def test_multidim_sigmoid(m_): with pm.Node(name="logistic") as graph: m = pm.parameter(name="m") n = pm.parameter(name="n") x = pm.input("x", shape=(m)) w = pm.state("w", shape=(m)) i = pm.index(0, m - 1, name="i") o = pm.sigmoid(w[i] * x[i], name="out") x_ = np.random.randint(0, 10, m_).astype(np.float) w_ = np.random.randint(0, 10, m_).astype(np.float) shape_dict = {"m": m_} input_dict = {"x": x_, "w": w_} np_res = sigmoid((x_ * w_)) coarse_eval = graph("out", input_dict) np.testing.assert_allclose(np_res, coarse_eval) lowered = set_shape_and_lower(graph, shape_dict) keys = [f"out/out({i},)" for i in range(m_)] x_ = np.random.randint(0, 10, m_).astype(np.float) w_ = np.random.randint(0, 10, m_).astype(np.float) input_dict = {} for i in range(m_): input_dict[f"x/x({i},)"] = x_[i] input_dict[f"w/w({i},)"] = w_[i] np_res = sigmoid((x_ * w_)) lower_res = np.asarray(lowered(keys, input_dict)).reshape(np_res.shape) np.testing.assert_allclose(lower_res, np_res)
def get_value_info_shape(vi, mgdfg): if isinstance(vi, np.ndarray): ret = vi.shape else: ret = [] for i, dim in enumerate(vi.type.tensor_type.shape.dim): if hasattr(dim, 'dim_param') and dim.dim_param: if dim.dim_param in mgdfg.nodes: shape_node = mgdfg.nodes[dim.dim_param] else: shape_node = pm.parameter(name=dim.dim_param, graph=mgdfg) d_val = shape_node elif not dim.dim_value: shape_node = pm.parameter(name=f"{vi.name}_dim_{i}", graph=mgdfg) d_val = shape_node elif dim.dim_value > 0: d_val = dim.dim_value else: continue ret.append(d_val) ret = tuple(ret) return ret if len(ret) > 0 else (1, )
def test_multi_dim(): with pm.Node(name="elem4") as graph: m = pm.parameter(name="m") n = pm.parameter(name="n") x = pm.input("x", shape=(m, n)) w = pm.state("w", shape=(m, n)) i = pm.index(0, m - 1, name="i") j = pm.index(0, n - 1, name="j") w[i, j] = (w[i, j] * x[i, j]) m_ = 3 n_ = 4 x_ = np.random.randint(0, 10, m_ * n_).reshape((m_, n_)) w_ = np.random.randint(0, 10, m_ * n_).reshape((m_, n_)) coarse_eval = graph("w", x=x_, w=w_) np_result = x_ * w_ np.testing.assert_allclose(coarse_eval, np_result) shape_pass = NormalizeGraph({"m": m_, "n": n_}) graph_shapes = shape_pass(graph) shape_res = graph_shapes("w", x=x_, w=w_) np.testing.assert_allclose(shape_res, np_result) lower_pass = Lower({}) lowered_graph = lower_pass(graph_shapes) input_info = {} for i in range(m_): for j in range(n_): input_info[f"w/w({i}, {j})"] = w_[i, j] input_info[f"x/x({i}, {j})"] = x_[i, j] fine_grained_eval = lowered_graph("w/w(2, 3)", input_info) assert fine_grained_eval == np_result[2, 3]
def test_load_linear_regressor(m_): shape_dict = {"m": m_} m = pm.parameter("m") mu = pm.parameter(name="mu", default=1.0) x = pm.input("x", shape=(m)) y = pm.input("y") w = pm.state("w", shape=(m)) graph = pm.linear_regressor_train(x, w, y, mu, m) test_graph, input_info, out_info, keys = linear(m=m_, coarse=True) assert len(test_graph.nodes.keys()) == len(graph.nodes.keys()) assert op_counts(test_graph) == op_counts(graph) shape_val_pass = pm.NormalizeGraph(shape_dict) new_graph = shape_val_pass(graph) test_res = new_graph(keys, input_info) np.testing.assert_allclose(test_res, out_info["w"]) test_graph_lowered, input_info, new_out_info, keys = linear(m=m_) flatten_pass = pm.Lower({}) test_flatten_pass = pm.Lower({}) flattened_g = flatten_pass(new_graph) ref_lowered = test_flatten_pass(test_graph_lowered, {}) assert len(ref_lowered.nodes.keys()) == len(flattened_g.nodes.keys()) assert op_counts(ref_lowered) == op_counts(flattened_g) all_vals = flattened_g(keys, input_info) np.testing.assert_allclose(new_out_info["w"], all_vals)
def test_multi_dim_op_slice(): with pm.Node(name="elem2") as graph: m = pm.parameter(name="m") n = pm.parameter(name="n") mu = pm.parameter(name="mu", default=2.0) x = pm.input(name="x", shape=(m, n)) w = pm.state(name="w", shape=(m, n)) i = pm.index(0, m - 1, name="i") j = pm.index(0, n - 1, name="j") out = (x[i, j] * w[i, j]).set_name("w_out") w[i, j] = (mu * (out[i, j] - w[i, j])) m_ = 3 n_ = 2 x_ = np.random.randint(0, 10, m_ * n_).reshape((m_, n_)) w_ = np.random.randint(0, 10, m_ * n_).reshape((m_, n_)) coarse_eval = graph("w", x=x_, w=w_) np_result = (x_ * w_ - w_) * 2.0 np.testing.assert_allclose(coarse_eval, np_result) shape_pass = NormalizeGraph({"m": m_, "n": n_}) graph_shapes = shape_pass(graph) shape_res = graph_shapes("w", x=x_, w=w_) np.testing.assert_allclose(shape_res, np_result) lower_pass = Lower({}) lowered_graph = lower_pass(graph_shapes) input_info = {} for i in range(m_): for j in range(n_): input_info[f"w/w({i}, {j})"] = w_[i, j] input_info[f"x/x({i}, {j})"] = x_[i, j] fine_grained_eval = lowered_graph("w/w(2, 1)", input_info) assert fine_grained_eval == np_result[2, 1]
def test_translate_conv(x_shape, w_shape, params): shape_dict = {"n": x_shape[0], "c": x_shape[1], "ih": x_shape[2], "iw": x_shape[3], "nf": w_shape[0], "kh": w_shape[2], "kw": w_shape[3], "stride": params["stride"], "pad": params["pad"]} _, input_info, out_info, keys = conv(x_shape, w_shape, params, coarse=True, debug_matrix=False) n = pm.parameter(name="n") c = pm.parameter(name="ic") ih = pm.parameter(name="ih") iw = pm.parameter(name="iw") nf = pm.parameter(name="nf") kh = pm.parameter(name="kh") kw = pm.parameter(name="kw") x = pm.input(name="data", shape=(n, c, ih, iw)) w = pm.state(name="w", shape=(nf, c, kh, kw)) b = pm.state(name="bias", shape=(nf)) stride = pm.parameter(name="stride") pad = pm.parameter(name="pad") out = pm.output(name="out") graph = pm.conv_bias(x, w, b, out, stride, pad) tinput_info = copy.deepcopy(input_info) res0 = graph("out", tinput_info) np.testing.assert_allclose(res0, out_info["out"])
def test_name_change(): with pm.Node() as graph: operation = pm.parameter(default=None, name='operation1') pm.parameter(default=None, name='operation3') assert 'operation1' in graph.nodes operation.name = 'operation2' assert 'operation2' in graph.nodes assert graph['operation2'] is operation # We cannot rename to an existing operation with pytest.raises(ValueError): operation.name = 'operation3'
