root,
+ Map>* var_map) {
+if (var_map->find(pattern->name_hint()) == var_map->end()) {
+ // if we haven't encountered this var yet, make a new free var and
associate
+ // it with the value at 'root'
+ auto free_var = VarNode::make(pattern->name_hint(), Type());
+ var_map->Set(pattern->name_hint(), Array({free_var, root}));
+ return std::move(free_var);
+} else {
+ // if we have encountered this var already, return the free var that was
created
+ return (*var_map)[pattern->name_hint()][0];
+}
+ }
+
+ Expr ExtractPattern(const Constant& pattern, const Expr& root,
+ Map>* var_map) {
+return root;
+ }
+
+ /* How does this work?
Review comment:
Let's document this in the following style
\brief
\param A
...
\param N
\return
\note How does it work?
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[GitHub] [incubator-tvm] zhiics commented on a change in pull request #4771: [Relay] Added Merge Composite pass
zhiics commented on a change in pull request #4771: [Relay] Added Merge
Composite pass
URL: https://github.com/apache/incubator-tvm/pull/4771#discussion_r375530819
##
File path: src/relay/pass/merge_composite.cc
##
@@ -0,0 +1,205 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file src/relay/pass/merge_composite.cc
+ * \brief Merges expressions matching patterns into functions marked
+ * as 'composite'. This is primarily intended to be used alongside the
+ * external codegen infrastructure to support the case where multiple
+ * Relay operators map to a single external operator.
+ */
+
+#include
+#include
+#include
+#include
+#include
+
+namespace tvm {
+namespace relay {
+namespace merge_composite {
+
+
+class MergeCompositeWrapper : public ExprMutator {
+ public:
+ explicit MergeCompositeWrapper(const std::string& pattern_name, const Expr&
pattern)
+: pattern_name_(pattern_name), pattern_(pattern) {}
+
+ Expr ExtractPattern(const Var& pattern, const Expr& root,
+ Map>* var_map) {
+if (var_map->find(pattern->name_hint()) == var_map->end()) {
+ // if we haven't encountered this var yet, make a new free var and
associate
+ // it with the value at 'root'
+ auto free_var = VarNode::make(pattern->name_hint(), Type());
+ var_map->Set(pattern->name_hint(), Array({free_var, root}));
+ return std::move(free_var);
+} else {
+ // if we have encountered this var already, return the free var that was
created
+ return (*var_map)[pattern->name_hint()][0];
+}
+ }
+
+ Expr ExtractPattern(const Constant& pattern, const Expr& root,
+ Map>* var_map) {
+return root;
+ }
+
+ /* How does this work?
+ *
+ * A pattern consists of Relay expression containing only operator call
nodes, constants
+ * and free variables. The free variables indicate where the pattern can
'attach' in your
+ * graph. This function takes the final call node of the pattern and the
call node currently
+ * being traversed in the Relay graph. It traverses through the pattern in
lockstep with call node
+ * from the graph (referred to as the 'root' node here) to check they're
identical. If at any point
+ * they differ, an empty expression is returned to signify the extract
failed. If a free var is
+ * reached in the pattern, the corresponding value in the root is associated
with the name of the
+ * free var (via the var_map) so that when we construct the composite
function, the inputs match
+ * up correctly with the rest of the graph. The return value of this
function when successful is
+ * a new Relay expression ready to be wrapped into a composite function.
+ */
+ Expr ExtractPattern(const Call& pattern, const Call& root,
+ Map>* var_map) {
+// check to make sure both calls are to operators (not functions)
+if (!pattern->op->IsInstance() || !root->op->IsInstance())
+ return Expr();
+if (pattern->op.as()->name != root->op.as()->name)
+ return Expr();
+
+unsigned int i = 0;
+Array new_args;
+for (const auto& arg : pattern->args) {
+ Expr new_arg;
+ if (arg->IsInstance()) {
+// fail if the root argument is not also a call node
+if (!root->args[i]->IsInstance()) {
+ return Expr();
+}
+// if it's a call node, recursively call this function
+new_arg = ExtractPattern(Downcast(arg),
+ Downcast(root->args[i]),
+ var_map);
+ } else if (arg->IsInstance()) {
+// if there's a var in the pattern, it must be a free var
+// so call the function to update the var_map
+new_arg = ExtractPattern(Downcast(arg),
+ root->args[i],
+ var_map);
+ } else if (arg->IsInstance()) {
+// if there's a constant, simply get the corresponding
+// value of the constant from the root
+new_arg = ExtractPattern(Downcast(arg),
+ root->args[i],
+ var_map);
+ }
+ if (!new_arg.defined()) {
+return Expr();
+ }
+
[GitHub] [incubator-tvm] zhiics commented on a change in pull request #4771: [Relay] Added Merge Composite pass
zhiics commented on a change in pull request #4771: [Relay] Added Merge
Composite pass
URL: https://github.com/apache/incubator-tvm/pull/4771#discussion_r375524422
##
File path: src/relay/pass/merge_composite.cc
##
@@ -0,0 +1,205 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file src/relay/pass/merge_composite.cc
+ * \brief Merges expressions matching patterns into functions marked
+ * as 'composite'. This is primarily intended to be used alongside the
+ * external codegen infrastructure to support the case where multiple
+ * Relay operators map to a single external operator.
+ */
+
+#include
+#include
+#include
+#include
+#include
+
+namespace tvm {
+namespace relay {
+namespace merge_composite {
+
Review comment:
remove one blank line
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[GitHub] [incubator-tvm] zhiics commented on a change in pull request #4771: [Relay] Added Merge Composite pass
zhiics commented on a change in pull request #4771: [Relay] Added Merge
Composite pass
URL: https://github.com/apache/incubator-tvm/pull/4771#discussion_r375542736
##
File path: tests/python/relay/test_pass_merge_composite.py
##
@@ -0,0 +1,439 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Unit tests for merge composite."""
+from tvm import expr
+from tvm import relay
+from tvm.relay.testing import run_opt_pass
+
+"""
+The merge composite pass is designed to merge multiple relay operators, that
+match a given pattern, and combine them into a single relay function.
+
+For example suppose we have the graph:
+
+conv2d
+ | (merge composite pass)
+ bias_add> conv2d_bias_relu
+ |(our target)
+ relu
+
+Our Relay IR before the pass:
+fn (%data: Tensor[(1, 512, 28, 28), float32], %kernel: Tensor[(256, 512,
1, 1), float32],
+%bias: Tensor[(256), float32]) -> Tensor[(1, 256, 28, 28),
float32] {
+%0 = nn.conv2d(%data, %kernel, kernel_size=[1, 1])
+/* ty=Tensor[(1, 256, 28, 28), float32] */;
+%1 = nn.bias_add(%0, %bias) /* ty=Tensor[(1, 256, 28, 28), float32] */;
+nn.relu(%1) /* ty=Tensor[(1, 256, 28, 28), float32] */
+}
+
+Our Relay IR after the pass:
+fn (%data: Tensor[(1, 512, 28, 28), float32], %kernel: Tensor[(256, 512,
1, 1), float32],
+%bias: Tensor[(256), float32]) -> Tensor[(1, 256, 28, 28),
float32] {
+ %2 = fn (%x: Tensor[(1, 512, 28, 28), float32], %y: Tensor[(256, 512, 1,
1), float32],
+%z: Tensor[(256), float32], Primitive=1,
Composite="conv2d_bias_relu") ->
+Tensor[(1, 256, 28, 28), float32] {
+%0 = nn.conv2d(%x, %y, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 28,
28), float32] */;
+%1 = nn.bias_add(%0, %z) /* ty=Tensor[(1, 256, 28, 28), float32] */;
+nn.relu(%1) /* ty=Tensor[(1, 256, 28, 28), float32] */
+ };
+ %2(%data, %kernel, %bias) /* ty=Tensor[(1, 256, 28, 28), float32] */
+}
+
+As you can see in the second relay example, the pattern we specified has been
wrapped
+in a function. The function is then called, producing the same result as the
first relay
+example.
+
+One convenient use for this pass is to offload multiple operators to a single
external
+codegen function.
+"""
+
+
+def make_add_sub_mul_pattern():
+"""Create a pattern to match the following graph.
+
+add sub
+ \ /
+ \ /
+ mul
+"""
+x = relay.var('x')
+y = relay.var('y')
+add_node = relay.add(x, y)
+sub_node = relay.subtract(x, y)
+mul_node = relay.multiply(add_node, sub_node)
+return mul_node
+
+
+def make_add_relu_pattern():
+"""Create a pattern to match the following graph.
+
+add
+ |
+ relu
+"""
+x = relay.var('x')
+y = relay.var('y')
+add_node = relay.add(x, y)
+r = relay.nn.relu(add_node)
+return r
+
+
+def make_conv_bias_relu_pattern():
+"""Create a pattern to match the following graph.
+
+ conv2d
+ |
+ bias_add
+ |
+ relu
+"""
+x = relay.var('x')
+y = relay.var('y')
+z = relay.var('z')
+conv_node = relay.nn.conv2d(x, y)
+bias_node = relay.nn.bias_add(conv_node, z)
+r = relay.nn.relu(bias_node)
+return r
+
+
+def test_simple_merge():
+"""Test composite function is correctly produced from simple graph.
+
+We could expect the pattern `make_add_relu_pattern` to be merged
+into a single op `add_relu`.
+
+a b
+\ / a b
+add> \ /
+ | add_relu
+ relu
+
+"""
+pattern_table = [
+("add_relu", make_add_relu_pattern())
+]
+
+def before():
+a = relay.var('a', shape=(10, 10))
+b = relay.var('b', shape=(10, 10))
+add_node = relay.add(a, b)
+r = relay.nn.relu(add_node)
+return relay.Function([a, b], r)
+
+def expected():
+a = relay.var('a', shape=(10, 10))
+b = relay.var('b', shape=(10, 10))
+
+# add_relu function
+in_1 = relay.var('in_1', shape=(10, 10))
+in_2 = relay.var('in_2', shape=(
[GitHub] [incubator-tvm] zhiics commented on a change in pull request #4771: [Relay] Added Merge Composite pass
zhiics commented on a change in pull request #4771: [Relay] Added Merge
Composite pass
URL: https://github.com/apache/incubator-tvm/pull/4771#discussion_r375526504
##
File path: src/relay/pass/merge_composite.cc
##
@@ -0,0 +1,205 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file src/relay/pass/merge_composite.cc
+ * \brief Merges expressions matching patterns into functions marked
+ * as 'composite'. This is primarily intended to be used alongside the
+ * external codegen infrastructure to support the case where multiple
+ * Relay operators map to a single external operator.
