def test_linear_multiple_consecutive_cast_ops(self): @mb.program(input_specs=[mb.TensorSpec(shape=(10, 20))]) def prog(x): x = mb.cast(x=x, dtype="fp16") x = mb.cast(x=x, dtype="fp16") x = mb.cast(x=x, dtype="int32") x = mb.cast(x=x, dtype="int64") x = mb.cast(x=x, dtype="fp32") x = mb.cast(x=x, dtype="fp16") x = mb.square(x=x) return x self.assertEqual( get_op_types_in_program(prog), ['cast', 'cast', 'cast', 'cast', 'cast', 'cast', 'square']) apply_pass_and_basic_check(prog, "common::cast_optimization") _, _, block = apply_pass_and_basic_check( prog, "common::dead_code_elimination") self.assertEqual(get_op_types_in_program(prog), ["cast", "square"]) self.assertEqual(block.find_ops(op_type="cast")[0].dtype.val, "fp16") assert_model_is_valid( prog, {"x": (10, 20)}, expected_output_shapes={block.outputs[0].name: (10, 20)}, )
def test_move_multiple_uses_overlapping(self): ''' Input graph: x (input) ---> cast ---> cast (output) | |-------> transpose ---> transpose (output) ''' @mb.program(input_specs=[mb.TensorSpec(shape=(10, 20))]) def prog(x): x1 = mb.cast(x=x, dtype="fp16") x2 = mb.cast(x=x1, dtype="fp32") x3 = mb.transpose(x=x1, perm=[1, 0]) x4 = mb.transpose(x=x3, perm=[1, 0]) return x2, x4 assert get_op_types_in_program(prog) == ['cast', 'cast', 'transpose', 'transpose'] apply_pass_and_basic_check(prog, "common::topological_reorder") _, _, block = apply_pass_and_basic_check(prog, "common::dead_code_elimination") assert get_op_types_in_program(prog) == ['cast', 'transpose', 'transpose', 'cast'] assert_model_is_valid( prog, {"x": (10, 20)}, expected_output_shapes={ block.outputs[0].name: (10, 20), block.outputs[1].name: (10, 20) }, )
def test_consecutive_fusable_casts_on_all_branches(self): @mb.program(input_specs=[mb.TensorSpec(shape=(10, 20))]) def prog(x): x = mb.cast(x=x, dtype="int32") x1 = mb.cast(x=x, dtype="fp16") x2 = mb.cast(x=x, dtype="fp16") x3 = mb.cast(x=x, dtype="fp16") x4 = mb.square(x=x1) x5 = mb.relu(x=x2) x6 = mb.log(x=x3) return x4, x5, x6 self.assertEqual( get_op_types_in_program(prog), ['cast', 'cast', 'cast', 'cast', 'square', 'relu', 'log']) apply_pass_and_basic_check(prog, "common::cast_optimization") _, _, block = apply_pass_and_basic_check( prog, "common::dead_code_elimination") self.assertEqual(get_op_types_in_program(prog), ["cast", "square", "relu", "log"]) self.assertEqual(block.find_ops(op_type="cast")[0].dtype.val, "fp16") assert_model_is_valid( prog, {"x": (10, 20)}, expected_output_shapes={ block.outputs[0].name: (10, 20), block.outputs[1].name: (10, 20), block.outputs[2].name: (10, 20), }, )
def test_single_input_to_single_operation(self): @mb.program(input_specs=[mb.TensorSpec(shape=(10, 20))]) def prog(x): x = mb.square(x=x) return x self.assertEqual(get_op_types_in_program(prog), ['square']) apply_pass_and_basic_check( prog, transform.FP16ComputePrecision(op_selector=lambda op: True)) _, _, block = apply_pass_and_basic_check( prog, "common::dead_code_elimination") self.assertEqual(get_op_types_in_program(prog), ["cast", "square", "cast"]) # Asserting first cast configuration cast_1 = block.find_ops(op_type="cast")[0] self.assertEqual(cast_1.dtype.val, "fp16") self.assertEqual(len(cast_1.outputs), 1) self.assertEqual(len(cast_1.outputs[0].child_ops), 1) self.assertEqual(cast_1.outputs[0].child_ops[0].op_type, "square") # Asserting second cast configuration cast_2 = block.find_ops(op_type="cast")[1] self.assertEqual(cast_2.dtype.val, "fp32") self.assertEqual(len(cast_2.outputs), 1) self.assertEqual(len(cast_2.outputs[0].child_ops), 0) assert_model_is_valid( prog, {"x": (10, 20)}, expected_output_shapes={block.outputs[0].name: (10, 20)}, )