def test_squeeze(): with pm.Node(name="indexop") as graph: m = pm.parameter(name="m") n = pm.parameter(name="n") x = pm.state("x", shape=(m, n)) x_us = pm.squeeze(x, axis=None, name="res") m_ = 5 n_ = 1 x_ = np.random.randint(0, 10, (m_, n_)) input_info = {"m": m_, "n": n_, "x": x_} res = graph("res", input_info) np.testing.assert_allclose(res, np.squeeze(x_, axis=1))
def test_conditional_callback(): with pm.Node() as graph: a = pm.parameter(default=1) b = pm.parameter(default=2) c = pm.placeholder() d = pm.predicate(c, a, b + 1) # Check that we have "traced" the correct number of operation evaluations tracer = pm.Profiler() assert graph(d, {c: True}, callback=tracer) == 1 assert len(tracer.times) == 3 tracer = pm.Profiler() assert graph(d, {c: False}, callback=tracer) == 3 assert len(tracer.times) == 4
def test_linear_deserialize(): graph_name = "linear_reg1" with pm.Node(name=graph_name) as graph: m = pm.placeholder("m") x_ = pm.placeholder("x", shape=(m)) y_ = pm.placeholder("y") w_ = pm.placeholder("w", shape=(m)) mu = pm.parameter(name="mu", default=1.0) i = pm.index(0, (m - 1).set_name("m-1"), name="i") h = pm.sum([i], (x_[i] * w_[i]).set_name("x*w"), name="h") d = (h - y_).set_name("h-y") g = (d * x_[i]).set_name("d*x") mug = (mu * g[i]).set_name("mu*g[i]") w_ = ((w_[i]) - mug).set_name("w_out") x = np.random.randint(0, 10, 10) y = np.random.randint(0, 10, 1)[0] w = np.random.randint(0, 10, 10) graph_res = graph("w_out", {"x": x, "y": y, "w": w}) actual_res = w - ((np.sum(x * w) - y) * x) * 1.0 np.testing.assert_allclose(graph_res, actual_res) cwd = Path(f"{__file__}").parent base_path = f"{cwd}/pmlang_examples" full_path = f"{base_path}/outputs" pb_path = f"{full_path}/{graph_name}.srdfg" pm.pb_store(graph, full_path) node = pm.pb_load(pb_path) new_graph_res = node("w_out", {"x": x, "y": y, "w": w}) np.testing.assert_allclose(graph_res, new_graph_res) np.testing.assert_allclose(actual_res, new_graph_res)
def test_dict(): expected = 13 with pm.Node() as graph: a = pm.parameter(default=expected) b = pm.identity({'foo': a}) actual = graph(b)['foo'] assert actual is expected, "expected %s but got %s" % (expected, actual)
def test_list(): expected = 13 with pm.Node() as graph: a = pm.parameter(default=expected) b = pm.identity([a, a]) actual, _ = graph(b) assert actual is expected, "expected %s but got %s" % (expected, actual)
def test_single_dim_norm(): with pm.Node(name="elem1") as graph: m = pm.parameter("m") x = pm.input("x", shape=m) w = pm.state("w", shape=m) i = pm.index(0, m - 1, name="i") w[i] = (w[i] * x[i]) x_ = np.random.randint(0, 10, 3) w_ = np.random.randint(0, 10, 3) coarse_eval = graph("w", x=x_, w=w_) np_result = x_ * w_ np.testing.assert_allclose(coarse_eval, np_result) shape_pass = NormalizeGraph({"m": 3}) graph_shapes = shape_pass(graph) shape_res = graph_shapes("w", x=x_, w=w_) np.testing.assert_allclose(shape_res, np_result) lower_pass = Lower({}) lowered_graph = lower_pass(graph_shapes) input_info = {f"w/w({i},)": w_[i] for i in range(len(w_))} input_info.update({f"x/x({i},)": x_[i] for i in range(len(x_))}) fine_grained_eval = lowered_graph("w/w(1,)", input_info) assert fine_grained_eval == np_result[1] pb_path = f"{OUTPATH}/{graph.name}.srdfg" pm.pb_store(lowered_graph, OUTPATH) loaded_node = pm.pb_load(pb_path) input_info = {f"w/w({i},)": w_[i] for i in range(len(w_))} input_info.update({f"x/x({i},)": x_[i] for i in range(len(x_))}) fine_grained_eval = loaded_node("w/w(1,)", input_info) assert fine_grained_eval == np_result[1]