+ */
+
+#include
+#include
+#include
+#include
+#include
+
+namespace tvm {
+namespace relay {
+namespace merge_composite {
+
+
+class MergeCompositeWrapper : public ExprMutator {
+ public:
+ explicit MergeCompositeWrapper(const std::string& pattern_name, const Expr&
pattern)
+: pattern_name_(pattern_name), pattern_(pattern) {}
+
+ Expr ExtractPattern(const Var& pattern, const Expr& root,
+ Map>* var_map) {
+if (var_map->find(pattern->name_hint()) == var_map->end()) {
+ // if we haven't encountered this var yet, make a new free var and
associate
+ // it with the value at 'root'
+ auto free_var = VarNode::make(pattern->name_hint(), Type());
+ var_map->Set(pattern->name_hint(), Array({free_var, root}));
+ return std::move(free_var);
+} else {
+ // if we have encountered this var already, return the free var that was
created
+ return (*var_map)[pattern->name_hint()][0];
+}
+ }
+
+ Expr ExtractPattern(const Constant& pattern, const Expr& root,
+ Map>* var_map) {
+return root;
+ }
+
+ /* How does this work?
Review comment:
Let's document this in the following style
\brief
\param A
...
\param N
\return
\Note How does it work?
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[GitHub] [incubator-tvm] zhiics commented on a change in pull request #4771: [Relay] Added Merge Composite pass
zhiics commented on a change in pull request #4771: [Relay] Added Merge
Composite pass
URL: https://github.com/apache/incubator-tvm/pull/4771#discussion_r375547370
##
File path: src/relay/pass/merge_composite.cc
##
@@ -0,0 +1,205 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file src/relay/pass/merge_composite.cc
+ * \brief Merges expressions matching patterns into functions marked
+ * as 'composite'. This is primarily intended to be used alongside the
+ * external codegen infrastructure to support the case where multiple
+ * Relay operators map to a single external operator.
+ */
+
+#include
+#include
+#include
+#include
+#include
+
+namespace tvm {
+namespace relay {
+namespace merge_composite {
+
+
+class MergeCompositeWrapper : public ExprMutator {
+ public:
+ explicit MergeCompositeWrapper(const std::string& pattern_name, const Expr&
pattern)
+: pattern_name_(pattern_name), pattern_(pattern) {}
+
+ Expr ExtractPattern(const Var& pattern, const Expr& root,
+ Map>* var_map) {
+if (var_map->find(pattern->name_hint()) == var_map->end()) {
+ // if we haven't encountered this var yet, make a new free var and
associate
+ // it with the value at 'root'
+ auto free_var = VarNode::make(pattern->name_hint(), Type());
+ var_map->Set(pattern->name_hint(), Array({free_var, root}));
+ return std::move(free_var);
+} else {
+ // if we have encountered this var already, return the free var that was
created
+ return (*var_map)[pattern->name_hint()][0];
+}
+ }
+
+ Expr ExtractPattern(const Constant& pattern, const Expr& root,
+ Map>* var_map) {
+return root;
+ }
+
+ /* How does this work?
+ *
+ * A pattern consists of Relay expression containing only operator call
nodes, constants
+ * and free variables. The free variables indicate where the pattern can
'attach' in your
+ * graph. This function takes the final call node of the pattern and the
call node currently
+ * being traversed in the Relay graph. It traverses through the pattern in
lockstep with call node
+ * from the graph (referred to as the 'root' node here) to check they're
identical. If at any point
+ * they differ, an empty expression is returned to signify the extract
failed. If a free var is
+ * reached in the pattern, the corresponding value in the root is associated
with the name of the
+ * free var (via the var_map) so that when we construct the composite
function, the inputs match
+ * up correctly with the rest of the graph. The return value of this
function when successful is
+ * a new Relay expression ready to be wrapped into a composite function.
+ */
+ Expr ExtractPattern(const Call& pattern, const Call& root,
+ Map>* var_map) {
+// check to make sure both calls are to operators (not functions)
+if (!pattern->op->IsInstance() || !root->op->IsInstance())
+ return Expr();
+if (pattern->op.as()->name != root->op.as()->name)
+ return Expr();
+
+unsigned int i = 0;
+Array new_args;
+for (const auto& arg : pattern->args) {
+ Expr new_arg;
+ if (arg->IsInstance()) {
+// fail if the root argument is not also a call node
+if (!root->args[i]->IsInstance()) {
+ return Expr();
+}
+// if it's a call node, recursively call this function
+new_arg = ExtractPattern(Downcast(arg),
+ Downcast(root->args[i]),
+ var_map);
+ } else if (arg->IsInstance()) {
+// if there's a var in the pattern, it must be a free var
+// so call the function to update the var_map
+new_arg = ExtractPattern(Downcast(arg),
+ root->args[i],
+ var_map);
+ } else if (arg->IsInstance()) {
+// if there's a constant, simply get the corresponding
+// value of the constant from the root
+new_arg = ExtractPattern(Downcast(arg),
+ root->args[i],
+ var_map);
+ }
+ if (!new_arg.defined()) {
+return Expr();
+ }
+
[GitHub] [incubator-tvm] tqchen commented on issue #4821: OSError: [WinError 10048] Only one usage of each socket address (protocol/network address/port) is normally permitted and OSError: [WinError 1
tqchen commented on issue #4821: OSError: [WinError 10048] Only one usage of each socket address (protocol/network address/port) is normally permitted and OSError: [WinError 10049] The requested address is not valid in its context URL: https://github.com/apache/incubator-tvm/issues/4821#issuecomment-582672692 Given that we do not have a clearly actionable item atm, I would recommend to bring a trouble shooting thread on https://discuss.tvm.ai/ instead. Feel free to open new thread after we have something that is actionable This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen closed issue #4821: OSError: [WinError 10048] Only one usage of each socket address (protocol/network address/port) is normally permitted and OSError: [WinError 10049]
tqchen closed issue #4821: OSError: [WinError 10048] Only one usage of each socket address (protocol/network address/port) is normally permitted and OSError: [WinError 10049] The requested address is not valid in its context URL: https://github.com/apache/incubator-tvm/issues/4821 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated (79ce87f -> 5ea4f0d)
This is an automated email from the ASF dual-hosted git repository. kevinthesun pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 79ce87f [Relay][Frontend][TFLite] Add parser support for logical operators (#4642) add 5ea4f0d [Relay] Conv2D padding representation (#4787) No new revisions were added by this update. Summary of changes: python/tvm/relay/op/nn/nn.py| 23 -- python/tvm/relay/op/nn/util.py | 56 + tests/python/relay/test_pass_alter_op_layout.py | 2 +- tests/python/unittest/test_graph_tuner_core.py | 22 +- topi/python/topi/cuda/conv2d.py | 3 +- 5 files changed, 90 insertions(+), 16 deletions(-) create mode 100644 python/tvm/relay/op/nn/util.py
[GitHub] [incubator-tvm] kevinthesun commented on issue #4787: [Relay] Conv2D padding representation
kevinthesun commented on issue #4787: [Relay] Conv2D padding representation URL: https://github.com/apache/incubator-tvm/pull/4787#issuecomment-582664192 Thanks @zxy844288792 @icemelon9 @comaniac This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] anijain2305 commented on a change in pull request #4790: Fast exponent
anijain2305 commented on a change in pull request #4790: Fast exponent
URL: https://github.com/apache/incubator-tvm/pull/4790#discussion_r375563628
##
File path: topi/include/topi/elemwise.h
##
@@ -360,5 +359,66 @@ inline Tensor full_like(const Tensor& x,
}, name, tag);
}
+/*
+ * \brief Fast exponential function implementation
+ * log2(e^x) = x * log2(e) * log2(2) =>
+ * log2(e^x) = log2(2^(x*log2(e))) =>
+ * e^x = 2^(x*log2(e))
+ * Splitting power x*log2(e) into integer and fractional parts:
+ * e^(n+f) = e^n * e^f
+ * n = floor(x*log2(e) + 1/2)
+ * f = x - n * ln(2)
+ * exp(x) = 2^n * exp(y)
+ * Approximation for fractional part:
+ * y = exp(f) = 1 + 2 * P(x**2)/(Q(x**2) - P(x**2))
+ */
+inline Tensor fast_exp(const Tensor& _x,
+ std::string name,
+ std::string tag) {
+ auto x_hi = make_const(DataType::Float(32), 88.3762626647950f);
+ auto x_lo = make_const(DataType::Float(32), -88.3762626647949f);
+ auto log2e = make_const(DataType::Float(32), 1.44269504088896341f);
+ auto ln2 = make_const(DataType::Float(32), 0.6931471805599453f);
+ PrimExpr p[6] = {make_const(DataType::Float(32), 1.9875691500E-4f),
+ make_const(DataType::Float(32), 1.3981999507E-3f),
+ make_const(DataType::Float(32), 8.3334519073E-3f),
+ make_const(DataType::Float(32), 4.1665795894E-2f),
+ make_const(DataType::Float(32), 1.665459E-1f),
+ make_const(DataType::Float(32), 5.001201E-1f)};
+ auto one = make_const(DataType::Float(32), 1.0f);
+ auto one_half = make_const(DataType::Float(32), 0.5f);
+ auto b = make_const(DataType::Float(32), 127.0f);
+
+ return compute(_x->shape,
+ [&](const Array& i) {
+ // clamp x
+ auto x = ::tvm::max(::tvm::min(_x(i), x_hi), x_lo);
+ // integer part
+ auto n = ::tvm::floor(x * log2e + one_half);
+ // fractional part
+ auto f = x - n * ln2;
+ auto y = (p[0] * f + p[1]) * f + p[2]) * f + p[3])* f+
p[4]) * f
+ + p[5]) * f* f + f + one;
+ // Return 2^m * exp(r).
+ auto ef = tvm::reinterpret(DataType::Float(32),
+ ::tvm::cast(DataType::Int(32), n
+ b) << 23);
+ return ::tvm::max(ef * y, _x(i)); // NOLINT(*)
+ },
+ name, tag);
+}
+
+
+inline Tensor exp(const Tensor& x,
+ std::string name = "T_exp",
+ std::string tag = kElementWise) {
+ if (x->dtype == DataType::Float(32)) {
+return fast_exp(x, name, tag);
Review comment:
How about having 3 new relay contrib operators - contrib.fast_exp,
contrib.fast_tanh, contrib.fast_softmax. We can then add a Relay pass with
opt_level 4, that legalizes these functions to their approximate counterparts.
Edit - Sorry should have told why these 3. For softmax, we are essentially
playing with exp op. Softmax takes substantial time in SSD models, where input
shape is very large. For tanh, we already have a fast_tanh that is enabled by
default. We should change that.
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[GitHub] [incubator-tvm] kevinthesun merged pull request #4787: [Relay] Conv2D padding representation
kevinthesun merged pull request #4787: [Relay] Conv2D padding representation URL: https://github.com/apache/incubator-tvm/pull/4787 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] junrushao1994 commented on a change in pull request #4644: [WIP] Relay op strategy
junrushao1994 commented on a change in pull request #4644: [WIP] Relay op
strategy
URL: https://github.com/apache/incubator-tvm/pull/4644#discussion_r375566300
##
File path: include/tvm/relay/op_attr_types.h
##
@@ -207,14 +216,182 @@ enum AnyCodegenStrategy {
kVariableDimensions
};
-/* \brief A runtime representation of shape. */
+/*! \brief A runtime representation of shape. */
using Shape = Array;
using FShapeFunc = runtime::TypedPackedFunc<
Array(const Attrs& attrs,
const Array& inputs,
const Array& out_ndims)>;
+/*!