def test_mul_op_fp32_int32_inputs(self): @mb.program(input_specs=[mb.TensorSpec(shape=(4,), dtype=types.fp32)]) def prog(x): const = mb.const(val=5) out = mb.mul(x=x, y=const) return out assert get_op_types_in_program(prog) == ["mul"] apply_pass_and_basic_check(prog, "mil_backend::homogenize_input_dtypes") # verify that there is no cast op in the program, since the # const input (int32) should have been promoted to a float32 and replaced with a new const assert get_op_types_in_program(prog) == ["mul"]
def test_move_transpose_before_subblock(self): ''' Input graph: x (input) ---> cast ---> transpose ---> cast (output) | | -----------> square ------> transpose (x1_t) ---> cast (output) | | -----------> squeeze ----> equal ----> squeeze | (true) <--- / \ ---> (false) | | | /<-(x1_t)->\ | add <-/ \--> add |---------> | <---------| | add ---> cast (output) ''' @mb.program(input_specs=[mb.TensorSpec(shape=(10, 20))]) def prog(x): x = mb.cast(x=x, dtype="fp16") x1 = mb.square(x=x) x1_t = mb.transpose(x=x1, perm=[1, 0]) def true_fn(): return mb.add(x=x1_t, y=1, name='x2') def false_fn(): return mb.add(x=x1_t, y=2, name='x2') is_one = mb.equal(x=mb.squeeze(x=x), y=1) pred = mb.squeeze(x=is_one) x3 = mb.cond(pred=pred, _true_fn=true_fn, _false_fn=false_fn) x4 = mb.add(x=x1_t, y=x3) x5 = mb.cast(x=x4, dtype="fp32") return x5 apply_pass_and_basic_check(prog, "common::topological_reorder") _, _, block = apply_pass_and_basic_check(prog, "common::dead_code_elimination") assert get_op_types_in_program(prog) == ['cast', 'square', 'squeeze', 'equal', 'squeeze', 'transpose', 'cond', 'add', 'cast'] assert_model_is_valid( prog, {"x": (10, 20)}, expected_output_shapes={ block.outputs[0].name: (20, 10) }, )
def test_pad_transposed_forked_conv(self): @mb.program(input_specs=[mb.TensorSpec(shape=(1, 16, 20, 24))]) def prog(x): pad = mb.pad(x=x, pad=[0, 0, 1, 1, 1, 1, 0, 0]) x = mb.transpose(x=pad, perm=[0, 3, 1, 2]) x = mb.conv(x=x, weight=np.random.random([24, 24, 3, 3]), pad_type="valid") x = mb.transpose(x=x, perm=[0, 2, 3, 1]) y = mb.transpose(x=pad, perm=[0, 3, 1, 2]) y = mb.conv(x=y, weight=np.random.random([24, 24, 3, 3]), pad_type="valid") y = mb.transpose(x=y, perm=[0, 2, 3, 1]) return x, y prev_prog, prev_block, block = apply_pass_and_basic_check( prog, "common::pad_conv_connect") self.assertEqual(get_op_types_in_program(prev_prog), [ "pad", "transpose", "conv", "transpose", "transpose", "conv", "transpose" ]) self.assertEqual(get_op_types_in_program(prog), [ "transpose", "pad", "conv", "transpose", "transpose", "pad", "conv", "transpose" ]) assert_model_is_valid( prog, {"x": (1, 16, 20, 24)}, expected_output_shapes={ block.outputs[0].name: (1, 16, 20, 24), block.outputs[1].name: (1, 16, 20, 24) }, )