def test_single_dim_op_slice(): with pm.Node(name="elem3") as graph: m = pm.parameter(name="m") x = pm.input("x", shape=m) w = pm.state("w", shape=m) i = pm.index(0, m - 1, name="i") out = (w[i] * x[i]) w[i] = (out[i] - w[i]) m_ = 3 x_ = np.random.randint(0, 10, m_) w_ = np.random.randint(0, 10, m_) coarse_eval = graph("w", x=x_, w=w_) np_result = x_ * w_ - w_ np.testing.assert_allclose(coarse_eval, np_result) shape_pass = NormalizeGraph({"m": 3}) graph_shapes = shape_pass(graph) shape_res = graph_shapes("w", x=x_, w=w_) np.testing.assert_allclose(shape_res, np_result) lower_pass = Lower({}) lowered_graph = lower_pass(graph_shapes) input_info = {f"w/w({i},)": w_[i] for i in range(len(w_))} input_info.update({f"x/x({i},)": x_[i] for i in range(len(x_))}) fine_grained_eval = lowered_graph("w/w(2,)", input_info) assert fine_grained_eval == np_result[2]
def create_svm_wifi(features, locations, lr=0.0001, deltav=1, train_size=7703): with pm.Node(name="svm_wifi") as graph: learning_rate = pm.parameter("learning_rate", default=lr) delta = pm.parameter("delta", default=deltav) n_features = pm.parameter("n_features", default=features) n_locations = pm.parameter("n_locations", default=locations) x_train = pm.input("x_train", shape=(n_features, )) y_train = pm.input("y_train", shape=(n_locations, )) y_train_inv = pm.input("y_train_inv", shape=(n_locations, )) weights = pm.state("weights", shape=(n_features, n_locations)) i = pm.index(0, n_features - 1, name="i") j = pm.index(0, n_locations - 1, name="j") scores = pm.sum([i], (weights[i, j] * x_train[i]), name="scores") correct_class_score = pm.sum([j], (scores[j] * y_train[j]), name="correct_class_score") h = ((scores[j] - correct_class_score + delta).set_name("h") > 0) # margin = (pm.cast(np.float32, h[j]) * y_train_inv[j]).set_name("margin") margin = (h[j] * y_train_inv[j]).set_name("margin") valid_margin_count = pm.sum([j], margin[j], name="valid_margin_count") partial = (y_train[j] * valid_margin_count).set_name("partial") updated_margin = (margin[j] - partial[j]).set_name("updated_margin") # # # dW = (x_train[i] * updated_margin[j]).set_name("dW") weights[i, j] = (weights[i, j] - learning_rate * dW[i, j]).set_name("weights_update") shape_dict = {"n_features": features, "n_locations": locations} input_info, keys, out_info = svm_wifi_datagen(features, locations, lr, deltav, lowered=True) cwd = Path(f"{__file__}").parent full_path = f"{cwd}/outputs" tabla_path = f"{full_path}/{graph.name}_{locations}_{features}_tabla.json" tabla_ir, tabla_graph = pm.generate_tabla(graph, shape_dict, tabla_path, context_dict=input_info, add_kwargs=True)