+ * \brief Operator implementation in TVM.
+ */
+class OpImplementNode : public Object {
+ public:
+ /*! \brief Compute function */
+ FTVMCompute fcompute;
+ /*! \brief Schedule function */
+ FTVMSchedule fschedule;
+ /*! \brief Priority level */
+ Integer plevel;
+
+ void VisitAttrs(tvm::AttrVisitor* v) {
+v->Visit("plevel", &plevel);
+ }
+
+ static constexpr const char* _type_key = "relay.OpImplement";
+ TVM_DECLARE_FINAL_OBJECT_INFO(OpImplementNode, Object);
+};
+
+/*!
+ * \brief Operator implementation class.
+ */
+class OpImplement : public ObjectRef {
+ public:
+ /*! \brief default constructor */
+ OpImplement() {}
+ /*! \brief constructor from node pointer */
+ explicit OpImplement(ObjectPtr n) : ObjectRef(n) {}
+ /*!
+ * \brief access the internal node container
+ * \return the pointer to the internal node container
+ */
+ inline const OpImplementNode* operator->() const;
Review comment:
Shall we use `TVM_DEFINE_OBJECT_REF_METHODS` instead?
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[GitHub] [incubator-tvm] alexwong commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser
alexwong commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#discussion_r375563704 ## File path: python/tvm/relay/frontend/pytorch.py ## @@ -0,0 +1,1023 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# pylint: disable=import-self, too-many-lines, len-as-condition, no-else-return, unused-variable, too-many-nested-blocks +# pylint: disable=consider-iterating-dictionary, invalid-name, unused-argument, unused-variable, broad-except +"""PT: PyTorch frontend.""" +import numpy as np + +import tvm + +from .. import analysis as _analysis +from .. import expr as _expr +from .. import module as _module +from .. import op as _op +from .common import get_relay_op +from .common import infer_shape as _infer_shape + +__all__ = ["from_pytorch"] + +# operator implementation +def _elemwise(name): +def _impl(inputs, input_types): +# TODO: Figure out a better way to get typing to work for tensor + scalar +type0 = input_types[0] +if isinstance(inputs[1], _expr.Expr): +type0 = input_types[1] + +type1 = input_types[1] +if isinstance(inputs[0], _expr.Expr): +type1 = input_types[0] + +data0 = _convert_elemwise_input(inputs[0], type0) +data1 = _convert_elemwise_input(inputs[1], type1) + +return get_relay_op(name)(data0, data1) +return _impl + +def _unsqueeze(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +return _op.transform.expand_dims(data, int(axis), 1) +return _impl + +def _concatenate(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +if isinstance(data, _expr.Expr): +data = [data] + +return _op.tensor.concatenate(data, int(axis)) +return _impl + +def _slice(): +def _impl(inputs, input_types): +data = inputs[0] +strides = [] + +if isinstance(data, _expr.Expr): +inferred_shape = _infer_shape(data) +end = [] +for infer in inferred_shape: +end.append(int(infer)) +if isinstance(data, _expr.Var): +end = inferred_shape +end = list(end) +else: +end = data.shape + +begin = [0]*len(end) +dim = int(inputs[1]) +begin[dim] = int(inputs[2]) + +if isinstance(inputs[3], str) and inputs[3].isdigit(): +end[dim] = min(end[dim], int(inputs[3])) +else: +end[dim] = inputs[3] + +strides.append(int(inputs[4])) +return _op.transform.strided_slice(data, begin, end, strides) +return _impl + +def _select(): +def _impl(inputs, input_types): +data = inputs[0] +dim = int(inputs[1]) +index = int(inputs[2]) + +return _op.transform.take(data, _expr.const(index, dtype="int32"), axis=dim) +return _impl + +def _ones(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Expr): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(1), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _zeros(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Expr): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(0), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _relu(): +def _impl(inputs, input_types): +data = inputs[0] +return _op.nn.relu(data) +return _impl + +def _adaptive_avg_2d(): +def _impl(inputs, input_types): +data = inputs[0] +output_size = _infer_shape(inputs[1]) + +return _op.contrib.contrib.adaptive_avg_pool2d( +data, +output_size=output_size) +return _impl + +def _adaptive_max_2d(): +def _impl(inputs, input_types): +data = inputs[0] +output_size = _infer_shape(inputs[1]) + +return _op.contrib.contrib.adaptive_max_pool2d( +data, +
[GitHub] [incubator-tvm] anijain2305 commented on a change in pull request #4790: Fast exponent
anijain2305 commented on a change in pull request #4790: Fast exponent
URL: https://github.com/apache/incubator-tvm/pull/4790#discussion_r375563628
##
File path: topi/include/topi/elemwise.h
##
@@ -360,5 +359,66 @@ inline Tensor full_like(const Tensor& x,
}, name, tag);
}
+/*
+ * \brief Fast exponential function implementation
+ * log2(e^x) = x * log2(e) * log2(2) =>
+ * log2(e^x) = log2(2^(x*log2(e))) =>
+ * e^x = 2^(x*log2(e))
+ * Splitting power x*log2(e) into integer and fractional parts:
+ * e^(n+f) = e^n * e^f
+ * n = floor(x*log2(e) + 1/2)
+ * f = x - n * ln(2)
+ * exp(x) = 2^n * exp(y)
+ * Approximation for fractional part:
+ * y = exp(f) = 1 + 2 * P(x**2)/(Q(x**2) - P(x**2))
+ */
+inline Tensor fast_exp(const Tensor& _x,
+ std::string name,
+ std::string tag) {
+ auto x_hi = make_const(DataType::Float(32), 88.3762626647950f);
+ auto x_lo = make_const(DataType::Float(32), -88.3762626647949f);
+ auto log2e = make_const(DataType::Float(32), 1.44269504088896341f);
+ auto ln2 = make_const(DataType::Float(32), 0.6931471805599453f);
+ PrimExpr p[6] = {make_const(DataType::Float(32), 1.9875691500E-4f),
+ make_const(DataType::Float(32), 1.3981999507E-3f),
+ make_const(DataType::Float(32), 8.3334519073E-3f),
+ make_const(DataType::Float(32), 4.1665795894E-2f),
+ make_const(DataType::Float(32), 1.665459E-1f),
+ make_const(DataType::Float(32), 5.001201E-1f)};
+ auto one = make_const(DataType::Float(32), 1.0f);
+ auto one_half = make_const(DataType::Float(32), 0.5f);
+ auto b = make_const(DataType::Float(32), 127.0f);
+
+ return compute(_x->shape,
+ [&](const Array& i) {
+ // clamp x
+ auto x = ::tvm::max(::tvm::min(_x(i), x_hi), x_lo);
+ // integer part
+ auto n = ::tvm::floor(x * log2e + one_half);
+ // fractional part
+ auto f = x - n * ln2;
+ auto y = (p[0] * f + p[1]) * f + p[2]) * f + p[3])* f+
p[4]) * f
+ + p[5]) * f* f + f + one;
+ // Return 2^m * exp(r).
+ auto ef = tvm::reinterpret(DataType::Float(32),
+ ::tvm::cast(DataType::Int(32), n
+ b) << 23);
+ return ::tvm::max(ef * y, _x(i)); // NOLINT(*)
+ },
+ name, tag);
+}
+
+
+inline Tensor exp(const Tensor& x,
+ std::string name = "T_exp",
+ std::string tag = kElementWise) {
+ if (x->dtype == DataType::Float(32)) {
+return fast_exp(x, name, tag);
Review comment:
How about having 3 new relay contrib operators - contrib.fast_exp,
contrib.fast_tanh, contrib.fast_softmax. We can then add a Relay pass with
opt_level 4, that legalizes these functions to their approximate counterparts.
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[GitHub] [incubator-tvm] tqchen commented on a change in pull request #4818: [REFACTOR][PY] Establish tvm.runtime
tqchen commented on a change in pull request #4818: [REFACTOR][PY] Establish
tvm.runtime
URL: https://github.com/apache/incubator-tvm/pull/4818#discussion_r375561036
##
File path: python/tvm/runtime/ndarray.py
##
@@ -18,132 +18,36 @@
"""Runtime NDArray api"""
import ctypes
import numpy as np
-from .base import _LIB, check_call, c_array, string_types, _FFI_MODE, c_str
-from .runtime_ctypes import TVMType, TVMContext, TVMArray, TVMArrayHandle
-from .runtime_ctypes import TypeCode, tvm_shape_index_t
+import tvm._ffi
+
+from tvm._ffi.base import _LIB, check_call, c_array, string_types, _FFI_MODE
+from tvm._ffi.runtime_ctypes import DataType, TVMContext, TVMArray,
TVMArrayHandle
+from tvm._ffi.runtime_ctypes import TypeCode, tvm_shape_index_t
try:
# pylint: disable=wrong-import-position
if _FFI_MODE == "ctypes":
raise ImportError()
-from ._cy3.core import _set_class_ndarray, _make_array, _from_dlpack
-from ._cy3.core import NDArrayBase as _NDArrayBase
+from tvm._ffi._cy3.core import _set_class_ndarray, _make_array,
_from_dlpack
+from tvm._ffi._cy3.core import NDArrayBase
except (RuntimeError, ImportError):
# pylint: disable=wrong-import-position
-from ._ctypes.ndarray import _set_class_ndarray, _make_array, _from_dlpack
-from ._ctypes.ndarray import NDArrayBase as _NDArrayBase
-
-
-def context(dev_type, dev_id=0):
-"""Construct a TVM context with given device type and id.
-
-Parameters
---
-dev_type: int or str
-The device type mask or name of the device.
-
-dev_id : int, optional
-The integer device id
-
-Returns
----
-ctx: TVMContext
-The corresponding context.
-
-Examples
-
-Context can be used to create reflection of context by
-string representation of the device type.
-
-.. code-block:: python
-
- assert tvm.context("cpu", 1) == tvm.cpu(1)
- assert tvm.context("gpu", 0) == tvm.gpu(0)
- assert tvm.context("cuda", 0) == tvm.gpu(0)
-"""
-if isinstance(dev_type, string_types):
-if '-device=micro_dev' in dev_type:
-dev_type = 'micro_dev'
-else:
-dev_type = dev_type.split()[0]
-if dev_type not in TVMContext.STR2MASK:
-raise ValueError("Unknown device type %s" % dev_type)
-dev_type = TVMContext.STR2MASK[dev_type]
-return TVMContext(dev_type, dev_id)
-
-
-def numpyasarray(np_data):
-"""Return a TVMArray representation of a numpy array.