def test_input_name_shadow(self): @mb.program(input_specs=[mb.TensorSpec(shape=(10, 20))]) def prog(x): # op name "x" results in output var name "x", which shadows prog # input var name "x" x = mb.transpose(x=x, perm=[1, 0], name="x") x = mb.relu(x=x, name="relu") return x prev_prog, _, block = apply_pass_and_basic_check( prog, "common::dedup_op_and_var_names") self.assertEqual(get_op_types_in_program(prev_prog), ['transpose', 'relu']) self.assertEqual(get_op_names_in_program(prev_prog), ['x', 'relu']) self.assertEqual(get_op_types_in_program(prog), ['transpose', 'relu']) self.assertEqual(get_op_names_in_program(prog), ['x', 'relu']) op = prog['main'].find_ops(op_type='transpose')[0] self.assertEqual("x_1", op.outputs[0].name) assert_model_is_valid( prog, {"x": (10, 20)}, expected_output_shapes={block.outputs[0].name: (20, 10)}, )
def test_linear_bias_fusion(self, rank, op_type, is_first_input, broadcast, backend): """ Input graph: Const | V input -----> linear -----> add/sub ---> out Output graph: input -----> linear ----> out """ input_shape = [1, 2, 3] input_shape = input_shape[-rank:] input_shape = tuple(input_shape) @mb.program(input_specs=[mb.TensorSpec(shape=input_shape)]) def prog(x): linear_weight = np.reshape(np.arange(6), (2, 3)).astype(np.float32) linear_bias = np.array([1., 2.]) bias = np.array([3., 4.]) if broadcast: if rank >= 2: bias = np.reshape(bias, (1, 2)) x = mb.linear( x=x, weight=linear_weight, bias=linear_bias, ) func = mb.add if op_type == "add" else mb.sub if is_first_input: kwargs = { "x": x, "y": bias, } else: kwargs = { "x": bias, "y": x, } x = func(**kwargs) return x prev_prog, prev_block, block = apply_pass_and_basic_check( prog, "common::fuse_linear_bias") assert get_op_types_in_program(prev_prog) == ["linear", op_type] assert get_op_types_in_program(prog) == ["linear"] # validate graph pass output_shape = [1, 2, 2] output_shape = tuple(output_shape[-rank:]) assert_model_is_valid( prog, {"x": input_shape}, expected_output_shapes={block.outputs[0].name: output_shape}, backend=backend, )
def test_mul_add_fusion_to_batchnorm(self, flip_mul_input_order, flip_add_input_order, rank_3_const_input): C = 3 gamma = np.random.rand(1, C, 1, 1) beta = np.random.rand(1, C, 1, 1) if rank_3_const_input: gamma = np.squeeze(gamma, axis=0) beta = np.squeeze(beta, axis=0) @mb.program(input_specs=[mb.TensorSpec(shape=(1, 10, 10, C))]) def prog(x): x = mb.transpose(x=x, perm=[0, 3, 1, 2]) if flip_mul_input_order: x = mb.mul(x=gamma, y=x) else: x = mb.mul(x=x, y=gamma) if flip_add_input_order: x = mb.add(x=beta, y=x) else: x = mb.add(x=x, y=beta) return x prev_prog, prev_block, block = apply_pass_and_basic_check( prog, "common::fuse_elementwise_to_batchnorm") if get_op_types_in_program(prev_prog) != ["transpose", "mul", "add"]: raise AssertionError if get_op_types_in_program(prog) != ["transpose", "batch_norm"]: raise AssertionError assert_model_is_valid( prog, {"x": (1, 10, 10, C)}, expected_output_shapes={block.outputs[0].name: (1, C, 10, 10)}, )