def test_sigmoid(m_): with pm.Node(name="logistic1") as graph: m = pm.parameter(name="m") n = pm.parameter(name="n") x = pm.input("x", shape=(m)) w = pm.state("w", shape=(m)) i = pm.index(0, m - 1, name="i") o = pm.sigmoid(pm.sum([i], w[i] * x[i]), name="out") x_ = np.random.randint(0, 10, m_) w_ = np.random.randint(0, 10, m_) input_dict = {"x": x_, "w": w_} np_res = int(sigmoid(np.sum(x_ * w_))) shape_dict = {"m": m_} coarse_eval = graph("out", x=x_, w=w_) np.testing.assert_allclose(np_res, coarse_eval) lowered = set_shape_and_lower(graph, shape_dict)
def test_index_op(): with pm.Node(name="indexop") as graph: m = pm.parameter(name="m") n = pm.parameter(name="n") i = pm.index(0, m - 1, name="i") j = pm.index(0, n - 1, name="j") i_ = (i + 1).set_name("i_") k = (i + j).set_name("k") m_ = 5 n_ = 3 input_info = {"m": m_, "n": n_} res = graph("k", input_info) op1 = np.arange(0, m_) op2 = np.arange(0, n_) value = np.array(list(product(*(op1, op2)))) value = np.array(list(map(lambda x: x[0] + x[1], value))) np.testing.assert_allclose(res, value)
def test_binary_operators_right(binary_operators): operator, a, b, expected = binary_operators with pm.Node() as graph: _b = pm.parameter(default=b) operation = eval('a %s _b' % operator) actual = graph(operation) assert actual == expected, "expected %s %s %s == %s but got %s" % \ (a, operator, b, expected, actual)
def test_conditional(): with pm.Node() as graph: x = pm.parameter(default=4) y = pm.placeholder(name='y') condition = pm.placeholder(name='condition') z = pm.predicate(condition, x, y) assert graph(z, condition=False, y=5) == 5 # We expect a value error if we evaluate the other branch without a placeholder with pytest.raises(ValueError): print(graph(z, condition=False))
def test_new(): test_a = np.array([1, 2, 3, 4]) test_b = np.array([5, 6, 7, 8]) test_placeholder = pm.placeholder("hello") with pm.Node(name="main") as graph: a = pm.parameter(default=6, name="a") b = pm.parameter(default=5, name="b") a = (a + b).set_name("a_mul_b") with pm.Node(name="graph2") as graph2: c = pm.variable([[[0, 1, 2], [3, 4, 5], [6, 7, 8]], [[9, 10, 11], [12, 13, 14], [15, 16, 17]], [[18, 19, 20], [21, 22, 23], [24, 25, 26]]], name="c") c_2 = (c * 2).set_name(name="c2") e = pm.parameter(default=4, name="e") l = pm.placeholder("test") x = (l * e).set_name("placeholdermult") i = pm.index(0, 1, name="i") j = pm.index(0, 1, name="j") k = pm.index(0, 2, name="k") e_i = pm.var_index(c, [i, j, k], "e_i")
def linear_reg(): with pm.Node(name="linear_reg") as graph: m = pm.placeholder("m") x = pm.placeholder("x", shape=(m), type_modifier="input") y = pm.placeholder("y", type_modifier="input") w = pm.placeholder("w", shape=(m), type_modifier="state") mu = pm.parameter(name="mu", default=1.0) i = pm.index(0, (graph["m"]-1).set_name("m-1"), name="i") h = pm.sum([i], (x[i] * w[i]).set_name("x*w"), name="h") d = (h-y).set_name("h-y") g = (d*x).set_name("d*x") w_ = (w - (mu*g).set_name("mu*g")).set_name("w-mu*g")