-"""
-data = np_data
-assert data.flags['C_CONTIGUOUS']
-arr = TVMArray()
-shape = c_array(tvm_shape_index_t, data.shape)
-arr.data = data.ctypes.data_as(ctypes.c_void_p)
-arr.shape = shape
-arr.strides = None
-arr.dtype = TVMType(np.dtype(data.dtype).name)
-arr.ndim = data.ndim
-# CPU device
-arr.ctx = context(1, 0)
-return arr, shape
-
-
-def empty(shape, dtype="float32", ctx=context(1, 0)):
-"""Create an empty array given shape and device
-
-Parameters
---
-shape : tuple of int
-The shape of the array
-
-dtype : type or str
-The data type of the array.
-
-ctx : TVMContext
-The context of the array
-
-Returns
----
-arr : tvm.nd.NDArray
-The array tvm supported.
-"""
-shape = c_array(tvm_shape_index_t, shape)
-ndim = ctypes.c_int(len(shape))
-handle = TVMArrayHandle()
-dtype = TVMType(dtype)
-check_call(_LIB.TVMArrayAlloc(
-shape, ndim,
-ctypes.c_int(dtype.type_code),
-ctypes.c_int(dtype.bits),
-ctypes.c_int(dtype.lanes),
-ctx.device_type,
-ctx.device_id,
-ctypes.byref(handle)))
-return _make_array(handle, False, False)
+from tvm._ffi._ctypes.ndarray import _set_class_ndarray, _make_array,
_from_dlpack
+from tvm._ffi._ctypes.ndarray import NDArrayBase
-def from_dlpack(dltensor):
-"""Produce an array from a DLPack tensor without memory copy.
-Retreives the underlying DLPack tensor's pointer to create an array from
the
-data. Removes the original DLPack tensor's destructor as now the array is
-responsible for destruction.
+@tvm._ffi.register_object
+class NDArray(NDArrayBase):
+"""Lightweight NDArray class of TVM runtime.
Review comment:
Feel free to send a followup PR
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[GitHub] [incubator-tvm] jroesch commented on issue #4818: [REFACTOR][PY] Establish tvm.runtime
jroesch commented on issue #4818: [REFACTOR][PY] Establish tvm.runtime URL: https://github.com/apache/incubator-tvm/pull/4818#issuecomment-582656463 cc @robo-corg This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] jroesch commented on a change in pull request #4818: [REFACTOR][PY] Establish tvm.runtime
jroesch commented on a change in pull request #4818: [REFACTOR][PY] Establish
tvm.runtime
URL: https://github.com/apache/incubator-tvm/pull/4818#discussion_r375559169
##
File path: python/tvm/runtime/ndarray.py
##
@@ -18,132 +18,36 @@
"""Runtime NDArray api"""
import ctypes
import numpy as np
-from .base import _LIB, check_call, c_array, string_types, _FFI_MODE, c_str
-from .runtime_ctypes import TVMType, TVMContext, TVMArray, TVMArrayHandle
-from .runtime_ctypes import TypeCode, tvm_shape_index_t
+import tvm._ffi
+
+from tvm._ffi.base import _LIB, check_call, c_array, string_types, _FFI_MODE
+from tvm._ffi.runtime_ctypes import DataType, TVMContext, TVMArray,
TVMArrayHandle
+from tvm._ffi.runtime_ctypes import TypeCode, tvm_shape_index_t
try:
# pylint: disable=wrong-import-position
if _FFI_MODE == "ctypes":
raise ImportError()
-from ._cy3.core import _set_class_ndarray, _make_array, _from_dlpack
-from ._cy3.core import NDArrayBase as _NDArrayBase
+from tvm._ffi._cy3.core import _set_class_ndarray, _make_array,
_from_dlpack
+from tvm._ffi._cy3.core import NDArrayBase
except (RuntimeError, ImportError):
# pylint: disable=wrong-import-position
-from ._ctypes.ndarray import _set_class_ndarray, _make_array, _from_dlpack
-from ._ctypes.ndarray import NDArrayBase as _NDArrayBase
-
-
-def context(dev_type, dev_id=0):
-"""Construct a TVM context with given device type and id.
-
-Parameters
---
-dev_type: int or str
-The device type mask or name of the device.
-
-dev_id : int, optional
-The integer device id
-
-Returns
----
-ctx: TVMContext
-The corresponding context.
-
-Examples
-
-Context can be used to create reflection of context by
-string representation of the device type.
-
-.. code-block:: python
-
- assert tvm.context("cpu", 1) == tvm.cpu(1)
- assert tvm.context("gpu", 0) == tvm.gpu(0)
- assert tvm.context("cuda", 0) == tvm.gpu(0)
-"""
-if isinstance(dev_type, string_types):
-if '-device=micro_dev' in dev_type:
-dev_type = 'micro_dev'
-else:
-dev_type = dev_type.split()[0]
-if dev_type not in TVMContext.STR2MASK:
-raise ValueError("Unknown device type %s" % dev_type)
-dev_type = TVMContext.STR2MASK[dev_type]
-return TVMContext(dev_type, dev_id)
-
-
-def numpyasarray(np_data):
-"""Return a TVMArray representation of a numpy array.
-"""
-data = np_data
-assert data.flags['C_CONTIGUOUS']
-arr = TVMArray()
-shape = c_array(tvm_shape_index_t, data.shape)
-arr.data = data.ctypes.data_as(ctypes.c_void_p)
-arr.shape = shape
-arr.strides = None
-arr.dtype = TVMType(np.dtype(data.dtype).name)
-arr.ndim = data.ndim
-# CPU device
-arr.ctx = context(1, 0)
-return arr, shape
-
-
-def empty(shape, dtype="float32", ctx=context(1, 0)):
-"""Create an empty array given shape and device
-
-Parameters
---
-shape : tuple of int
-The shape of the array
-
-dtype : type or str
-The data type of the array.
-
-ctx : TVMContext
-The context of the array
-
-Returns
----
-arr : tvm.nd.NDArray
-The array tvm supported.
-"""
-shape = c_array(tvm_shape_index_t, shape)
-ndim = ctypes.c_int(len(shape))
-handle = TVMArrayHandle()
-dtype = TVMType(dtype)
-check_call(_LIB.TVMArrayAlloc(
-shape, ndim,
-ctypes.c_int(dtype.type_code),
-ctypes.c_int(dtype.bits),
-ctypes.c_int(dtype.lanes),
-ctx.device_type,
-ctx.device_id,
-ctypes.byref(handle)))
-return _make_array(handle, False, False)
+from tvm._ffi._ctypes.ndarray import _set_class_ndarray, _make_array,
_from_dlpack
+from tvm._ffi._ctypes.ndarray import NDArrayBase
-def from_dlpack(dltensor):
-"""Produce an array from a DLPack tensor without memory copy.
-Retreives the underlying DLPack tensor's pointer to create an array from
the
-data. Removes the original DLPack tensor's destructor as now the array is
-responsible for destruction.
+@tvm._ffi.register_object
+class NDArray(NDArrayBase):
+"""Lightweight NDArray class of TVM runtime.
Review comment:
I think the comments here could use some rewording.
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[GitHub] [incubator-tvm] masahi commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser
masahi commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#discussion_r37170 ## File path: python/tvm/relay/frontend/pytorch.py ## @@ -0,0 +1,1023 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# pylint: disable=import-self, too-many-lines, len-as-condition, no-else-return, unused-variable, too-many-nested-blocks +# pylint: disable=consider-iterating-dictionary, invalid-name, unused-argument, unused-variable, broad-except +"""PT: PyTorch frontend.""" +import numpy as np + +import tvm + +from .. import analysis as _analysis +from .. import expr as _expr +from .. import module as _module +from .. import op as _op +from .common import get_relay_op +from .common import infer_shape as _infer_shape + +__all__ = ["from_pytorch"] + +# operator implementation +def _elemwise(name): +def _impl(inputs, input_types): +# TODO: Figure out a better way to get typing to work for tensor + scalar +type0 = input_types[0] +if isinstance(inputs[1], _expr.Expr): +type0 = input_types[1] + +type1 = input_types[1] +if isinstance(inputs[0], _expr.Expr): +type1 = input_types[0] + +data0 = _convert_elemwise_input(inputs[0], type0) +data1 = _convert_elemwise_input(inputs[1], type1) + +return get_relay_op(name)(data0, data1) +return _impl + +def _unsqueeze(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +return _op.transform.expand_dims(data, int(axis), 1) +return _impl + +def _concatenate(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +if isinstance(data, _expr.Expr): +data = [data] + +return _op.tensor.concatenate(data, int(axis)) +return _impl + +def _slice(): +def _impl(inputs, input_types): +data = inputs[0] +strides = [] + +if isinstance(data, _expr.Expr): +inferred_shape = _infer_shape(data) +end = [] +for infer in inferred_shape: +end.append(int(infer)) +if isinstance(data, _expr.Var): +end = inferred_shape +end = list(end) +else: +end = data.shape + +begin = [0]*len(end) +dim = int(inputs[1]) +begin[dim] = int(inputs[2]) + +if isinstance(inputs[3], str) and inputs[3].isdigit(): +end[dim] = min(end[dim], int(inputs[3])) +else: +end[dim] = inputs[3] + +strides.append(int(inputs[4])) +return _op.transform.strided_slice(data, begin, end, strides) +return _impl + +def _select(): +def _impl(inputs, input_types): +data = inputs[0] +dim = int(inputs[1]) +index = int(inputs[2]) + +return _op.transform.take(data, _expr.const(index, dtype="int32"), axis=dim) +return _impl + +def _ones(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Expr): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(1), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _zeros(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Expr): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(0), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _relu(): +def _impl(inputs, input_types): +data = inputs[0] +return _op.nn.relu(data) +return _impl + +def _adaptive_avg_2d(): +def _impl(inputs, input_types): +data = inputs[0] +output_size = _infer_shape(inputs[1]) + +return _op.contrib.contrib.adaptive_avg_pool2d( +data, +output_size=output_size) +return _impl + +def _adaptive_max_2d(): +def _impl(inputs, input_types): +data = inputs[0] +output_size = _infer_shape(inputs[1]) + +return _op.contrib.contrib.adaptive_max_pool2d( +data, +
[GitHub] [incubator-tvm] masahi commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser
masahi commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#discussion_r37170 ## File path: python/tvm/relay/frontend/pytorch.py ## @@ -0,0 +1,1023 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# pylint: disable=import-self, too-many-lines, len-as-condition, no-else-return, unused-variable, too-many-nested-blocks +# pylint: disable=consider-iterating-dictionary, invalid-name, unused-argument, unused-variable, broad-except +"""PT: PyTorch frontend.""" +import numpy as np + +import tvm + +from .. import analysis as _analysis +from .. import expr as _expr +from .. import module as _module +from .. import op as _op +from .common import get_relay_op +from .common import infer_shape as _infer_shape + +__all__ = ["from_pytorch"] + +# operator implementation +def _elemwise(name): +def _impl(inputs, input_types): +# TODO: Figure out a better way to get typing to work for tensor + scalar +type0 = input_types[0] +if isinstance(inputs[1], _expr.Expr): +type0 = input_types[1] + +type1 = input_types[1] +if isinstance(inputs[0], _expr.Expr): +type1 = input_types[0] + +data0 = _convert_elemwise_input(inputs[0], type0) +data1 = _convert_elemwise_input(inputs[1], type1) + +return get_relay_op(name)(data0, data1) +return _impl + +def _unsqueeze(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +return _op.transform.expand_dims(data, int(axis), 1) +return _impl + +def _concatenate(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +if isinstance(data, _expr.Expr): +data = [data] + +return _op.tensor.concatenate(data, int(axis)) +return _impl + +def _slice(): +def _impl(inputs, input_types): +data = inputs[0] +strides = [] + +if isinstance(data, _expr.Expr): +inferred_shape = _infer_shape(data) +end = [] +for infer in inferred_shape: +end.append(int(infer)) +if isinstance(data, _expr.Var): +end = inferred_shape +end = list(end) +else: +end = data.shape + +begin = [0]*len(end) +dim = int(inputs[1]) +begin[dim] = int(inputs[2]) + +if isinstance(inputs[3], str) and inputs[3].isdigit(): +end[dim] = min(end[dim], int(inputs[3])) +else: +end[dim] = inputs[3] + +strides.append(int(inputs[4])) +return _op.transform.strided_slice(data, begin, end, strides) +return _impl + +def _select(): +def _impl(inputs, input_types): +data = inputs[0] +dim = int(inputs[1]) +index = int(inputs[2]) + +return _op.transform.take(data, _expr.const(index, dtype="int32"), axis=dim) +return _impl + +def _ones(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Expr): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(1), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _zeros(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Expr): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(0), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _relu(): +def _impl(inputs, input_types): +data = inputs[0] +return _op.nn.relu(data) +return _impl + +def _adaptive_avg_2d(): +def _impl(inputs, input_types): +data = inputs[0] +output_size = _infer_shape(inputs[1]) + +return _op.contrib.contrib.adaptive_avg_pool2d( +data, +output_size=output_size) +return _impl + +def _adaptive_max_2d(): +def _impl(inputs, input_types): +data = inputs[0] +output_size = _infer_shape(inputs[1]) + +return _op.contrib.contrib.adaptive_max_pool2d( +data, +