def test_op_name_duplicated_many(self): @mb.program(input_specs=[mb.TensorSpec(shape=(10, 20))]) def prog(x): x = mb.cast(x=x, dtype="fp16", name="castop") x = mb.cast(x=x, dtype="fp16", name="castop") x = mb.cast(x=x, dtype="int32", name="castop_2") x = mb.cast(x=x, dtype="int64", name="castop") x = mb.cast(x=x, dtype="fp32", name="castop_2") x = mb.square(x=x, name="square") return x prev_prog, _, block = apply_pass_and_basic_check( prog, "common::dedup_op_and_var_names") self.assertEqual(get_op_types_in_program(prev_prog), ['cast', 'cast', 'cast', 'cast', 'cast', 'square']) self.assertEqual( get_op_names_in_program(prev_prog), ['castop', 'castop', 'castop_2', 'castop', 'castop_2', 'square']) self.assertEqual(get_op_types_in_program(prog), ['cast', 'cast', 'cast', 'cast', 'cast', 'square']) self.assertEqual(get_op_names_in_program(prog), [ 'castop', 'castop_1', 'castop_2', 'castop_3', 'castop_2_1', 'square' ]) assert_model_is_valid( prog, {"x": (10, 20)}, expected_output_shapes={block.outputs[0].name: (10, 20)}, )
def test_invalid_leaky_relu_pattern1(self): """ Invalid because alpha value greater than 1 Input graph: const (val = 1.3) | input ----> mul ---------------> maximum -----------> output | | |---------------------------------- Output graph: same as input graph """ @mb.program(input_specs=[mb.TensorSpec(shape=(3, 5, 6))]) def prog(x): x1 = mb.mul(x=x, y=1.3) x1 = mb.maximum(x=x1, y=x) return x1 prev_prog, prev_block, block = apply_pass_and_basic_check( prog, "common::fuse_leaky_relu" ) assert get_op_types_in_program(prev_prog) == ["mul", "maximum"] assert get_op_types_in_program(prog) == ["mul", "maximum"]
def test_adjust_cast(self): """ Input graph: func main(int32 x) { fp64 y = cast(x=x, dtype="fp64") } -> (y) becomes func main(int32 x) { fp32 y = cast(x=x, dtype="fp32") } -> (y) """ @mb.program( input_specs=[mb.TensorSpec(shape=(1, 1, 1, 1), dtype=types.int32)]) def prog(x): y = mb.cast(x=x, dtype="fp64") return y prev_prog, prev_block, block = apply_pass_and_basic_check( prog, "mil_backend::adjust_io_to_supported_types") assert get_op_types_in_program(prev_prog) == ['cast'] assert get_op_types_in_program(prog) == ['cast'] prev_cast = prev_prog.functions['main'].operations[1] cast = prog.functions['main'].operations[2] assert prev_cast.dtype.val == "fp64" assert prev_cast.outputs[0].dtype == types.fp64 assert cast.dtype.val == "fp32" assert cast.outputs[0].dtype == types.fp32
def test_mixed_input_dtypes(self, op, x_dtype, y_dtype): @mb.program(input_specs=[ mb.TensorSpec(shape=(10, 10), dtype=string_to_builtin(x_dtype)), mb.TensorSpec(shape=(10, 10), dtype=string_to_builtin(y_dtype)) ]) def prog(x, y): x = getattr(mb, op)(x=x, y=y) return x assert get_op_types_in_program(prog) == [op] _, _, block = apply_pass_and_basic_check( prog, "mil_backend::homogenize_input_dtypes") assert get_op_types_in_program(prog) == ["cast", op] promoted_dtype = promote_types(string_to_builtin(x_dtype), string_to_builtin(y_dtype)) # Asserting cast configuration cast = block.find_ops(op_type="cast")[0] assert cast.dtype.val == builtin_to_string(promoted_dtype) assert len(cast.outputs) == 1 assert len(cast.outputs[0].child_ops) == 1 assert cast.outputs[0].child_ops[0].op_type == op
def test_0(self, reverse_order): x_shape = tuple(np.random.randint(low=1, high=4, size=5)) @mb.program(input_specs=[mb.TensorSpec(shape=x_shape)]) def program(x): sigmoid_x = mb.sigmoid(x=x) if not reverse_order: x = mb.mul(x=x, y=sigmoid_x) else: x = mb.mul(x=sigmoid_x, y=x) return x prev_prog, prev_block, block = apply_pass_and_basic_check( program, "mil_backend::fuse_activation_silu" ) assert get_op_types_in_program(prev_prog) == ["sigmoid", "mul"] assert get_op_types_in_program(program) == ["silu"] assert_model_is_valid( program=program, inputs={"x": x_shape}, backend=("mlprogram", "fp32"), expected_output_shapes={block.outputs[0].name: tuple(x_shape)}, )