def test_second(): test_a = np.array([1, 2, 3, 4]) test_b = np.array([5, 6, 7, 8]) with pm.Node(name="main") as graph: a = pm.parameter(default=6, name="a") b = pm.parameter(default=5, name="b") a = (a + b).set_name("a_mul_b") with pm.Node(name="graph2") as graph2: n = pm.placeholder("n") b = pm.placeholder("b") e = pm.parameter(default=6, name="e") l = pm.state("test", shape=(n, b)) i = pm.index(0, graph2["n"] - 1) j = pm.index(0, graph2["b"] - 1) lij = pm.var_index(l, [i, j], "lij") x = (l * e).set_name("placeholdermult") _ = graph2("test", {l: np.arange(16).reshape((-1, 4))}) _ = graph2("lij", {l: np.arange(16).reshape((-1, 4))}) _ = graph2("placeholdermult", {l: np.arange(16).reshape((-1, 4))})
def gen_from_shape(graph_type, input_shape, params=None): if graph_type == "linear": x = pm.input(name="x", shape=input_shape) w = pm.state(name="w", shape=input_shape) y = pm.input(name="y") mu = pm.parameter(name="mu", default=1.0) m = pm.parameter(name="m", default=input_shape) return pm.linear_regressor_train(x, w, y, mu, m, name="linear_regressor") elif graph_type == "logistic": x = pm.input(name="x", shape=input_shape) w = pm.state(name="w", shape=input_shape) y = pm.input(name="y") mu = pm.parameter(name="mu", default=1.0) m = pm.parameter(name="m", default=input_shape) return pm.logistic_regressor_train(x, w, y, mu, m, name="logistic_regressor") elif graph_type == "svm": x = pm.input(name="x", shape=input_shape) w = pm.state(name="w", shape=input_shape) y = pm.input(name="y") mu = pm.parameter(name="mu", default=1.0) m = pm.parameter(name="m", default=input_shape) return pm.svm_classifier_train(x, w, y, mu, m, name="svm_classifier")
def test_load_nested_linear_regressor(m_): shape_dict = {"m": m_} with pm.Node(name="nested_linear") as graph: m = pm.parameter(name="m") mu = pm.parameter(name="mu", default=1.0) x = pm.input("x", shape=(m)) y = pm.input("y") w = pm.state("w", shape=(m)) pm.linear_regressor_train(x, w, y, mu, m, name="linear_regressor") j = pm.index(0, m-1, name="j") tw = (w[j] - 4).set_name("tw") test_graph, input_info, out_info, keys = linear(m=m_, coarse=True) shape_val_pass = pm.NormalizeGraph(shape_dict) new_graph = shape_val_pass(graph) test_res = new_graph("tw", input_info) np.testing.assert_allclose(test_res, (out_info["w"] - 4)) ref_graph, input_info, new_out_info, keys = linear(m=m_) flatten_pass = pm.Lower({}) keys = [f"tw/tw({i},)" for i in range(m_)] flattened_g = flatten_pass(new_graph) all_vals = flattened_g(keys, input_info)
def linear_reg_graph(): graph_name = "linear_reg" with pm.Node(name="linear_reg") as graph: m = pm.placeholder("m") mu = pm.parameter(name="mu", default=1.0) x_ = pm.placeholder("x", shape=(m), type_modifier="input") y_ = pm.placeholder("y", type_modifier="input") w_ = pm.placeholder("w", shape=(m), type_modifier="state") i = pm.index(0, m - 1, name="i") h = pm.sum([i], (x_[i] * w_[i]).set_name("x*w"), name="h") d = (h - y_).set_name("h-y") g = (d * x_[i]).set_name("d*x") w_out = (w_[i]) - mu * g[i] w_out.set_name("res") return graph