[GitHub] [incubator-tvm] alexwong commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser
alexwong commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#discussion_r375549738 ## File path: python/tvm/relay/frontend/pytorch.py ## @@ -0,0 +1,1023 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# pylint: disable=import-self, too-many-lines, len-as-condition, no-else-return, unused-variable, too-many-nested-blocks +# pylint: disable=consider-iterating-dictionary, invalid-name, unused-argument, unused-variable, broad-except +"""PT: PyTorch frontend.""" +import numpy as np + +import tvm + +from .. import analysis as _analysis +from .. import expr as _expr +from .. import module as _module +from .. import op as _op +from .common import get_relay_op +from .common import infer_shape as _infer_shape + +__all__ = ["from_pytorch"] + +# operator implementation +def _elemwise(name): +def _impl(inputs, input_types): +# TODO: Figure out a better way to get typing to work for tensor + scalar +type0 = input_types[0] +if isinstance(inputs[1], _expr.Expr): +type0 = input_types[1] + +type1 = input_types[1] +if isinstance(inputs[0], _expr.Expr): +type1 = input_types[0] + +data0 = _convert_elemwise_input(inputs[0], type0) +data1 = _convert_elemwise_input(inputs[1], type1) + +return get_relay_op(name)(data0, data1) +return _impl + +def _unsqueeze(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +return _op.transform.expand_dims(data, int(axis), 1) +return _impl + +def _concatenate(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +if isinstance(data, _expr.Expr): +data = [data] + +return _op.tensor.concatenate(data, int(axis)) +return _impl + +def _slice(): +def _impl(inputs, input_types): +data = inputs[0] +strides = [] + +if isinstance(data, _expr.Expr): +inferred_shape = _infer_shape(data) +end = [] +for infer in inferred_shape: +end.append(int(infer)) +if isinstance(data, _expr.Var): +end = inferred_shape +end = list(end) +else: +end = data.shape + +begin = [0]*len(end) +dim = int(inputs[1]) +begin[dim] = int(inputs[2]) + +if isinstance(inputs[3], str) and inputs[3].isdigit(): +end[dim] = min(end[dim], int(inputs[3])) +else: +end[dim] = inputs[3] + +strides.append(int(inputs[4])) +return _op.transform.strided_slice(data, begin, end, strides) +return _impl + +def _select(): +def _impl(inputs, input_types): +data = inputs[0] +dim = int(inputs[1]) +index = int(inputs[2]) + +return _op.transform.take(data, _expr.const(index, dtype="int32"), axis=dim) +return _impl + +def _ones(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Expr): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(1), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _zeros(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Expr): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(0), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _relu(): +def _impl(inputs, input_types): +data = inputs[0] +return _op.nn.relu(data) +return _impl + +def _adaptive_avg_2d(): +def _impl(inputs, input_types): +data = inputs[0] +output_size = _infer_shape(inputs[1]) + +return _op.contrib.contrib.adaptive_avg_pool2d( +data, +output_size=output_size) +return _impl + +def _adaptive_max_2d(): +def _impl(inputs, input_types): +data = inputs[0] +output_size = _infer_shape(inputs[1]) + +return _op.contrib.contrib.adaptive_max_pool2d( +data, +
[GitHub] [incubator-tvm] alexwong commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser
alexwong commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#discussion_r375549738 ## File path: python/tvm/relay/frontend/pytorch.py ## @@ -0,0 +1,1023 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# pylint: disable=import-self, too-many-lines, len-as-condition, no-else-return, unused-variable, too-many-nested-blocks +# pylint: disable=consider-iterating-dictionary, invalid-name, unused-argument, unused-variable, broad-except +"""PT: PyTorch frontend.""" +import numpy as np + +import tvm + +from .. import analysis as _analysis +from .. import expr as _expr +from .. import module as _module +from .. import op as _op +from .common import get_relay_op +from .common import infer_shape as _infer_shape + +__all__ = ["from_pytorch"] + +# operator implementation +def _elemwise(name): +def _impl(inputs, input_types): +# TODO: Figure out a better way to get typing to work for tensor + scalar +type0 = input_types[0] +if isinstance(inputs[1], _expr.Expr): +type0 = input_types[1] + +type1 = input_types[1] +if isinstance(inputs[0], _expr.Expr): +type1 = input_types[0] + +data0 = _convert_elemwise_input(inputs[0], type0) +data1 = _convert_elemwise_input(inputs[1], type1) + +return get_relay_op(name)(data0, data1) +return _impl + +def _unsqueeze(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +return _op.transform.expand_dims(data, int(axis), 1) +return _impl + +def _concatenate(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +if isinstance(data, _expr.Expr): +data = [data] + +return _op.tensor.concatenate(data, int(axis)) +return _impl + +def _slice(): +def _impl(inputs, input_types): +data = inputs[0] +strides = [] + +if isinstance(data, _expr.Expr): +inferred_shape = _infer_shape(data) +end = [] +for infer in inferred_shape: +end.append(int(infer)) +if isinstance(data, _expr.Var): +end = inferred_shape +end = list(end) +else: +end = data.shape + +begin = [0]*len(end) +dim = int(inputs[1]) +begin[dim] = int(inputs[2]) + +if isinstance(inputs[3], str) and inputs[3].isdigit(): +end[dim] = min(end[dim], int(inputs[3])) +else: +end[dim] = inputs[3] + +strides.append(int(inputs[4])) +return _op.transform.strided_slice(data, begin, end, strides) +return _impl + +def _select(): +def _impl(inputs, input_types): +data = inputs[0] +dim = int(inputs[1]) +index = int(inputs[2]) + +return _op.transform.take(data, _expr.const(index, dtype="int32"), axis=dim) +return _impl + +def _ones(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Expr): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(1), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _zeros(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Expr): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(0), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _relu(): +def _impl(inputs, input_types): +data = inputs[0] +return _op.nn.relu(data) +return _impl + +def _adaptive_avg_2d(): +def _impl(inputs, input_types): +data = inputs[0] +output_size = _infer_shape(inputs[1]) + +return _op.contrib.contrib.adaptive_avg_pool2d( +data, +output_size=output_size) +return _impl + +def _adaptive_max_2d(): +def _impl(inputs, input_types): +data = inputs[0] +output_size = _infer_shape(inputs[1]) + +return _op.contrib.contrib.adaptive_max_pool2d( +data, +
[GitHub] [incubator-tvm] alexwong commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser
alexwong commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#discussion_r375549738 ## File path: python/tvm/relay/frontend/pytorch.py ## @@ -0,0 +1,1023 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# pylint: disable=import-self, too-many-lines, len-as-condition, no-else-return, unused-variable, too-many-nested-blocks +# pylint: disable=consider-iterating-dictionary, invalid-name, unused-argument, unused-variable, broad-except +"""PT: PyTorch frontend.""" +import numpy as np + +import tvm + +from .. import analysis as _analysis +from .. import expr as _expr +from .. import module as _module +from .. import op as _op +from .common import get_relay_op +from .common import infer_shape as _infer_shape + +__all__ = ["from_pytorch"] + +# operator implementation +def _elemwise(name): +def _impl(inputs, input_types): +# TODO: Figure out a better way to get typing to work for tensor + scalar +type0 = input_types[0] +if isinstance(inputs[1], _expr.Expr): +type0 = input_types[1] + +type1 = input_types[1] +if isinstance(inputs[0], _expr.Expr): +type1 = input_types[0] + +data0 = _convert_elemwise_input(inputs[0], type0) +data1 = _convert_elemwise_input(inputs[1], type1) + +return get_relay_op(name)(data0, data1) +return _impl + +def _unsqueeze(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +return _op.transform.expand_dims(data, int(axis), 1) +return _impl + +def _concatenate(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +if isinstance(data, _expr.Expr): +data = [data] + +return _op.tensor.concatenate(data, int(axis)) +return _impl + +def _slice(): +def _impl(inputs, input_types): +data = inputs[0] +strides = [] + +if isinstance(data, _expr.Expr): +inferred_shape = _infer_shape(data) +end = [] +for infer in inferred_shape: +end.append(int(infer)) +if isinstance(data, _expr.Var): +end = inferred_shape +end = list(end) +else: +end = data.shape + +begin = [0]*len(end) +dim = int(inputs[1]) +begin[dim] = int(inputs[2]) + +if isinstance(inputs[3], str) and inputs[3].isdigit(): +end[dim] = min(end[dim], int(inputs[3])) +else: +end[dim] = inputs[3] + +strides.append(int(inputs[4])) +return _op.transform.strided_slice(data, begin, end, strides) +return _impl + +def _select(): +def _impl(inputs, input_types): +data = inputs[0] +dim = int(inputs[1]) +index = int(inputs[2]) + +return _op.transform.take(data, _expr.const(index, dtype="int32"), axis=dim) +return _impl + +def _ones(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Expr): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(1), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _zeros(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Expr): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(0), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _relu(): +def _impl(inputs, input_types): +data = inputs[0] +return _op.nn.relu(data) +return _impl + +def _adaptive_avg_2d(): +def _impl(inputs, input_types): +data = inputs[0] +output_size = _infer_shape(inputs[1]) + +return _op.contrib.contrib.adaptive_avg_pool2d( +data, +output_size=output_size) +return _impl + +def _adaptive_max_2d(): +def _impl(inputs, input_types): +data = inputs[0] +output_size = _infer_shape(inputs[1]) + +return _op.contrib.contrib.adaptive_max_pool2d( +data, +
[GitHub] [incubator-tvm] u99127 opened a new issue #4824: Tflite frontend needs to use zero point of input tensor while lowering qnn.conv2d for padding
u99127 opened a new issue #4824: Tflite frontend needs to use zero point of input tensor while lowering qnn.conv2d for padding URL: https://github.com/apache/incubator-tvm/issues/4824 The Tflite frontend ignores the zero point for input tensors when creating a separate pad operation while lowering quantized convolutions. This is fixed by PR #4807 and the tests broken by that are fixed #4816 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] anijain2305 commented on issue #4823: Improve tflite testing for quantized conv2d.