def test_float16_input_output_with_opset_version_inference(self): """ Input graph: main(%x: (1, 1, 4, 4, fp16)(Tensor)) { block0() { %pixel_unshuffle_0: (1, 4, 2, 2, fp16)(Tensor) = pixel_unshuffle(x=%x, downscale_factor=2, name="pixel_unshuffle_0") } -> (%pixel_unshuffle_0) } This function would be inferred as an iOS16 function, and the graph pass should behave properly """ @mb.program(input_specs=[mb.TensorSpec(shape=(1, 1, 4, 4), dtype=types.fp16)]) def prog(x): x = mb.pixel_unshuffle(x=x, downscale_factor=np.uint32(2)) return x prev_prog, prev_block, block = apply_pass_and_basic_check( prog, "mil_backend::adjust_io_to_supported_types" ) prev_inputs = list(prev_block.inputs.items()) inputs = list(block.inputs.items()) prev_outputs = prev_block.outputs outputs = block.outputs assert prev_inputs[0][1].name == inputs[0][1].name assert outputs[0].name == prev_outputs[0].name assert get_op_types_in_program(prog) == ['pixel_unshuffle'] assert inputs[0][1].dtype == types.fp16 assert block.outputs[0].dtype == types.fp16
def test_nn_backend_style_sanitization(self): ''' Test that intermediate var names are unchanged, and only model input and output names are modified, i.e. sanitized (adhering to the format [a-zA-Z_][a-zA-Z0-9_]*) for the NN backend. ''' prog = Program() func_inputs = {"x/0": mb.placeholder(shape=[2, 3]), "y": mb.placeholder(shape=[2, 3])} with Function(func_inputs) as ssa_fun: x, y = ssa_fun.inputs["x/0"], ssa_fun.inputs["y"] x = mb.relu(x=x, name="relu/1") z = mb.add(x=x, y=y, name="out/1") ssa_fun.set_outputs([z]) prog.add_function("main", ssa_fun) prev_prog, prev_block, block = apply_pass_and_basic_check( prog, "common::sanitize_input_output_names", skip_output_name_check=True ) relu_op = prog.find_ops(op_type="relu", exactly_one=True)[0] assert relu_op.inputs["x"].name == "x_0" # input name: sanitized assert relu_op.outputs[0].name == "relu/1" # intermediate name: unchanged assert block.outputs[0].name == "out_1" # output name: sanitized # convert prev_prog to NN backend mlmodel = ct.convert(prev_prog) spec = mlmodel._spec assert spec.description.input[0].name == "x_0" assert spec.description.output[0].name == "out_1" relu_layer = spec.neuralNetwork.layers[0] assert relu_layer.output[0] == "relu/1"
def test_invalid_leaky_relu_pattern2(self): """ Invalid because input to the "maximum" op is not same as the input of the "mul" op Input graph: const (val = 0.3) | input ----> mul ---------------> maximum -----------> output | const Output graph: same as input graph """ @mb.program(input_specs=[mb.TensorSpec(shape=(3, 5, 6))]) def prog(x): x1 = mb.mul(x=x, y=0.3) x1 = mb.maximum(x=x1, y=0.4) return x1 prev_prog, prev_block, block = apply_pass_and_basic_check( prog, "common::fuse_leaky_relu" ) assert get_op_types_in_program(prev_prog) == ["mul", "maximum"] assert get_op_types_in_program(prog) == ["mul", "maximum"]