def test_conditional_with_length(): def f(a): return a, a with pm.Node() as graph: x = pm.parameter(default=4) y = pm.placeholder(name='y') condition = pm.placeholder(name='condition') z1, z2 = pm.predicate(condition, pm.func_op(f, x).set_name("xfunc"), pm.func_op(f, y).set_name("yfunc"), shape=2, name="predfunc") assert graph([z1, z2], condition=True) == (4, 4) assert graph([z1, z2], condition=False, y=5) == (5, 5)
def test_linear_embedded(): with pm.Node(name="linear_reg") as graph: m = pm.placeholder("m") x = pm.placeholder("x", shape=(m), type_modifier="input") y = pm.placeholder("y", type_modifier="input") w = pm.placeholder("w", shape=(m), type_modifier="state") mu = pm.parameter(name="mu", default=1.0) i = pm.index(0, (graph["m"] - 1).set_name("m-1"), name="i") h = pm.sum([i], (x[i] * w[i]).set_name("x*w"), name="h") d = (h - y).set_name("h-y") g = (d * x).set_name("d*x") w_ = (w - (mu * g).set_name("mu*g")).set_name("w-mu*g") x = np.random.randint(0, 10, 5) y = np.random.randint(0, 10, 1)[0] w = np.random.randint(0, 10, 5) graph_res = graph("w-mu*g", {"x": x, "y": y, "w": w}) actual_res = w - ((np.sum(x * w) - y) * x) * 1.0 np.testing.assert_allclose(graph_res, actual_res)
def test_lower_group_op(): with pm.Node(name="linear_reg1") as graph: m = pm.parameter(name="m") x = pm.input("x", shape=(m)) y = pm.input("y") w = pm.state("w", shape=(m)) i = pm.index(0, m - 1, name="i") h = pm.sum([i], w[i] * x[i], name="h") m_ = 3 n_ = 3 x_ = np.random.randint(0, 10, m_) w_ = np.random.randint(0, 10, (m_)) np_result = np.sum(x_ * w_) np.testing.assert_allclose(graph("h", {"w": w_, "x": x_}), np_result) np.testing.assert_allclose(graph("h", w=w_, x=x_), np_result) shape_pass = NormalizeGraph({"m": m_, "n": n_}) graph_shapes = shape_pass(graph) shape_res = graph_shapes("h", x=x_, w=w_) np.testing.assert_allclose(shape_res, np_result) lower_pass = Lower({}) lowered_graph = lower_pass(graph_shapes) input_info = {f"w/w({i},)": w_[i] for i in range(len(w_))} input_info.update({f"x/x({i},)": x_[i] for i in range(len(x_))}) # fine_grained_eval = lowered_graph("h/h(4,)", input_info) assert fine_grained_eval == np_result pb_path = f"{OUTPATH}/linear_reg1.srdfg" pm.pb_store(lowered_graph, OUTPATH) loaded_node = pm.pb_load(pb_path) # input_info = {f"w/w({i},)": w_[i] for i in range(len(w_))} input_info.update({f"x/x({i},)": x_[i] for i in range(len(x_))}) loaded_res = loaded_node("h/h(4,)", input_info) assert loaded_node.func_hash() == lowered_graph.func_hash() assert loaded_res == np_result
def test_avg_pool(data_shape, kernel_shape, stride): data = np.random.randint(0, 5, data_shape) tout = pooling(data, kernel_shape[0], kernel_shape[1], stride=stride) out = pm.output(name="out") n = pm.parameter("ns") ic = pm.parameter("ic") ih = pm.parameter("ih") iw = pm.parameter("iw") kh = pm.parameter("kh") kw = pm.parameter("kw") x = pm.input(name="data", shape=(n, ic, ih, iw)) g = pm.avg_pool2d(x, out, kh, kw, stride=stride, pad=0) inp_info = {} inp_info["data"] = data inp_info["kh"] = kernel_shape[0] inp_info["kw"] = kernel_shape[1] test_out = g("out", inp_info) np.testing.assert_allclose(test_out, tout)