anijain2305 commented on issue #4823: Improve tflite testing for quantized conv2d. URL: https://github.com/apache/incubator-tvm/issues/4823#issuecomment-582641958 Thanks @u99127 for raising this. I will work in coming weeks to get the unit coverage better. @FrozenGene might also be interested in this. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] u99127 opened a new issue #4823: Improve tflite testing for quantized conv2d.
u99127 opened a new issue #4823: Improve tflite testing for quantized conv2d. URL: https://github.com/apache/incubator-tvm/issues/4823 @anijain2305 , @inadob While we have tests for qnn.conv2d at the relay level, the recent issue with padding highlighted by the pull request #4807 has highlighted the need for better unit tests for quantized conv2d in the tflite frontend. One of the reasons that we hit this issue in the tflite frontend is that the relay unit tests for qnn.conv2d considered an implicit padding while the tflite frontend lowers the padding in quantized conv2d into a separate relay operation. Thus we need to have a unit test for qnn.conv2d for the tflite frontend. Further it's probably worth a separate audit to make sure we have adequate tests for qnn ops in the tflite frontend for those operators that are not added. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] alexwong commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser
alexwong commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#discussion_r375519693 ## File path: python/tvm/relay/frontend/pytorch.py ## @@ -0,0 +1,1093 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# pylint: disable=import-self, too-many-lines, len-as-condition, no-else-return, unused-variable, too-many-nested-blocks +# pylint: disable=consider-iterating-dictionary, invalid-name, unused-argument, unused-variable, broad-except +"""PT: PyTorch frontend.""" +import numpy as np + +import tvm + +from .. import analysis as _analysis +from .. import expr as _expr +from .. import module as _module +from .. import op as _op +from .common import get_relay_op +from .common import infer_shape as _infer_shape + +__all__ = ['from_pytorch'] + +# operator implementation +def _elemwise(name): +def _impl(inputs, input_types): +# TODO: Figure out a better way to get typing to work for tensor + scalar +type0 = input_types[0] +if isinstance(inputs[1], (_expr.Call, _expr.TupleGetItem, _expr.Var)): +type0 = input_types[1] + +type1 = input_types[1] +if isinstance(inputs[0], (_expr.Call, _expr.TupleGetItem, _expr.Var)): +type1 = input_types[0] + +data0 = _convert_elemwise_input(inputs[0], type0) +data1 = _convert_elemwise_input(inputs[1], type1) + +return get_relay_op(name)(data0, data1) +return _impl + +def _unsqueeze(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +return _op.transform.expand_dims(data, int(axis), 1) +return _impl + +def _concatenate(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +if isinstance(data, (_expr.Call, _expr.TupleGetItem, _expr.Var)): +data = [data] + +return _op.tensor.concatenate(data, int(axis)) +return _impl + +def _slice(): +def _impl(inputs, input_types): +data = inputs[0] +strides = [] + +inferred_shape = _infer_shape(data) +end = [] +for infer in inferred_shape: +end.append(int(infer)) +if isinstance(data, _expr.Var): +end = _infer_shape(data) +end = list(end) + +begin = [0]*len(end) +dim = int(inputs[1]) +begin[dim] = int(inputs[2]) + +if isinstance(inputs[3], str) and inputs[3].isdigit(): +end[dim] = min(end[dim], int(inputs[3])) +else: +end[dim] = inputs[3] + +strides.append(int(inputs[4])) +return _op.transform.strided_slice(data, begin, end, strides) +return _impl + +def _select(): +def _impl(inputs, input_types): +data = inputs[0] +dim = int(inputs[1]) +index = int(inputs[2]) + +return _op.transform.take(data, _expr.const(index, dtype='int32'), axis=dim) +return _impl + +def _ones(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Var): +shape = _infer_shape(inputs[0]) +elif isinstance(inputs[0], (_expr.Call, _expr.TupleGetItem)): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(1), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _zeros(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Var): +shape = _infer_shape(inputs[0]) +elif isinstance(inputs[0], (_expr.Call, _expr.TupleGetItem)): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(0), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _relu(): +def _impl(inputs, input_types): +data = inputs[0] +return _op.nn.relu(data) +return _impl + +def _adaptive_avg_2d(): +def _impl(inputs, input_types): +data = inputs[0] +output_size = _infer_shape(inputs[1]) + +return _op.contrib.contrib.adaptive_avg_pool2d( +data, +output_size=output_size) +return _impl
[GitHub] [incubator-tvm] u99127 commented on a change in pull request #4822: [Frontend][TFLite] Add MIRROR_PAD operator
u99127 commented on a change in pull request #4822: [Frontend][TFLite] Add
MIRROR_PAD operator
URL: https://github.com/apache/incubator-tvm/pull/4822#discussion_r375518286
##
File path: python/tvm/relay/frontend/tflite.py
##
@@ -1422,10 +1423,48 @@ def convert_pad(self, op):
# convert list of lists to tuple of tuples
paddings = tuple(tuple(l) for l in pad_list)
-# Use default pad_value 0 because TFLite does not support
constant_values parameter
+# Use default pad_value 0 because TFLite PAD does not support
constant_values parameter
out = _op.nn.pad(in_expr, paddings)
return out
+def convert_mirror_pad(self, op):
+"""Convert TFLite MIRROR_PAD"""
+try:
+from tflite.Operator import Operator
+from tflite.BuiltinOptions import BuiltinOptions
+from tflite.MirrorPadOptions import MirrorPadOptions
+except ImportError:
+raise ImportError("The tflite package must be installed")
+
+# the quantized form MirrorPad is not yet implemented in TFLite.
+if self.is_quantized(op):
+raise tvm.error.OpNotImplemented(
+'TFlite quantized MIRROR_PAD operator is not supported yet.')
Review comment:
Pedantry : A not implemented error suggests to me as a user that the support
exists in the tflite tooling but the TVM stack has not yet implemented the
support i.e. the tflite tooling can't yet produce the mirror_pad operation, do
we need a different notification here actually asking the user to report an
issue asking for a feature to be added ?
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[GitHub] [incubator-tvm] u99127 commented on a change in pull request #4822: [Frontend][TFLite] Add MIRROR_PAD operator
u99127 commented on a change in pull request #4822: [Frontend][TFLite] Add
MIRROR_PAD operator
URL: https://github.com/apache/incubator-tvm/pull/4822#discussion_r375516566
##
File path: python/tvm/relay/frontend/tflite.py
##
@@ -1422,10 +1423,48 @@ def convert_pad(self, op):
# convert list of lists to tuple of tuples
paddings = tuple(tuple(l) for l in pad_list)
-# Use default pad_value 0 because TFLite does not support
constant_values parameter
+# Use default pad_value 0 because TFLite PAD does not support
constant_values parameter
out = _op.nn.pad(in_expr, paddings)
return out
+def convert_mirror_pad(self, op):
+"""Convert TFLite MIRROR_PAD"""
+try:
+from tflite.Operator import Operator
+from tflite.BuiltinOptions import BuiltinOptions
+from tflite.MirrorPadOptions import MirrorPadOptions
+except ImportError:
+raise ImportError("The tflite package must be installed")
+
+# the quantized form MirrorPad is not yet implemented in TFLite.