def test_onehot_matmul_to_gather_fusion(rank): """ Input: %2 = one_hot(%1, on_value=1, off_value=0, axis=-1) %3 = const() # rank 2 %4 = matmul(%2, %3) Output: %4 = gather(%3, %2, axis=0) """ rank4_shape = (10, 3, 6, 7) input_shape = rank4_shape[-rank:] vocab_size = 15 embedding_size = 12 @mb.program(input_specs=[mb.TensorSpec(shape=input_shape, dtype=types.int32)]) def prog(x): x = mb.one_hot( indices=x, on_value=1, off_value=0, axis=-1, one_hot_vector_size=vocab_size ) x = mb.matmul(x=x, y=np.random.rand(vocab_size, embedding_size)) return x prev_prog, prev_block, block = apply_pass_and_basic_check( prog, "common::fuse_onehot_matmul_to_gather" ) assert get_op_types_in_program(prev_prog) == ["one_hot", "matmul"] assert get_op_types_in_program(prog) == ["gather"] assert_model_is_valid( prog, {"x": input_shape}, expected_output_shapes={block.outputs[0].name: input_shape + (embedding_size,)}, )
def test_slicebyindex_mask_elimination(begin_mask, end_mask): @mb.program(input_specs=[mb.TensorSpec(shape=(4, 4))]) def prog(x): begin = [1, 1] end = [1, 1] for i in range(2): if not begin_mask[i]: begin[i] = 0 if not end_mask[i]: end[i] = 4 r1 = mb.slice_by_index(x=x, begin=begin, end=end, begin_mask=begin_mask, end_mask=end_mask) return mb.relu(x=r1) prev_prog, prev_block, block = apply_pass_and_basic_check( prog, "common::noop_elimination") assert get_op_types_in_program(prev_prog) == ["slice_by_index", "relu"] assert get_op_types_in_program(prog) == ["relu"] assert_model_is_valid( prog, {"x": (4, 4)}, expected_output_shapes={block.outputs[0].name: (4, 4)}, )
def test_concat_interleave_fusion_pass(): """ Given: %3 = concat(%1.a, %1.b, axis=-3, interleave=False) #shape = (B, n*C, H, W) %4 = reshape(%3) #shape = (B, n, C, H, W) %5 = transpose(%4, perm=[0, 2, 1, 3, 4]) # shape = (B, C, n, H, W) %6 = reshape(%5) # shape = (B, C*n, H, W) Result: %6 = concat(%1.a, %1.b, axis=-3, interleave=True) """ B, C, H, W = 1, 10, 20, 20 @mb.program(input_specs=[mb.TensorSpec(shape=(B,C,H,W)), mb.TensorSpec(shape=(B,C,H,W))]) def prog(x, y): z = mb.concat(values=[x,y], axis=1) z = mb.reshape(x=z, shape=(B, 2, C, H, W)) z = mb.transpose(x=z, perm=[0, 2, 1, 3, 4]) z = mb.reshape(x=z, shape=(B, -1, H, W)) return z prev_prog, prev_block, block = apply_pass_and_basic_check( prog, "common::detect_concat_interleave" ) assert get_op_types_in_program(prev_prog) == ["concat", "reshape", "transpose", "reshape"] assert get_op_types_in_program(prog) == ["concat"] concat_op = prog.find_ops(op_type="concat", exactly_one=True)[0] assert concat_op.interleave.val assert_model_is_valid( prog, {"x": (B, C, H, W), "y": (B, C, H, W)}, expected_output_shapes={block.outputs[0].name: (B, 2*C, H, W)}, )
def test_failure_different_constants(self): @mb.program(input_specs=[mb.TensorSpec(shape=(1, 2, 6, 8))]) def prog(x1): pad1 = mb.pad(x=x1, pad=[0, 0, 1, 1], mode='constant', constant_val=1.0) pad2 = mb.pad(x=pad1, pad=[1, 1, 0, 0], mode='constant', constant_val=2.0) return pad2 prev_prog, _, block = apply_pass_and_basic_check( prog, "common::merge_consecutive_paddings") assert get_op_types_in_program(prev_prog) == ["pad", "pad"] assert get_op_types_in_program(prog) == ["pad", "pad"] inputs = {"x1": (1, 2, 6, 8)} assert_model_is_valid( prog, inputs, expected_output_shapes={block.outputs[0].name: (1, 2, 8, 10)}, )