def lenet(lenet_type="lenet5", coarse=True, debug=False): with pm.Node(name="lenet") as graph: n = pm.parameter(name="n") c = pm.parameter(name="ic") ih = pm.parameter(name="ih") iw = pm.parameter(name="iw") nf1 = pm.parameter(name="nf1") kh1 = pm.parameter(name="kh1") kw1 = pm.parameter(name="kw1") data = pm.input(name="data", shape=(n, c, ih, iw)) w1 = pm.state(name="w1", shape=(nf1, c, kh1, kw1)) b1 = pm.state(name="b1", shape=(nf1)) s1 = pm.parameter(name="s1") p1 = pm.parameter(name="p1") c1 = pm.output(name="c1", shape=(n, nf1, 28, 28)) a1 = pm.output(name="a1", shape=(n, nf1, 28, 28)) l1 = pm.output(name="l1", shape=(n, nf1, 14, 14)) pm.conv_bias(data, w1, b1, c1, s1, p1) pm.elem_tanh(c1, a1, shape=a1.shape) pm.avg_pool2d(a1, l1, 2, 2, 2, 0) nf2 = pm.parameter(name="nf2") kh2 = pm.parameter(name="kh2") kw2 = pm.parameter(name="kw2") w2 = pm.state(name="w2", shape=(nf2, nf1, kh2, kw2)) b2 = pm.state(name="b2", shape=(nf2)) s2 = pm.parameter(name="s2") p2 = pm.parameter(name="p2") c2 = pm.output(name="c2", shape=(n, nf2, 10, 10)) a2 = pm.output(name="a2", shape=(n, nf2, 10, 10)) l2 = pm.output(name="l2", shape=(n, nf2, 5, 5)) pm.conv_bias(l1, w2, b2, c2, s2, p2) pm.elem_tanh(c2, a2, shape=a2.shape) pm.avg_pool2d(a2, l2, 2, 2, 2, 0) nf3 = pm.parameter(name="nf3") kh3 = pm.parameter(name="kh3") kw3 = pm.parameter(name="kw3") w3 = pm.state(name="w3", shape=(nf3, nf2, kh3, kw3)) b3 = pm.state(name="b3", shape=(nf3)) s3 = pm.parameter(name="s3") p3 = pm.parameter(name="p3") c3 = pm.output(name="c3", shape=(n, nf3, 1, 1)) a3 = pm.output(name="a3", shape=(n, nf3, 1, 1)) pm.conv_bias(l2, w3, b3, c3, s3, p3) pm.elem_tanh(c3, a3, shape=a3.shape) f4 = pm.output(name="f4", shape=(n, nf3)) pm.coarse_flatten(a3, f4, axis=1, shape=f4.shape) m5 = pm.parameter(name="m5") n5 = pm.parameter(name="n5") f5 = pm.output(name="f5", shape=(n, m5)) w5 = pm.state(name="w5", shape=(m5, n5)) # w5 = pm.state(name="w5", shape=(n5, m5)) a6 = pm.output(name="a5", shape=(n, m5)) b5 = pm.state(name="b5", shape=(n5, )) pm.gemm(f4, w5, b5, f5, shape=f5.shape, alpha=1.0, beta=0.0, transA=False, transB=True) pm.elem_tanh(f5, a6, shape=a6.shape) m7 = pm.parameter(name="m7") n7 = pm.parameter(name="n7") f7 = pm.output(name="f7", shape=(n, n7)) w7 = pm.state(name="w7", shape=(m7, n7)) # w7 = pm.state(name="w7", shape=(n7, m7)) b7 = pm.state(name="b7", shape=(n7, )) pm.gemm(a6, w7, b7, f7, shape=f7.shape, alpha=1.0, beta=0.0, transA=False, transB=False) out = pm.output(name="sm") pm.softmax(f7, out, axis=1) if coarse: in_info, keys, out_info = np_lenetv2() return graph, in_info, out_info, keys else: shape_dict = { "n": 1, "ic": 1, "ih": 32, "iw": 32, "nf1": 6, "kh1": 5, "kw1": 5, "s1": 1, "p1": 0, "nf2": 16, "kh2": 5, "kw2": 5, "s2": 1, "p2": 0, "nf3": 120, "kh3": 5, "kw3": 5, "s3": 1, "p3": 0, "m5": 120, "n5": 84, "m7": 84, "n7": 10 } shape_val_pass = pm.NormalizeGraph(shape_dict, debug=debug) new_graph = shape_val_pass(graph) in_info, keys, out_info = np_lenetv2(lowered=True) return new_graph, in_info, out_info, keys