+if self.is_quantized(op):
+raise tvm.error.OpNotImplemented(
Review comment:
For future reference,
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/mlir/lite/ir/tfl_ops.td#L2803
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[GitHub] [incubator-tvm] alexwong edited a comment on issue #4497: [Relay] Add a PyTorch to Relay Parser
alexwong edited a comment on issue #4497: [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#issuecomment-582619862 > @alexwong please address my comment at https://github.com/apache/incubator-tvm/pull/4497/files#r370911013 > > Add `with torch.no_grad()` and remove `detach()` there. Also calling `eval()` once on your torch module is a good idea. I've seen some issues if I omit `eval()` and do forward. Resolved, eval() is called in load_torchvision or the specific single op function. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] alexwong commented on issue #4497: [Relay] Add a PyTorch to Relay Parser
alexwong commented on issue #4497: [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#issuecomment-582619862 > @alexwong please address my comment at https://github.com/apache/incubator-tvm/pull/4497/files#r370911013 > > Add `with torch.no_grad()` and remove `detach()` there. Also calling `eval()` once on your torch module is a good idea. I've seen some issues if I omit `eval()` and do forward. Addressed, eval() is called in load_torchvision or the specific single op function. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] jroesch commented on issue #4815: [TOPI][Relay] Add bitwise ops
jroesch commented on issue #4815: [TOPI][Relay] Add bitwise ops URL: https://github.com/apache/incubator-tvm/pull/4815#issuecomment-582618898 cc @jwfromm can you review this? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] alexwong commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser
alexwong commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#discussion_r375513751 ## File path: python/tvm/relay/frontend/pytorch.py ## @@ -0,0 +1,1093 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# pylint: disable=import-self, too-many-lines, len-as-condition, no-else-return, unused-variable, too-many-nested-blocks +# pylint: disable=consider-iterating-dictionary, invalid-name, unused-argument, unused-variable, broad-except +"""PT: PyTorch frontend.""" +import numpy as np + +import tvm + +from .. import analysis as _analysis +from .. import expr as _expr +from .. import module as _module +from .. import op as _op +from .common import get_relay_op +from .common import infer_shape as _infer_shape + +__all__ = ['from_pytorch'] + +# operator implementation +def _elemwise(name): +def _impl(inputs, input_types): +# TODO: Figure out a better way to get typing to work for tensor + scalar +type0 = input_types[0] +if isinstance(inputs[1], (_expr.Call, _expr.TupleGetItem, _expr.Var)): +type0 = input_types[1] + +type1 = input_types[1] +if isinstance(inputs[0], (_expr.Call, _expr.TupleGetItem, _expr.Var)): +type1 = input_types[0] + +data0 = _convert_elemwise_input(inputs[0], type0) +data1 = _convert_elemwise_input(inputs[1], type1) + +return get_relay_op(name)(data0, data1) +return _impl + +def _unsqueeze(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +return _op.transform.expand_dims(data, int(axis), 1) +return _impl + +def _concatenate(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +if isinstance(data, (_expr.Call, _expr.TupleGetItem, _expr.Var)): +data = [data] + +return _op.tensor.concatenate(data, int(axis)) +return _impl + +def _slice(): +def _impl(inputs, input_types): +data = inputs[0] +strides = [] + +inferred_shape = _infer_shape(data) +end = [] +for infer in inferred_shape: +end.append(int(infer)) +if isinstance(data, _expr.Var): +end = _infer_shape(data) +end = list(end) + +begin = [0]*len(end) +dim = int(inputs[1]) +begin[dim] = int(inputs[2]) + +if isinstance(inputs[3], str) and inputs[3].isdigit(): +end[dim] = min(end[dim], int(inputs[3])) +else: +end[dim] = inputs[3] + +strides.append(int(inputs[4])) +return _op.transform.strided_slice(data, begin, end, strides) +return _impl + +def _select(): +def _impl(inputs, input_types): +data = inputs[0] +dim = int(inputs[1]) +index = int(inputs[2]) + +return _op.transform.take(data, _expr.const(index, dtype='int32'), axis=dim) +return _impl + +def _ones(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Var): +shape = _infer_shape(inputs[0]) +elif isinstance(inputs[0], (_expr.Call, _expr.TupleGetItem)): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(1), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _zeros(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Var): +shape = _infer_shape(inputs[0]) +elif isinstance(inputs[0], (_expr.Call, _expr.TupleGetItem)): +shape = _infer_shape(inputs[0]) +else: +shape = inputs[0].shape + +return _op.full(_expr.const(0), shape, dtype=_convert_data_type(input_types[0])) +return _impl + +def _relu(): +def _impl(inputs, input_types): +data = inputs[0] +return _op.nn.relu(data) +return _impl + +def _adaptive_avg_2d(): +def _impl(inputs, input_types): +data = inputs[0] +output_size = _infer_shape(inputs[1]) + +return _op.contrib.contrib.adaptive_avg_pool2d( +data, +output_size=output_size) +return _impl
[GitHub] [incubator-tvm] alexwong commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser
alexwong commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#discussion_r375513313 ## File path: python/tvm/relay/frontend/pytorch.py ## @@ -0,0 +1,1093 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# pylint: disable=import-self, too-many-lines, len-as-condition, no-else-return, unused-variable, too-many-nested-blocks +# pylint: disable=consider-iterating-dictionary, invalid-name, unused-argument, unused-variable, broad-except +"""PT: PyTorch frontend.""" +import numpy as np + +import tvm + +from .. import analysis as _analysis +from .. import expr as _expr +from .. import module as _module +from .. import op as _op +from .common import get_relay_op +from .common import infer_shape as _infer_shape + +__all__ = ['from_pytorch'] + +# operator implementation +def _elemwise(name): +def _impl(inputs, input_types): +# TODO: Figure out a better way to get typing to work for tensor + scalar +type0 = input_types[0] +if isinstance(inputs[1], (_expr.Call, _expr.TupleGetItem, _expr.Var)): +type0 = input_types[1] + +type1 = input_types[1] +if isinstance(inputs[0], (_expr.Call, _expr.TupleGetItem, _expr.Var)): +type1 = input_types[0] + +data0 = _convert_elemwise_input(inputs[0], type0) +data1 = _convert_elemwise_input(inputs[1], type1) + +return get_relay_op(name)(data0, data1) +return _impl + +def _unsqueeze(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +return _op.transform.expand_dims(data, int(axis), 1) +return _impl + +def _concatenate(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +if isinstance(data, (_expr.Call, _expr.TupleGetItem, _expr.Var)): Review comment: Previously was handling constants differently. Moved to just catching any expr now. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] alexwong commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser
alexwong commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#discussion_r375513469 ## File path: python/tvm/relay/frontend/pytorch.py ## @@ -0,0 +1,1093 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# pylint: disable=import-self, too-many-lines, len-as-condition, no-else-return, unused-variable, too-many-nested-blocks +# pylint: disable=consider-iterating-dictionary, invalid-name, unused-argument, unused-variable, broad-except +"""PT: PyTorch frontend.""" +import numpy as np + +import tvm + +from .. import analysis as _analysis +from .. import expr as _expr +from .. import module as _module +from .. import op as _op +from .common import get_relay_op +from .common import infer_shape as _infer_shape + +__all__ = ['from_pytorch'] + +# operator implementation +def _elemwise(name): +def _impl(inputs, input_types): +# TODO: Figure out a better way to get typing to work for tensor + scalar +type0 = input_types[0] +if isinstance(inputs[1], (_expr.Call, _expr.TupleGetItem, _expr.Var)): +type0 = input_types[1] + +type1 = input_types[1] +if isinstance(inputs[0], (_expr.Call, _expr.TupleGetItem, _expr.Var)): +type1 = input_types[0] + +data0 = _convert_elemwise_input(inputs[0], type0) +data1 = _convert_elemwise_input(inputs[1], type1) + +return get_relay_op(name)(data0, data1) +return _impl + +def _unsqueeze(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +return _op.transform.expand_dims(data, int(axis), 1) +return _impl + +def _concatenate(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +if isinstance(data, (_expr.Call, _expr.TupleGetItem, _expr.Var)): +data = [data] + +return _op.tensor.concatenate(data, int(axis)) +return _impl + +def _slice(): +def _impl(inputs, input_types): +data = inputs[0] +strides = [] + +inferred_shape = _infer_shape(data) +end = [] +for infer in inferred_shape: +end.append(int(infer)) +if isinstance(data, _expr.Var): Review comment: Change to use existing inferred shape. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] alexwong commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser
alexwong commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#discussion_r375513644 ## File path: python/tvm/relay/frontend/pytorch.py ## @@ -0,0 +1,1093 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# pylint: disable=import-self, too-many-lines, len-as-condition, no-else-return, unused-variable, too-many-nested-blocks +# pylint: disable=consider-iterating-dictionary, invalid-name, unused-argument, unused-variable, broad-except +"""PT: PyTorch frontend.""" +import numpy as np + +import tvm + +from .. import analysis as _analysis +from .. import expr as _expr +from .. import module as _module +from .. import op as _op +from .common import get_relay_op +from .common import infer_shape as _infer_shape + +__all__ = ['from_pytorch'] + +# operator implementation +def _elemwise(name): +def _impl(inputs, input_types): +# TODO: Figure out a better way to get typing to work for tensor + scalar +type0 = input_types[0] +if isinstance(inputs[1], (_expr.Call, _expr.TupleGetItem, _expr.Var)): +type0 = input_types[1] + +type1 = input_types[1] +if isinstance(inputs[0], (_expr.Call, _expr.TupleGetItem, _expr.Var)): +type1 = input_types[0] + +data0 = _convert_elemwise_input(inputs[0], type0) +data1 = _convert_elemwise_input(inputs[1], type1) + +return get_relay_op(name)(data0, data1) +return _impl + +def _unsqueeze(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +return _op.transform.expand_dims(data, int(axis), 1) +return _impl + +def _concatenate(): +def _impl(inputs, input_types): +data = inputs[0] +axis = inputs[1] + +if isinstance(data, (_expr.Call, _expr.TupleGetItem, _expr.Var)): +data = [data] + +return _op.tensor.concatenate(data, int(axis)) +return _impl + +def _slice(): +def _impl(inputs, input_types): +data = inputs[0] +strides = [] + +inferred_shape = _infer_shape(data) +end = [] +for infer in inferred_shape: +end.append(int(infer)) +if isinstance(data, _expr.Var): +end = _infer_shape(data) +end = list(end) + +begin = [0]*len(end) +dim = int(inputs[1]) +begin[dim] = int(inputs[2]) + +if isinstance(inputs[3], str) and inputs[3].isdigit(): +end[dim] = min(end[dim], int(inputs[3])) +else: +end[dim] = inputs[3] + +strides.append(int(inputs[4])) +return _op.transform.strided_slice(data, begin, end, strides) +return _impl + +def _select(): +def _impl(inputs, input_types): +data = inputs[0] +dim = int(inputs[1]) +index = int(inputs[2]) + +return _op.transform.take(data, _expr.const(index, dtype='int32'), axis=dim) +return _impl + +def _ones(): +def _impl(inputs, input_types): +if isinstance(inputs[0], _expr.Var): Review comment: Moved together. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] alexwong commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser
alexwong commented on a change in pull request #4497: [Relay] Add a PyTorch to
Relay Parser
URL: https://github.com/apache/incubator-tvm/pull/4497#discussion_r375512544
##
File path: tests/python/frontend/pytorch/test_forward.py
##
@@ -0,0 +1,851 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=import-self, invalid-name, unused-argument
+"""Unit tests for various models and operators"""
+from time import time
+import os
+import sys
+from tempfile import TemporaryDirectory
+from scipy.stats import t as tdistr
+import numpy as np
+import torch
+from torch.nn import Module
+import tvm
+import torchvision
+
+from tvm import relay
+from tvm.contrib import graph_runtime
+from tvm.relay.testing.config import ctx_list
+
+sys.setrecursionlimit(1)
+
+def _vectorize(ten):
+return ten.reshape(-1)
+
+def atol(tru, est):
+def _atol_elt(tru, est):
+return abs(tru - est)
+tru = _vectorize(tru)
+est = _vectorize(est)
+return max([_atol_elt(x, y) for x, y in zip(tru, est)])
+
+def rtol(tru, est):
+def _rtol_elt(tru, est):
+return abs(tru - est) / min(abs(tru), abs(est))
+tru = _vectorize(tru)
+est = _vectorize(est)
+return max([_rtol_elt(x, y) for x, y in zip(tru, est)])
+
+def assert_shapes_match(tru, est):
+if tru.shape != est.shape:
+msg = "Output shapes {} and {} don't match"
+raise AssertionError(msg.format(tru.shape, est.shape))
+
+def load_torchvision(model_name):
+"""Given a model name, returns a Torchvision model in eval mode as well
+as an example input."""
+with torch.no_grad():
+if model_name.startswith('inception'):
+height = width = 299
+mean = [0.5, 0.5, 0.5]
+std = [0.5, 0.5, 0.5]
+else:
+height = width = 224
+mean = [0.485, 0.456, 0.406]
+std = [0.229, 0.224, 0.225]
+input_shape = [1, 3, height, width]
+input_data = torch.randn(input_shape).float()
+for channel in range(3):
+input_data[:, channel] -= mean[channel]
+input_data[:, channel] /= std[channel]
+model = getattr(torchvision.models, model_name)(pretrained=True)
+model = model.float().eval()
+return model, input_data
+
+def load_pretrainedmodels(model_name):
+"""Given a model name, returns a pretrainedmodels.pytorch model in eval
+mode as well as an example input."""