def test_program_bgr(self): """ Input graph: main(x: ImageType(color_layout="BGR", channel_first=True)) { y1 = relu(x) y2 = relu(x) output = add(y1, y2) } [output] Output graph: main(x: ImageType(channel_first=True)) { y1 = relu(x) y2 = relu(x) output = add(y1, y2) } [output] """ @mb.program(input_specs=[mb.TensorSpec(shape=(1, 3, 20, 20))]) def prog(x): y1 = mb.relu(x=x) y2 = mb.relu(x=x) z = mb.add(x=y1, y=y2) return z prog.main_input_types = (ct.ImageType(name='x', shape=[1, 3, 20, 20], color_layout="BGR", channel_first=True), ) prev_prog, prev_block, block = apply_pass_and_basic_check( prog, "mil_backend::insert_image_preprocessing_ops") assert get_op_types_in_program(prev_prog) == ["relu", "relu", "add"] assert get_op_types_in_program(prog) == ["relu", "relu", "add"]
def test(self, reverse_order, elem_op): x_shape = [1,] @mb.program(input_specs=[mb.TensorSpec(shape=x_shape)]) def program(x): x = mb.slice_by_index(x=x, begin=[0], end=[1], squeeze_mask=[True]) func = getattr(mb, elem_op) if reverse_order: x = func(x=2.0, y=x) else: x = func(x=x, y=2.0) expand = mb.expand_dims(x=x, axes=[0]) other_1 = mb.add(x=x, y=[1, 2, 3]) other_2 = mb.sub(x=x, y=[1, 2, 3]) return expand, other_1, other_2 prev_prog, prev_block, block = apply_pass_and_basic_check( program, "common::rank0_expand_dims_swap" ) assert get_op_types_in_program(prev_prog) == ["slice_by_index", elem_op, "expand_dims", "add", "sub"] assert get_op_types_in_program(program) == ["slice_by_index", "expand_dims", "expand_dims", elem_op, "squeeze", "add", "sub"] assert_model_is_valid( program=program, inputs={"x": x_shape}, expected_output_shapes={ block.outputs[0].name: tuple(x_shape), block.outputs[1].name: (3,), block.outputs[2].name: (3,), }, )
def test_add_conv_transpose_output_shape(): """ Given: %1: (1, 5, 39, fp32) = conv_transpose(...) # no output_shape input. Result: %2: (3, i32) = const(val=[1,5,39]) %3: (1, 5, 39, fp32) = conv_transpose(..., output_shape=%2) """ N, C_in, C_out, D1 = 1, 3, 5, 20 @mb.program(input_specs=[mb.TensorSpec(shape=(N, C_in, D1))]) def prog(x): weight = np.random.rand(C_out, C_in, D1).astype(np.float32) return mb.conv_transpose(x=x, weight=weight) prev_prog, prev_block, block = apply_pass_and_basic_check( prog, "common::add_conv_transpose_output_shape") assert get_op_types_in_program(prev_prog) == ["conv_transpose"] assert get_op_types_in_program(prog) == ["conv_transpose"] prev_conv_transpose_op = prev_prog.find_ops(op_type="conv_transpose", exactly_one=True)[0] conv_transpose_op = prog.find_ops(op_type="conv_transpose", exactly_one=True)[0] assert np.all(conv_transpose_op.output_shape.val == prev_conv_transpose_op.outputs[0].shape)
def test_negative_3(self): """ Input graph: input1(1, 5, 3, 4) -----> stack(axis=1) -----> reshape(shape=(-1, 2, 5, 4, 3)) ---> out(1, 5, 6, 4) ^ | input2(1, 5, 3, 4) ---------- Output graph: Unchanged -- this graph is not equivalent to a concat. """ @mb.program(input_specs=[mb.TensorSpec(shape=(1, 5, 3, 4)), mb.TensorSpec(shape=(1, 5, 3, 4))]) def prog(x1, x2): a = mb.stack(values=[x1, x2], axis=1) a = mb.reshape(x=a, shape=[-1, 2, 5, 4, 3]) return a prev_prog, prev_block, block = apply_pass_and_basic_check( prog, "common::replace_stack_reshape" ) self.assertEqual( get_op_types_in_program(prev_prog), ["stack", "reshape"] ) self.assertEqual(get_op_types_in_program(prog), ["stack", "reshape"])
def test_gelu_tanh_approximation(): """ Detect gelu tanh approx pattern, found in the TF bert model. y = ( tanh((.0447)x^3 + x ) * (sqrt(2/pi)) + 1 ) * 0.5 * x """ @mb.program(input_specs=[mb.TensorSpec(shape=(3, 5, 6))]) def prog(x): x1 = mb.pow(x=x, y=3) x1 = mb.mul(x=0.044715, y=x1) x1 = mb.add(x=x1, y=x) x1 = mb.mul(x=x1, y=np.sqrt(2 / np.pi)) x1 = mb.tanh(x=x1) x1 = mb.add(x=1, y=x1) x1 = mb.mul(x=0.5, y=x1) x1 = mb.mul(x=x, y=x1) return x1 prev_prog, prev_block, block = apply_pass_and_basic_check( prog, "common::fuse_gelu_tanh_approximation") assert get_op_types_in_program(prev_prog) == [ "pow", "mul", "add", "mul", "tanh", "add", "mul", "mul", ] assert get_op_types_in_program(prog) == ["gelu"] assert_model_is_valid( prog, {"x": (3, 5, 6)}, expected_output_shapes={block.outputs[0].name: (3, 5, 6)}, )
def test_success_h_axis(self): @mb.program(input_specs=[mb.TensorSpec(shape=(1, 2, 6, 8))]) def prog(x1): left = mb.slice_by_index(x=x1, begin=[0, 0, 1, 0], end=[0, 0, 2, 0], end_mask=[True, True, False, True]) right = mb.slice_by_index(x=x1, begin=[0, 0, -2, 0], end=[0, 0, -1, 0], end_mask=[True, True, False, True]) x = mb.concat(values=[left, x1, right], axis=2) return x prev_prog, _, block = apply_pass_and_basic_check( prog, "common::use_reflection_padding") assert get_op_types_in_program(prev_prog) == [ "slice_by_index", "slice_by_index", "concat" ] assert get_op_types_in_program(prog) == ["pad"] inputs = {"x1": (1, 2, 6, 8)} assert_model_is_valid( prog, inputs, expected_output_shapes={block.outputs[0].name: (1, 2, 8, 8)}, )
def test_success_3_layers(self): @mb.program(input_specs=[mb.TensorSpec(shape=(1, 2, 6, 8))]) def prog(x1): pad1 = mb.pad(x=x1, pad=[0, 0, 1, 1], mode='constant', constant_val=3.0) pad2 = mb.pad(x=pad1, pad=[1, 1, 0, 0], mode='constant', constant_val=3.0) pad3 = mb.pad(x=pad2, pad=[1, 1, 0, 0], mode='constant', constant_val=3.0) return pad3 prev_prog, _, block = apply_pass_and_basic_check( prog, "common::merge_consecutive_paddings") assert get_op_types_in_program(prev_prog) == ["pad", "pad", "pad"] assert get_op_types_in_program(prog) == ["pad"] pad_ops = [op for op in prog["main"].operations if op.op_type == "pad"] assert pad_ops[0].inputs["constant_val"].val == 3.0 inputs = {"x1": (1, 2, 6, 8)} assert_model_is_valid( prog, inputs, expected_output_shapes={block.outputs[0].name: (1, 2, 10, 10)}, )
def test_nested_block(self): @mb.program(input_specs=[mb.TensorSpec(shape=(1, ))]) def prog(x): def true_fn(): # returns var with name x shadows input 'x' return mb.add(x=x, y=1, name='x') def false_fn(): # two ops with name "x" return mb.add(x=x, y=-1, name='x') pred = mb.equal(x=mb.squeeze(x=x), y=1) return mb.cond(pred=pred, _true_fn=true_fn, _false_fn=false_fn) cond_op = prog.functions['main'].operations[-1] assert cond_op.blocks[0].outputs[0].name == 'x' assert cond_op.blocks[1].outputs[0].name == 'x' prev_prog, _, block = apply_pass_and_basic_check( prog, "common::dedup_op_and_var_names") cond_op = prog.functions['main'].operations[-1] assert cond_op.blocks[0].outputs[0].name == 'x_1' assert cond_op.blocks[1].outputs[0].name == 'x_2' assert_model_is_valid( prog, {"x": (1, )}, expected_output_shapes={block.outputs[0].name: (1, )}, )