+import pretrainedmodels #
https://github.com/Cadene/pretrained-models.pytorch
+model = getattr(pretrainedmodels, model_name)().float().eval()
+input_shape = [1, *model.input_size]
+input_data = torch.rand(input_shape).float() * 256
+for channel in range(3):
+input_data[:, channel] -= model.mean[channel]
+input_data[:, channel] /= model.std[channel]
+return model, input_data
+
+def load_model(model_name):
+"""Given a model name, returns a model as well as an example input."""
+if hasattr(torchvision.models, model_name):
+return load_torchvision(model_name)
+try:
+if hasattr(pretrainedmodels, model_name):
+return load_pretrainedmodels(model_name)
+except ModuleNotFoundError:
+raise ModuleNotFoundError('Please install pretrainedmodels.pytorch')
+raise RuntimeError('Model not supported')
+
+
+def confidence_interval(mean, stdev, count, alpha=.01):
+"""Returns the lower and upper bounds of the confidence interval of a
random
+variable. Confidence is 1 - alpha (default confidence is 99%)."""
+stdval = tdistr.ppf(1 - alpha / 2, count - 1)
+lower, upper = mean + np.array([-1, 1]) * stdval * stdev / np.sqrt(count)
+return lower, upper
+
+def measure_latency(model, input_shapes, output_shapes, thresh, dryruns=40):
+"""Compute the latency of the given model"""
+latencies = []
+count = 0
+while True:
+if isinstance(model, torch.nn.Module):
+input_data = [torch.rand(shape).float() for shape in input_shapes]
+if torch.cuda.is_available():
+input_data = list(map(lambda x: x.cuda(), input_data))
+model = model.cuda()
+t_start = time()
+model(*input_d
[GitHub] [incubator-tvm] masahi commented on issue #4497: [Relay] Add a PyTorch to Relay Parser
masahi commented on issue #4497: [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#issuecomment-582598505 @tqchen we should probably finish the docker update in https://github.com/apache/incubator-tvm/pull/4756 before we merge this PR. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] masahi commented on a change in pull request #4497: [Relay] Add a PyTorch to Relay Parser
masahi commented on a change in pull request #4497: [Relay] Add a PyTorch to
Relay Parser
URL: https://github.com/apache/incubator-tvm/pull/4497#discussion_r375484453
##
File path: tests/python/frontend/pytorch/test_forward.py
##
@@ -0,0 +1,851 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=import-self, invalid-name, unused-argument
+"""Unit tests for various models and operators"""
+from time import time
+import os
+import sys
+from tempfile import TemporaryDirectory
+from scipy.stats import t as tdistr
+import numpy as np
+import torch
+from torch.nn import Module
+import tvm
+import torchvision
+
+from tvm import relay
+from tvm.contrib import graph_runtime
+from tvm.relay.testing.config import ctx_list
+
+sys.setrecursionlimit(1)
+
+def _vectorize(ten):
+return ten.reshape(-1)
+
+def atol(tru, est):
+def _atol_elt(tru, est):
+return abs(tru - est)
+tru = _vectorize(tru)
+est = _vectorize(est)
+return max([_atol_elt(x, y) for x, y in zip(tru, est)])
+
+def rtol(tru, est):
+def _rtol_elt(tru, est):
+return abs(tru - est) / min(abs(tru), abs(est))
+tru = _vectorize(tru)
+est = _vectorize(est)
+return max([_rtol_elt(x, y) for x, y in zip(tru, est)])
+
+def assert_shapes_match(tru, est):
+if tru.shape != est.shape:
+msg = "Output shapes {} and {} don't match"
+raise AssertionError(msg.format(tru.shape, est.shape))
+
+def load_torchvision(model_name):
+"""Given a model name, returns a Torchvision model in eval mode as well
+as an example input."""
+with torch.no_grad():
+if model_name.startswith('inception'):
+height = width = 299
+mean = [0.5, 0.5, 0.5]
+std = [0.5, 0.5, 0.5]
+else:
+height = width = 224
+mean = [0.485, 0.456, 0.406]
+std = [0.229, 0.224, 0.225]
+input_shape = [1, 3, height, width]
+input_data = torch.randn(input_shape).float()
+for channel in range(3):
+input_data[:, channel] -= mean[channel]
+input_data[:, channel] /= std[channel]
+model = getattr(torchvision.models, model_name)(pretrained=True)
+model = model.float().eval()
+return model, input_data
+
+def load_pretrainedmodels(model_name):
+"""Given a model name, returns a pretrainedmodels.pytorch model in eval
+mode as well as an example input."""
+import pretrainedmodels #
https://github.com/Cadene/pretrained-models.pytorch
+model = getattr(pretrainedmodels, model_name)().float().eval()
+input_shape = [1, *model.input_size]
+input_data = torch.rand(input_shape).float() * 256
+for channel in range(3):
+input_data[:, channel] -= model.mean[channel]
+input_data[:, channel] /= model.std[channel]
+return model, input_data
+
+def load_model(model_name):
+"""Given a model name, returns a model as well as an example input."""
+if hasattr(torchvision.models, model_name):
+return load_torchvision(model_name)
+try:
+if hasattr(pretrainedmodels, model_name):
+return load_pretrainedmodels(model_name)
+except ModuleNotFoundError:
+raise ModuleNotFoundError('Please install pretrainedmodels.pytorch')
+raise RuntimeError('Model not supported')
+
+
+def confidence_interval(mean, stdev, count, alpha=.01):
+"""Returns the lower and upper bounds of the confidence interval of a
random
+variable. Confidence is 1 - alpha (default confidence is 99%)."""
+stdval = tdistr.ppf(1 - alpha / 2, count - 1)
+lower, upper = mean + np.array([-1, 1]) * stdval * stdev / np.sqrt(count)
+return lower, upper
+
+def measure_latency(model, input_shapes, output_shapes, thresh, dryruns=40):
+"""Compute the latency of the given model"""
+latencies = []
+count = 0
+while True:
+if isinstance(model, torch.nn.Module):
+input_data = [torch.rand(shape).float() for shape in input_shapes]
+if torch.cuda.is_available():
+input_data = list(map(lambda x: x.cuda(), input_data))
+model = model.cuda()
+t_start = time()
+model(*input_dat
[GitHub] [incubator-tvm] anijain2305 commented on issue #4807: [Frontend][TFLite] Fix quantized pad value for convolution
anijain2305 commented on issue #4807: [Frontend][TFLite] Fix quantized pad value for convolution URL: https://github.com/apache/incubator-tvm/pull/4807#issuecomment-582591219 @inadob You can close this one. #4816 covers this. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated (23f3988 -> 79ce87f)
This is an automated email from the ASF dual-hosted git repository. liuyizhi pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 23f3988 [QNN] Optimize lowering for requantize and FixedPointMultiply. (#4798) add 79ce87f [Relay][Frontend][TFLite] Add parser support for logical operators (#4642) No new revisions were added by this update. Summary of changes: python/tvm/relay/frontend/tflite.py | 34 tests/python/frontend/tflite/test_forward.py | 31 + 2 files changed, 65 insertions(+)
[GitHub] [incubator-tvm] yzhliu commented on issue #4642: [Relay][Frontend][TFLite] Add parser support for logical operators
yzhliu commented on issue #4642: [Relay][Frontend][TFLite] Add parser support for logical operators URL: https://github.com/apache/incubator-tvm/pull/4642#issuecomment-582590768 Thanks @inadob @anijain2305 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] yzhliu merged pull request #4642: [Relay][Frontend][TFLite] Add parser support for logical operators
yzhliu merged pull request #4642: [Relay][Frontend][TFLite] Add parser support for logical operators URL: https://github.com/apache/incubator-tvm/pull/4642 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] anijain2305 commented on issue #4696: [Relay][Frontend][TFlite] Add support for quantized LOGISTIC
anijain2305 commented on issue #4696: [Relay][Frontend][TFlite] Add support for quantized LOGISTIC URL: https://github.com/apache/incubator-tvm/pull/4696#issuecomment-582584426 @inadob Please rebase This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated (2989d72 -> 23f3988)
This is an automated email from the ASF dual-hosted git repository. wuwei pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 2989d72 [Frontend][TFLite] Dynamically calculate input_stats of any fake_quant range (#4789) add 23f3988 [QNN] Optimize lowering for requantize and FixedPointMultiply. (#4798) No new revisions were added by this update. Summary of changes: src/relay/qnn/op/requantize.cc | 20 +--- src/relay/qnn/util.cc| 10 +++--- tests/python/relay/test_op_qnn_requantize.py | 15 +++ 3 files changed, 39 insertions(+), 6 deletions(-)
[GitHub] [incubator-tvm] vinx13 commented on issue #4798: [QNN] Optimize lowering for requantize and FixedPointMultiply.
vinx13 commented on issue #4798: [QNN] Optimize lowering for requantize and FixedPointMultiply. URL: https://github.com/apache/incubator-tvm/pull/4798#issuecomment-582582122 Thanks @anijain2305 @jackwish this is merged This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] vinx13 merged pull request #4798: [QNN] Optimize lowering for requantize and FixedPointMultiply.
vinx13 merged pull request #4798: [QNN] Optimize lowering for requantize and FixedPointMultiply. URL: https://github.com/apache/incubator-tvm/pull/4798 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] kevinthesun commented on issue #4789: [Frontend][TFLite] Dynamically calculate input_stats of any fake_quant range
kevinthesun commented on issue #4789: [Frontend][TFLite] Dynamically calculate input_stats of any fake_quant range URL: https://github.com/apache/incubator-tvm/pull/4789#issuecomment-582582023 Thanks @inadob @wyc-ruiker @anijain2305 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] kevinthesun merged pull request #4789: [Frontend][TFLite] Dynamically calculate input_stats of any fake_quant range
kevinthesun merged pull request #4789: [Frontend][TFLite] Dynamically calculate input_stats of any fake_quant range URL: https://github.com/apache/incubator-tvm/pull/4789 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated (019356f -> 2989d72)
This is an automated email from the ASF dual-hosted git repository. kevinthesun pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 019356f Fixed subprocess creation under windows (#4820) add 2989d72 [Frontend][TFLite] Dynamically calculate input_stats of any fake_quant range (#4789) No new revisions were added by this update. Summary of changes: tests/python/frontend/tflite/test_forward.py | 43 +--- 1 file changed, 27 insertions(+), 16 deletions(-)
[GitHub] [incubator-tvm] anijain2305 commented on issue #4798: [QNN] Optimize lowering for requantize and FixedPointMultiply.
anijain2305 commented on issue #4798: [QNN] Optimize lowering for requantize and FixedPointMultiply. URL: https://github.com/apache/incubator-tvm/pull/4798#issuecomment-582581737 Ping This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
